{ "updated": "2026-09-29", "scales": [ { "id": "pugenais-9", "name": "Problematic Use of Generative Artificial Intelligence Scale – 9 items", "acronym": "PUGenAIS-9", "summary": "A 9-item screener of problematic generative AI use modelled on the nine DSM-5 Internet Gaming Disorder criteria, validated in Chinese and US adults. Use it for cross-national prevalence studies or clinical-style screening of GenAI overuse.", "target": "Generative AI (GenAI) tools", "constructs": [ "dependence", "health" ], "populations": [ "general adults" ], "items": 9, "response": null, "subscales": [ { "name": "(unidimensional)", "items": 9, "description": "One item per IGD criterion (past year): preoccupation, tolerance, withdrawal, persistence, escape, problems, deception, displacement, conflict." } ], "psychometrics": { "structure": "31-item nine-dimension CFA (CFI .968, TLI .957, SRMR .027, RMSEA .080); 9-item short form CFA (CFI .948, TLI .931, RMSEA .045, SRMR .038); network analysis on the validation sample", "reliability": "α and ω computed (values not in the main text, likely in the supplement); CITC .67–.80", "validity": [ "Measurement invariance: configural and partial metric across nationality (4 parameters freed); full invariance up to latent means across gender", "Criterion: positive correlations with rumination, stress, loneliness and ADHD symptoms, negative with self-esteem; linked to use frequency and emotionally supportive use; not correlated with GenAI literacy", "Latent profile analysis: estimated 5–10% prevalence" ], "samples": [ { "n": 1508, "country": "China and United States", "population": "adults (Credamo and Prolific), exploratory phase (64 excluded)" }, { "n": 1426, "country": "China and United States", "population": "adults, validation phase (35 excluded)" } ] }, "status": "published", "citation": "Sun, H., Wu, D., Liu, W., Yao, M., & Yu, G. (2027). Emotionally vulnerable subtype of internet gaming disorder: Measuring and exploring the pathology of problematic generative AI use. Computers in Human Behavior, 186, 109116. https://doi.org/10.1016/j.chb.2026.109116", "authors": "Haocan Sun; Di Wu; Weizi Liu; Mike Yao; Guoming Yu", "year": 2027, "venue": "Computers in Human Behavior", "doi": "10.1016/j.chb.2026.109116", "url": "https://doi.org/10.1016/j.chb.2026.109116", "preprint_url": "https://arxiv.org/abs/2510.06908", "items_available": true, "language": "Chinese & English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "aias-10-1", "name": "AI Addiction Scale – Screening Scale for Artificial Intelligence Addiction Diagnosis", "acronym": "AIAS-10+1 v4.0", "summary": "A short Ukrainian screening tool for generative AI addiction risk. It has 10 scored items in two factors (socio-emotional and behavioral-cognitive dependence), a separate critical-thinking item, and cut-off scores. Use it for quick risk screening in student samples.", "target": "Generative AI (items refer to 'AI'; 85.5% of the sample used ChatGPT)", "constructs": [ "dependence", "health" ], "populations": [ "university students" ], "items": 11, "response": "5-point frequency scale (1 = never to 5 = always / very often)", "subscales": [ { "name": "Socio-Emotional Dependence", "items": 4, "description": "Preferring to confide in or talk to AI over people and turning to it under stress." }, { "name": "Behavioral-Cognitive Dependence", "items": 6, "description": "Accepting AI answers unchecked, anxiety or avoidance of tasks without AI, more time spent and hiding use." }, { "name": "Critical Thinking Indicator (+1)", "items": 1, "description": "A single reverse item (checking AI information against other sources) scored separately from the addiction index." } ], "psychometrics": { "structure": "Two factors for the 10 core items from EFA (KMO .790), revised from a hypothesized three-factor 11-item v3.0; no CFA", "reliability": "α .795 total (10 items); .866 socio-emotional; .696 behavioral-cognitive", "validity": [ "Nomological: loneliness and stress predicted AI addiction (bootstrap mediation analyses)", "Criterion: ROC against an external criterion (AUC = .733; Youden-optimal cut-off ≥18); Gaussian mixture modelling and K-means supported the cut-offs; 20.2% classified at risk" ], "samples": [ { "n": 242, "country": "Ukraine", "population": "university students who actively use generative AI" } ] }, "status": "published", "citation": "Chornomydz, A., Lukaniuk, M., & Oleshchuk, O. (2026). Development and psychometric validation of the screening scale for artificial intelligence addiction diagnosis (AIAS-10+1 v4.0). Psychological Journal, 12(3), 7–33. https://doi.org/10.31108/1.2026.12.3.1", "authors": "Andrii Chornomydz; Mariana Lukaniuk; Oleksandra Oleshchuk", "year": 2026, "venue": "Psychological Journal", "doi": "10.31108/1.2026.12.3.1", "url": "https://doi.org/10.31108/1.2026.12.3.1", "preprint_url": null, "items_available": false, "language": "Ukrainian", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ai-attachment-scale-kasturiratna", "name": "AI Attachment Scale", "acronym": "AIAS", "summary": "Measures how attached people feel to the generative AI systems they use (e.g., ChatGPT, Replika): emotional closeness, using AI in place of people, and treating AI with regard. Useful for studies of AI companionship, loneliness and social substitution.", "target": "Generative AI systems used most often (ChatGPT, Replika, DALL-E, DeepSeek and similar conversational systems)", "constructs": [ "relationships", "dependence", "anthropomorphism" ], "populations": [ "general adults", "university students" ], "items": 15, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Emotional Closeness", "items": 5, "description": "Feeling that exchanges with AI are meaningful, familiar and comforting, and unease if AI became unavailable" }, { "name": "Social Substitution", "items": 5, "description": "Turning to AI when there is no one to talk to or when feeling unsupported by people" }, { "name": "Normative Regard", "items": 5, "description": "Treating AI respectfully, politely and considerately" } ], "psychometrics": { "structure": "EFA in Singapore (n=339) and US (n=302) samples gave 15 items on 3 factors (58% and 70% variance). CFA in Study 4 (n=255): χ²(87)=187.77, CFI .96, TLI .95, RMSEA .06, SRMR .06, plus a higher-order model. Full scalar/residual invariance across sex; partial scalar and residual invariance across culture (Singapore vs US)", "reliability": "α .85–.94 for subscales, .95–.97 total; 1-week test–retest ICC .89 (r = .89; n = 234)", "validity": [ "Convergent: r = .77 with AI anthropomorphism and r = .72 with AI interaction positivity", "Discriminant: near-zero correlations (|r| ≤ .08) with Big Five, pet attachment and parasocial attachment; Fornell–Larcker criterion satisfied", "Measurement invariance across sex and culture (Singapore vs US)", "Nomological: correlates with loneliness, social anxiety, anxious attachment, need fulfilment, positive affect and life satisfaction" ], "samples": [ { "n": 62, "country": "Singapore and USA", "population": "pilot item evaluation (30 Singapore students, 32 US Prolific adults)" }, { "n": 339, "country": "Singapore", "population": "university students (EFA/invariance)" }, { "n": 302, "country": "USA", "population": "Prolific adults (EFA/invariance)" }, { "n": 255, "country": "Singapore", "population": "university students (CFA, validity, test–retest)" }, { "n": 301, "country": "USA", "population": "Prolific adults (correlates, invariance)" } ] }, "status": "published", "citation": "Kasturiratna, K. T. A. S., & Hartanto, A. (2026). Attachment to artificial intelligence: Development of the AI Attachment Scale, construct validation, and the psychological mechanisms of human–AI attachment. Computers in Human Behavior Reports, 21, 100912. https://doi.org/10.1016/j.chbr.2025.100912", "authors": "K. T. A. Sandeeshwara Kasturiratna; Andree Hartanto", "year": 2026, "venue": "Computers in Human Behavior Reports", "doi": "10.1016/j.chbr.2025.100912", "url": "https://doi.org/10.1016/j.chbr.2025.100912", "preprint_url": "https://doi.org/10.31234/osf.io/j4r5v_v2", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ai-attachment-scale-cheng-yu", "name": "AI Attachment Scale (Cheng & Yu)", "acronym": "AIAS", "summary": "Measures emotional bonds with AI chatbots such as ChatGPT: getting emotional support, distress when separated, and using the chatbot as a secure base. Useful in research on attachment to chatbots and on emotional design.", "target": "AI chatbots such as ChatGPT", "constructs": [ "relationships", "dependence" ], "populations": [ "general adults" ], "items": 15, "response": null, "subscales": [ { "name": "Emotional Support", "items": null, "description": "Turning to the chatbot for comfort and emotional support" }, { "name": "Separation Distress", "items": null, "description": "Distress when the chatbot is unavailable" }, { "name": "Secure Base", "items": null, "description": "Using the chatbot as a secure base for exploring and acting" } ], "psychometrics": { "structure": "EFA (N = 531) gave 15 items on 3 dimensions; CFA in a second sample (N = 375) supported the structure", "reliability": "Not reported in abstract", "validity": [ "Nomological/predictive: anthropomorphism was the strongest predictor; general attachment anxiety predicted AI attachment positively (β = .44) and avoidance negatively (β = −.53); AI attachment predicted behavioural intentions (β = .50)" ], "samples": [ { "n": 531, "country": "unknown", "population": "AI chatbot users (EFA)" }, { "n": 375, "country": "unknown", "population": "AI chatbot users (CFA)" } ] }, "status": "published", "citation": "Cheng, N., & Yu, R. (2026). Measuring and understanding emotional attachment in human–AI relationships. Ergonomics, 1–20. Advance online publication. https://doi.org/10.1080/00140139.2026.2622539", "authors": "Nuo Cheng; Ruifeng Yu", "year": 2026, "venue": "Ergonomics", "doi": "10.1080/00140139.2026.2622539", "url": "https://doi.org/10.1080/00140139.2026.2622539", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "EFA and CFA in separate samples plus nomological relations are reported, but reliability could not be confirmed.", "verified": "2026-09-29" }, { "id": "aied", "name": "AI Chatbot Emotional Dependence Scale", "acronym": "AIED", "summary": "A brief 5-item scale of adolescents' emotional dependence on AI chatbots: turning to them for comfort, relief and feeling understood. Use it alongside instrumental dependence measures in youth mental-health research.", "target": "LLM-based AI chatbots", "constructs": [ "dependence", "relationships", "health" ], "populations": [ "school students", "university students" ], "items": 5, "response": "7-point Likert (1 = strongly disagree to 7 = strongly agree)", "subscales": [ { "name": "(unidimensional)", "items": 5, "description": "Relying on an AI chatbot for comfort, emotional relief, perceived understanding and support when sad, stressed or lonely." } ], "psychometrics": { "structure": "One factor (6-item pool reduced to 5); EFA (n=2927; 69.07% variance, loadings .778–.864) and CFA (n=2928; CFI .98, TLI .95, SRMR .02)", "reliability": "α .89; CR .90", "validity": [ "Convergent: AVE .65; loadings ≥ .73", "Correlation r = .67 with Zhang et al.'s (2025) AI chatbot dependence scale", "Measurement invariance: configural, metric and scalar across sex and educational stage" ], "samples": [ { "n": 5855, "country": "China", "population": "adolescents from 7 middle and high schools in 5 provinces (Anhui, Hebei, Hubei, Guizhou, Shanxi); randomly split for EFA/CFA" } ] }, "status": "preprint", "citation": "Wei, X., Liu, D., & Cheung, H. N. (2026). Emotional dependence on AI chatbots: Development and validation of the AIED Scale [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/yhetz_v1", "authors": "Xiyu Wei; Dongyu Liu; Ho Nam Cheung", "year": 2026, "venue": "PsyArXiv", "doi": "10.31234/osf.io/yhetz_v1", "url": "https://doi.org/10.31234/osf.io/yhetz_v1", "preprint_url": "https://doi.org/10.31234/osf.io/yhetz_v1", "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ai-chatbots-acceptance-perception-scale", "name": "AI Chatbots Acceptance and Perception Scale (AI Chatbots Usage Scale)", "acronym": null, "summary": "A multidimensional scale of higher-education students' acceptance of AI chatbots: ease of use, perceived usefulness, trust, and accessibility. Use it in education research on chatbot adoption; the Trust subscale covers confidence in the accuracy and integrity of chatbot responses.", "target": "AI chatbots (platform-agnostic, e.g., ChatGPT-type tools)", "constructs": [ "acceptance-use", "trust" ], "populations": [ "university students" ], "items": null, "response": "5-point frequency scale (always to never)", "subscales": [ { "name": "Ease of Use", "items": 8, "description": "How easy AI chatbots are to use." }, { "name": "Perceived Usefulness", "items": 4, "description": "How useful chatbots are for learning." }, { "name": "Trust", "items": 4, "description": "Confidence in the accuracy, reliability, and integrity of AI chatbot responses." }, { "name": "Accessibility", "items": 3, "description": "Availability and access to AI chatbots." } ], "psychometrics": { "structure": "EFA (n=374) gave 4 factors, 42.48% variance; CFA (n=599) χ²/df=1.70, CFI=.979, TLI=.976, RMSEA=.034, GFI=.957", "reliability": "Total α/ω .927; Ease of Use .893, Usefulness .799, Trust .804, Accessibility .731 (ω .734)", "validity": [ "Convergent: AVE = .717, CR = .909", "Discriminant: HTMT < .85 for all factor pairs; √AVE > inter-factor correlations (Fornell–Larcker)" ], "samples": [ { "n": 374, "country": "Egypt", "population": "Faculty of Education students (EFA)" }, { "n": 599, "country": "Egypt", "population": "Faculty of Education students (CFA)" } ] }, "status": "published", "citation": "Ibrahim, A. R., & Nemt-allah, M. A. (2026). Development and psychometric validation of the AI chatbots acceptance and perception scale for higher education students. Scientific Reports, 16, 22941. https://doi.org/10.1038/s41598-026-62798-4", "authors": "Ashraf Ragab Ibrahim, Mohamed Ali Nemt-allah", "year": 2026, "venue": "Scientific Reports", "doi": "10.1038/s41598-026-62798-4", "url": "https://www.nature.com/articles/s41598-026-62798-4", "preprint_url": null, "items_available": false, "language": "Arabic", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "aidep-22", "name": "AI Dependence Scale for Chinese Undergraduates", "acronym": "AIDep-22", "summary": "A 22-item measure of university students' overreliance on (generative) AI for studying, covering emotional, functional and cognitive dependence and loss of control. Use it in higher-education research on academic AI overreliance.", "target": "AI tools used for study, framed around generative AI such as ChatGPT (items say 'AI'; one refers to chat prompts)", "constructs": [ "dependence", "reliance", "learning" ], "populations": [ "university students" ], "items": 22, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree); mean-score bands: 1.00–2.49 low, 2.50–3.49 moderate, ≥3.50 high", "subscales": [ { "name": "Emotional dependence", "items": 5, "description": "Unease, anxiety or tension without AI help, and feeling calm or supported when using it." }, { "name": "Functional dependence", "items": 6, "description": "Delegating academic tasks to AI and feeling unable to manage them without it." }, { "name": "Cognitive dependence", "items": 5, "description": "Asking AI before thinking and letting it make judgments; perceived decline in independent thinking." }, { "name": "Loss of control", "items": 6, "description": "Failed attempts to cut down, difficulty stopping, and urges to use AI." } ], "psychometrics": { "structure": "Four factors; EFA (sample 1) and CFA (sample 2: χ²(203)=228.89, CFI .994, TLI .993, RMSEA .018, SRMR .052); competing models with fewer factors fit worse", "reliability": "α .87 total; subscales α .86–.89; CR .87–.89", "validity": [ "Content validity by expert I-CVI review and cognitive interviews", "Convergent: AVE .53–.58", "Discriminant: Fornell-Larcker criterion met (inter-factor r .21–.26)", "Criterion: r = −.46 with academic self-efficacy", "Behavioral indicators: r = .48 with variety of AI use, .37 with frequency, .33 with extent", "Group differences by gender, year, major and use frequency" ], "samples": [ { "n": 400, "country": "China", "population": "undergraduates at one university in Southwest China (EFA)" }, { "n": 400, "country": "China", "population": "undergraduates at one university in Southwest China (CFA)" }, { "n": 30, "country": "China", "population": "pilot" } ] }, "status": "published", "citation": "Wu, H., Ni, H., Luo, W., & Wu, T. (2026). Development and validation of the AI dependence scale for Chinese undergraduates and a preliminary exploration. Frontiers in Psychology, 16, 1725393. https://doi.org/10.3389/fpsyg.2025.1725393", "authors": "Houyu Wu; Haiyang Ni; Wenfu Luo; Tenglong Wu", "year": 2026, "venue": "Frontiers in Psychology", "doi": "10.3389/fpsyg.2025.1725393", "url": "https://doi.org/10.3389/fpsyg.2025.1725393", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ailis", "name": "AI Information Literacy Scale", "acronym": "AILIS", "summary": "Measures how adults find, create and process, and critically assess information using generative AI tools, plus the ethics of doing so, grounded in information-behaviour theory. Use it in information-literacy, library or organisational research.", "target": "Generative AI tools as information sources", "constructs": [ "literacy", "credibility" ], "populations": [ "general adults" ], "items": 39, "response": null, "subscales": [ { "name": "Creation and processing", "items": null, "description": "Creating and processing information with GenAI" }, { "name": "Assessment and critique", "items": null, "description": "Evaluating and critiquing GenAI-provided information" }, { "name": "Seeking and retrieval", "items": null, "description": "Seeking and retrieving information with GenAI" }, { "name": "Ethics", "items": 2, "description": "Ethical information practices with GenAI" } ], "psychometrics": { "structure": "Four components from principal components analysis (39 items); no CFA mentioned in the abstract", "reliability": "α .92–.96 for the three broad factors; α .73 for the 2-item ethics factor", "validity": [ "Expert review (43 → 40 items)", "Group differences: higher scores among younger, more educated and more frequent AI users" ], "samples": [ { "n": 758, "country": "Israel", "population": "adults" } ] }, "status": "published", "citation": "Alon, L., & Levkovich, I. (2026). Information literacy in the age of generative tools: Development and validation of the AI Information Literacy Scale (AILIS). Computers in Human Behavior: Artificial Humans, 7, Article 100254. https://doi.org/10.1016/j.chbah.2026.100254", "authors": "Lilach Alon; Inbar Levkovich", "year": 2026, "venue": "Computers in Human Behavior: Artificial Humans", "doi": "10.1016/j.chbah.2026.100254", "url": "https://www.sciencedirect.com/science/article/pii/S2949882126000058", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "evidence": "partial", "flag_reason": "Structure comes from principal components analysis only (no CFA), and validity rests on group differences.", "verified": "2026-09-29" }, { "id": "ai-learning-strategies-scale", "name": "AI Learning Strategies Scale", "acronym": null, "summary": "A 12-item scale of how often learners use AI tools, mainly generative AI like ChatGPT, strategically to plan, carry out and reflect on their learning (self-regulated learning with AI). Use it to study individual differences in strategic AI use for studying.", "target": "AI tools used for learning (generative AI such as ChatGPT, Gemini, Claude, plus other AI learning apps)", "constructs": [ "learning", "self-efficacy" ], "populations": [ "university students", "general adults" ], "items": 12, "response": "5-point frequency scale (1 = never to 5 = always)", "subscales": [ { "name": "Forethought", "items": 4, "description": "Using AI to set goals and plan learning before starting" }, { "name": "Performance", "items": 4, "description": "Using AI to organise, process and monitor learning during the task" }, { "name": "Self-Reflection", "items": 4, "description": "Using AI to evaluate and reflect on one's learning afterwards" } ], "psychometrics": { "structure": "Study 2 EFA (n=320): 3 factors; Studies 3–4 CFA (n=285, n=270) confirmed the 3-factor model", "reliability": "α .94–.95 total (subscales .88–.92); 1-week test-retest ICC(2,1) .80 total, .66–.76 subscales", "validity": [ "Measurement invariance across sex (multi-group CFA)", "Convergent and discriminant validity (AVE/Fornell–Larcker)", "Nomological network: self-efficacy, autonomous motivation, mastery goals, Big Five, AI attitudes, AI use, AI fatigue", "Criterion validity: predicts richer self-regulated-learning content in a writing task" ], "samples": [ { "n": 320, "country": "USA", "population": "Prolific adults (EFA)" }, { "n": 285, "country": "USA", "population": "Prolific students (CFA)" }, { "n": 270, "country": "USA", "population": "Prolific students (CFA, invariance, nomological)" }, { "n": 126, "country": "Singapore", "population": "university subject-pool students (test-retest, criterion)" } ] }, "status": "preprint", "citation": "Lau, G. R., Liow, C. J. M., Gasevic, D., & Hartanto, A. (2026). AI Learning Strategies Scale: Development and validation for individual differences in self-regulated learning with AI [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/jqxh9_v1", "authors": "Gabriel Rongyang Lau; Caresse Jia Min Liow; Dragan Gasevic; Andree Hartanto", "year": 2026, "venue": null, "doi": "10.31234/osf.io/jqxh9_v1", "url": "https://doi.org/10.31234/osf.io/jqxh9_v1", "preprint_url": "https://doi.org/10.31234/osf.io/jqxh9_v1", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ai-giarism-scale-turkish", "name": "AI-giarism Scale – Turkish version", "acronym": null, "summary": "A scenario-based scale that asks university students how far various uses of generative AI in assignments count as academic misconduct. The uses range from copying AI output to AI-augmented writing to little or no AI reliance. Use it to study students' views on the ethics of AI-assisted plagiarism in Turkish-speaking samples.", "target": "generative AI use in academic assignments (AI-assisted plagiarism)", "constructs": [ "academic-integrity", "ethics-concerns" ], "populations": [ "graduate students", "university students" ], "items": 11, "response": "5-point agreement (1 = strongly disagree to 5 = strongly agree) that each described action constitutes academic misconduct", "subscales": [ { "name": "Plagiaristic AI use", "items": 2, "description": "Scenarios where AI output is copied or used without proper attribution" }, { "name": "AI-augmented writing", "items": 6, "description": "Scenarios where GenAI output is verified, edited and combined with the student's own work, with AI use disclosed" }, { "name": "No AI reliance", "items": 3, "description": "Scenarios with only minimal AI use (grammar check, source search) or none" } ], "psychometrics": { "structure": "EFA (PAF, oblimin): 3 factors, 59.7% of variance. CFA (N=426): χ²(41)=193.98, CFI .937, TLI .916, SRMR .052, RMSEA .094. The paper's EFA table and its factor labels do not line up cleanly; subscale item counts follow the CFA table.", "reliability": "α .63 (Plagiaristic), .91 (Augmented), .77 (No reliance); CR .63–.91", "validity": [ "Convergent validity: AVE .56–.64", "Factor intercorrelations reported (.04–.55)", "No gender differences (Mann–Whitney U)" ], "samples": [ { "n": 426, "country": "Türkiye", "population": "university students from 19 universities (84.5% undergraduate)" } ] }, "status": "published", "citation": "Gökçearslan, Ş., Mumcu, F., Chiu, T., & Lavicza, Z. (2026). Measuring AI-giarism: A validation study and dimensional exploration of the scale in Turkish. Journal of Learning and Teaching in Digital Age, 11(1), 111–118. https://doi.org/10.53850/joltida.1762390", "authors": "Şahin Gökçearslan; Filiz Mumcu; Thomas Chiu; Zsolt Lavicza", "year": 2026, "venue": "Journal of Learning and Teaching in Digital Age", "doi": "10.53850/joltida.1762390", "url": "https://doi.org/10.53850/joltida.1762390", "preprint_url": null, "items_available": true, "language": "Turkish", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "atrai-llm", "name": "ATRAI-LLM questionnaire (Attitudes Toward LLMs in Clinical Setting)", "acronym": "ATRAI-LLM", "summary": "Measures physicians' attitudes toward LLM-based chatbots used as a clinical reference tool, covering willingness to use, implementation outlook, and hopes and fears. Use it to survey clinicians before or during LLM roll-outs.", "target": "LLM-based chatbots answering clinical queries (e.g., YandexGPT, GigaChat in the Moscow health system)", "constructs": [ "attitudes", "acceptance-use", "health" ], "populations": [ "health professionals" ], "items": 19, "response": "Mixed: 5-point Likert (1 = extremely negative to 5 = extremely positive), multiple-choice/multiple-response and 5-point items. 8 background items and 11 main items, of which 9 are scored (total 0–36).", "subscales": [ { "name": "Willingness to Use", "items": 4, "description": "Views that encourage or discourage using an LLM assistant: wide implementation, trust in EHR-retrieved information, who should pay, workload change (score 0–16)" }, { "name": "Implementation Perspective", "items": 3, "description": "Most trustworthy topics, most-used functions and expected changes in practice (score 0–12)" }, { "name": "Hopes and Fears", "items": 2, "description": "Expected LLM-related changes in professional status and salary (score 0–8)" } ], "psychometrics": { "structure": "Domains identified by hierarchical clustering of item correlations. CFA supported three factors (RMSEA .05, CFI .97, TLI .96, SRMR .03); one-factor model fit poorly.", "reliability": "α .770 (95% CI .731–.800); ωt .830; no test–retest (the authors argue it is inappropriate for rapidly changing LLMs)", "validity": [ "Criterion: Spearman ρ = .68 between total score and a visual analogue scale of attitude toward LLMs", "Face and content validity via focus group and expert review" ], "samples": [ { "n": 562, "country": "Russia (Moscow)", "population": "Physicians from medical organizations of the Moscow Department of Health (71.7% had prior LLM-chatbot use)" } ] }, "status": "published", "citation": "Reshetnikov, R. V., Vasilev, Y. A., Shumskaya, Y. F., Akhmedzyanova, D. A., Alymova, Y. A., Vladzymyrskyy, A. V., Tyrov, I. A., Omelyanskaya, O. V., & Blokhin, I. A. (2026). Development and validation of the ATRAI questionnaire to assess attitudes toward large language models in clinical setting (ATRAI-LLM). European Journal of Investigation in Health, Psychology and Education, 16(7), 94. https://doi.org/10.3390/ejihpe16070094", "authors": "Roman V. Reshetnikov; Yuriy A. Vasilev; Yuliya F. Shumskaya; Dina A. Akhmedzyanova; Yulya A. Alymova; Anton V. Vladzymyrskyy; Ilya A. Tyrov; Olga V. Omelyanskaya; Ivan A. Blokhin", "year": 2026, "venue": "European Journal of Investigation in Health, Psychology and Education", "doi": "10.3390/ejihpe16070094", "url": "https://doi.org/10.3390/ejihpe16070094", "preprint_url": null, "items_available": true, "language": "Russian", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "academic-integrity-genai-short-questionnaire", "name": "Academic Integrity and Generative AI Short Questionnaire", "acronym": null, "summary": "A 15-item questionnaire on how university students approach academic integrity when using generative AI. It covers normative, ethical use (including awareness of misuse risks) and legitimate use of GenAI to support academic writing. Use it for quick surveys of GenAI-related integrity attitudes in higher education.", "target": "Generative AI in higher-education writing and assessment", "constructs": [ "academic-integrity", "ethics-concerns" ], "populations": [ "university students" ], "items": 15, "response": null, "subscales": [ { "name": "Normative integrity in AI use", "items": null, "description": "Ethical use of GenAI and awareness of the risks of misuse (merges two original dimensions)" }, { "name": "Support for academic writing", "items": null, "description": "Using GenAI legitimately to support academic writing" } ], "psychometrics": { "structure": "EFA and CFA with robust DWLS for ordinal items. The original 17 items in 3 dimensions (ethical use of AI, awareness of misuse risks, support for academic writing) were refined to 15 items in 2 correlated factors with adequate fit.", "reliability": "'High internal consistency' per abstract; coefficients not seen", "validity": [ "Convergent validity supported", "Discriminant validity acceptable but borderline", "Preliminary measurement invariance across gender (configural model absolute fit not optimal)" ], "samples": [ { "n": 419, "country": "Peru (inferred from author affiliations; not stated in abstract)", "population": "Higher education students" } ] }, "status": "published", "citation": "Garro-Aburto, L. L., Chávez-Díaz, J. M., Hinojosa Salazar, C. A., Llerena Espinoza, E. M., & Sánchez Sandoval, S. P. (2026). Academic integrity and generative artificial intelligence in higher education: Psychometric validation of a short questionnaire. Education Sciences, 16(7), 1125. https://doi.org/10.3390/educsci16071125", "authors": "Luzmila Lourdes Garro-Aburto; Jorge Miguel Chávez-Díaz; Carlos Alberto Hinojosa Salazar; Edith María Llerena Espinoza; Sara Pamela Sánchez Sandoval", "year": 2026, "venue": "Education Sciences", "doi": "10.3390/educsci16071125", "url": "https://doi.org/10.3390/educsci16071125", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "llm-acceptability-project-teams", "name": "Acceptability Scale for the Use of LLMs by Project Teams", "acronym": null, "summary": "A 13-item scale of project professionals' before-use (a priori) acceptability of LLMs, measured through beliefs about what LLMs and prompt engineering can do for project work. Use it for organisational or project-management diagnostics of LLM adoption.", "target": "Large language models (and prompt engineering) in project management", "constructs": [ "acceptance-use", "trust", "workplace" ], "populations": [ "employees & professionals" ], "items": 13, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Intention/Predisposition", "items": 8, "description": "Beliefs that AI/LLM tools improve decisions, forecasting, resource allocation and stakeholder management in projects" }, { "name": "Trust/Perceived Benefit", "items": 5, "description": "Beliefs about how prompt quality and engineering shape LLM output quality and domain adaptation" } ], "psychometrics": { "structure": "Polychoric EFA and CFA on the same sample; 17 items reduced to 13 on two correlated factors (r = .50). CFA (17-item model): CFI .979, TLI .976, RMSEA .060", "reliability": "Final 13 items: ω .904 / .840; CR .904 / .840 (ORION > .85)", "validity": [ "Content validation (2 AI experts, cognitive pretest with 3 project managers)", "Convergent validity: AVE .543 / .518 (13-item recalculation)", "Discriminant validity: Fornell–Larcker met; HTMT .587", "Nomological: Spearman ρ = .197 (p = .015) Trust/Perceived Benefit and .153 (p = .058) Intention/Predisposition with LLM usage frequency" ], "samples": [ { "n": 154, "country": "Brazil (predominantly)", "population": "Project management professionals (snowball sample)" } ] }, "status": "published", "citation": "Carvalho, M. Z. de, Penha, R., Vils, L., Bizarrias, F. S., & Serra, F. A. R. (2026). Acceptability scale for the use of large language models (LLMs) by project teams: Development and preliminary validation. Systems, 14(4), 366. https://doi.org/10.3390/systems14040366", "authors": "Carvalho, M. Z. de, Penha, R., Vils, L., Bizarrias, F. S., & Serra, F. A. R.", "year": 2026, "venue": "Systems", "doi": "10.3390/systems14040366", "url": "https://doi.org/10.3390/systems14040366", "preprint_url": null, "items_available": true, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ae-ai-agentic-engagement", "name": "Agentic Engagement with AI Scale", "acronym": "AE-AI", "summary": "A 16-item scale of how actively students steer their learning with generative AI: directing the AI, critically integrating its output, checking other sources and adjusting through reflection. Use it to study student agency in AI-assisted learning.", "target": "generative AI in learning", "constructs": [ "learning", "other" ], "populations": [ "university students" ], "items": 16, "response": null, "subscales": [ { "name": "Adaptive Direction", "items": null, "description": "Steering AI interactions toward one's learning goals" }, { "name": "Critical Integration", "items": null, "description": "Critically evaluating and integrating AI output" }, { "name": "Cross-Source Inquiry", "items": null, "description": "Checking AI output against other sources" }, { "name": "Reflective Calibration", "items": null, "description": "Adjusting one's AI use through reflection" } ], "psychometrics": { "structure": "28 items from interviews (n=26); expert review; EFA (n=340); CFA (n=256); final 4 factors, 16 items", "reliability": "Not reported in abstract (unverified)", "validity": [ "Criterion validity checking", "Expert review (content)" ], "samples": [ { "n": 340, "country": "not reported", "population": "students (EFA)" }, { "n": 256, "country": "not reported", "population": "students (CFA)" } ] }, "status": "published", "citation": "Dai, Y., Liu, S., Zhou, S., Lai, S., Liu, A., & Lim, C. P. (2026). Redefining and measuring student agency in AI-assisted learning: Development and validation of the agentic engagement with AI (AE-AI) scale. Computers & Education, 253, 105687. https://doi.org/10.1016/j.compedu.2026.105687", "authors": "Yun Dai; Suya Liu; Sihan Zhou; Sichen Lai; Ang Liu; Cher Ping Lim", "year": 2026, "venue": "Computers & Education", "doi": "10.1016/j.compedu.2026.105687", "url": "https://doi.org/10.1016/j.compedu.2026.105687", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "llm-apprehension-scale", "name": "Apprehension Towards Large Language Models Scale (one of the Four AI Apprehension Scales)", "acronym": null, "summary": "An 8-item scale of how much more reassurance people would need before feeling comfortable with LLM products. It covers the perceived morality of LLM makers, the social awkwardness of using LLMs in public, and their predictability. Parallel versions exist for general, personal and institutional AI, so you can compare apprehension across AI types.", "target": "Large language models (LLM products)", "constructs": [ "anxiety", "ethics-concerns", "trust" ], "populations": [ "general adults" ], "items": 8, "response": "11-point scale (0 = 'Nothing more needed' to 10 = 'A great deal more needed'); common stem 'I think further conditions are necessary for me…'", "subscales": [ { "name": "Implied Malice", "items": 2, "description": "Needing more before feeling that LLM makers take morality seriously and that the products are designed and run ethically" }, { "name": "Undesirability", "items": 3, "description": "Needing more before feeling normal and at ease interacting with LLM products in public" }, { "name": "Unpredictability", "items": 3, "description": "Needing more before feeling that LLM products work as described and have clear purposes and goals" } ], "psychometrics": { "structure": "CFA of the 3-factor model for the LLM version: CFI .99, TLI .98, SRMR .04, RMSEA .085 (90% CI .067–.103). The 3-factor model fit better than 1- and 2-factor alternatives. Standardized LLM loadings .78–.97. Network analysis also reported.", "reliability": "Reported across the four parallel scales: Implied Malice Spearman-Brown/α .86–.94; Undesirability and Unpredictability α .89–.97; CR .90–.97; AVE > .73; H .56–.81", "validity": [ "Convergent: ATAI Acceptance (negative) and ATAI Fear (positive) predict LLM apprehension (robust regression; R² .201, rising to .219 with openness)", "Discriminant: Fornell-Larcker criterion met; inter-factor r < .82; 3-factor model beats 1- and 2-factor models", "LLM apprehension shares 67.9% of variance with general-AI apprehension, showing partial distinctiveness" ], "samples": [ { "n": 559, "country": "United Kingdom", "population": "Adults aged 18-45 (M = 30.64; 50.3% male)" } ] }, "status": "preprint", "citation": "Yankouskaya, A., Almourad, M. B., Liebherr, M., Rahman, M. M., AlShakhsi, S., & Ali, R. (2026). The four artificial intelligence apprehension scales: Apprehension towards personal AI, general AI, institutional AI, and large language models [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-9018418/v1", "authors": "Ala Yankouskaya; Mohamed Basel Almourad; Magnus Liebherr; Mohammad Mominur Rahman; Sameha AlShakhsi; Raian Ali", "year": 2026, "venue": "Research Square (preprint)", "doi": "10.21203/rs.3.rs-9018418/v1", "url": "https://doi.org/10.21203/rs.3.rs-9018418/v1", "preprint_url": "https://www.researchsquare.com/article/rs-9018418/v1", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "aiums", "name": "Artificial Intelligence Use Motivations Scale", "acronym": "AIUMS", "summary": "A 22-item measure of why people use generative AI such as ChatGPT. It covers 11 motives, including usefulness, fun, companionship, escaping social anxiety, seeing AI as human-like, and prestige. Use it to study which motives drive healthy versus problematic GenAI use.", "target": "Generative AI systems (e.g., ChatGPT)", "constructs": [ "acceptance-use", "dependence", "anthropomorphism" ], "populations": [ "general adults" ], "items": 22, "response": "5-point Likert (strongly disagree to strongly agree)", "subscales": [ { "name": "Perceived usefulness", "items": 2, "description": "Using AI because it improves task performance" }, { "name": "Self-efficacy", "items": 2, "description": "Confidence in completing tasks with AI" }, { "name": "Availability", "items": 2, "description": "AI is easy for anyone to use without special skills" }, { "name": "Perceived trust", "items": 2, "description": "Believing AI output is correct and reliable" }, { "name": "Perceived safety", "items": 2, "description": "Not worrying about data exposure when using AI" }, { "name": "Seeking pleasure and fun", "items": 2, "description": "Using AI because it is enjoyable" }, { "name": "Social interaction", "items": 2, "description": "AI as companionship, like talking to a friend" }, { "name": "Escaping social anxiety and judgment", "items": 2, "description": "Preferring AI to avoid anxiety or judgment from people" }, { "name": "Anthropomorphism and empathy", "items": 2, "description": "Seeing AI as human-like, with intentions or awareness" }, { "name": "Tech-based social prestige", "items": 2, "description": "Using AI to look knowledgeable and up to date" }, { "name": "Personal innovativeness", "items": 2, "description": "Valuing AI's creative, novel suggestions" } ], "psychometrics": { "structure": "EFA (n = 648) gave 6 factors. A theory-based 11-factor CFA fit well: χ²/df 1.53, RMSEA .028, CFI .986, TLI .979.", "reliability": "Subscale α .71–.85", "validity": [ "Full measurement invariance across gender (multi-group CFA)", "Convergent/discriminant: compensatory-affective motives were related to problematic ChatGPT use, social anxiety and digital addiction; competence-adaptive motives were related to self-esteem and social support", "Incremental: motives explained an additional 15% of variance in problematic ChatGPT use" ], "samples": [ { "n": 1296, "country": "Iran", "population": "adults (online survey; 61.3% female; mean age 28.7)" } ] }, "status": "published", "citation": "Akbari, M., Mollaali Farkhani, S., Golmohammadi, M., Mirjalili, M., Yasaghi, F., Hatampour, R., & Griffiths, M. D. (2026). The Artificial Intelligence Use Motivations Scale (AIUMS): Development, validation, and their role in problematic generative AI use (PGAU). International Journal of Mental Health and Addiction. Advance online publication. https://doi.org/10.1007/s11469-026-01671-x", "authors": "Mehdi Akbari; Shirin Mollaali Farkhani; Mahsa Golmohammadi; Mitra Mirjalili; Fatemeh Yasaghi; Reza Hatampour; Mark D. Griffiths", "year": 2026, "venue": "International Journal of Mental Health and Addiction", "doi": "10.1007/s11469-026-01671-x", "url": "https://link.springer.com/article/10.1007/s11469-026-01671-x", "preprint_url": null, "items_available": true, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "baas", "name": "Behavioral AI Anthropomorphism Scale", "acronym": "BAAS", "summary": "Measures anthropomorphism as behaviour: how much users act socially toward AI chatbots (reciprocating, aligning socially and emotionally), rather than only whether they think the chatbot seems human. Useful for studies of trust in chatbots and of gender effects in human–AI interaction.", "target": "AI chatbots / conversational AI", "constructs": [ "anthropomorphism", "trust" ], "populations": [ "general adults" ], "items": null, "response": null, "subscales": [ { "name": "Enacted reciprocity", "items": null, "description": "Behaving reciprocally toward the chatbot during dialogue (phrase from the abstract; exact factor label unconfirmed)" }, { "name": "Socioemotional alignment", "items": null, "description": "Aligning socially and emotionally with the chatbot (phrase from the abstract; exact factor label unconfirmed)" } ], "psychometrics": { "structure": "Random split: EFA then CFA supported a two-factor structure and a hierarchical higher-order model (CFI .966, TLI .949, RMSEA .079, SRMR .044)", "reliability": "α .83–.91; ω .85–.92", "validity": [ "Discriminant: HTMT .85 relative to psychological anthropomorphism", "Incremental: explains variance in trust beyond self-reported psychological anthropomorphism (ΔR² .06–.07)", "Nomological: mediates associations of AI use frequency and gender attribution with trust; gender-similarity effects" ], "samples": [ { "n": 933, "country": "not stated in abstract (English-language sample)", "population": "adults" } ] }, "status": "published", "citation": "Ibrahim, F., Telle, N.-T., Lauckner, M., Karl, J. A., & Daseking, M. (2026). Gender similarity effects in human–AI interaction: Development and validation of the Behavioral AI Anthropomorphism Scale (BAAS) and implications for trust and gender attribution. Computers in Human Behavior Reports, 22, 101003. https://doi.org/10.1016/j.chbr.2026.101003", "authors": "Fabio Ibrahim; Nils-Torge Telle; Mathis Lauckner; Johannes Alfons Karl; Monika Daseking", "year": 2026, "venue": "Computers in Human Behavior Reports", "doi": "10.1016/j.chbr.2026.101003", "url": "https://doi.org/10.1016/j.chbr.2026.101003", "preprint_url": "https://doi.org/10.2139/ssrn.5954583", "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "aus-gpt", "name": "ChatGPT Acceptance and Use Scale (Mexican validation)", "acronym": "AUS-GPT", "summary": "A multidimensional scale, in the technology-acceptance tradition, of how students accept and use ChatGPT. It was translated, adapted and validated with Mexican university students in 2023 and 2025 cohorts, and tested for equivalence across gender and cohort. Use it to compare ChatGPT adoption across groups or over time in Spanish-speaking samples.", "target": "ChatGPT", "constructs": [ "acceptance-use" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Multiple acceptance-and-use dimensions (names and counts not seen)", "items": null, "description": "Technology-acceptance dimensions for ChatGPT; details not reported in the abstract" } ], "psychometrics": { "structure": "CFA with satisfactory model fit (indices not seen)", "reliability": "Internal consistency confirmed (values not seen)", "validity": [ "Content validity: near-perfect inter-rater agreement", "Convergent validity mixed", "Measurement invariance across cohorts (2023 vs 2025) and gender", "Known-groups/comparative: MANOVA Year × Gender interaction" ], "samples": [ { "n": 770, "country": "Mexico", "population": "higher education students (2023 and 2025 cohorts)" } ] }, "status": "published", "citation": "Ortega-Sánchez, D., & Pérez-González, C. (2026). Psychometric validation of the 'ChatGPT acceptance and use scale' (AUS-GPT), and comparative analysis of its adoption among Mexican higher education students (2023-2025). Social Sciences & Humanities Open, 13, 102717. https://doi.org/10.1016/j.ssaho.2026.102717", "authors": "Delfín Ortega-Sánchez; Carlos Pérez-González", "year": 2026, "venue": "Social Sciences & Humanities Open", "doi": "10.1016/j.ssaho.2026.102717", "url": "https://doi.org/10.1016/j.ssaho.2026.102717", "preprint_url": null, "items_available": false, "language": "Spanish", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "chatgpt-attitude-scale-nursing-education", "name": "ChatGPT Attitude Scale for Nursing Education", "acronym": null, "summary": "A 16-item scale of nursing students' attitudes toward using ChatGPT in their education, covering support for learning and guidance for clinical skills. Use it in nursing or health-professions education research on ChatGPT.", "target": "ChatGPT in nursing education", "constructs": [ "attitudes", "learning", "health" ], "populations": [ "university students" ], "items": 16, "response": null, "subscales": [ { "name": "Learning Process Support", "items": null, "description": "ChatGPT as support for the learning process" }, { "name": "Clinical Skill Guidance", "items": null, "description": "ChatGPT as guidance for clinical skill development" } ], "psychometrics": { "structure": "EFA (n=246): 2 factors, 16 items, 80.66% variance; CFA (n=310): good fit (indices not in abstract)", "reliability": "α .938 total; .993 and .921 subscales", "validity": [ "Content validity (CVI .92)", "Factorial validity via CFA" ], "samples": [ { "n": 246, "country": "Türkiye", "population": "nursing students, state university (EFA)" }, { "n": 310, "country": "Türkiye", "population": "nursing students, state university (CFA)" } ] }, "status": "published", "citation": "Berşe, S., Ağar, A., Akça, K., & Dirgar, E. (2026). Development of the ChatGPT Attitude Scale for Nursing Education: A methodological study. Nursing & Health Sciences, 28(2), e70358. https://doi.org/10.1111/nhs.70358", "authors": "Soner Berşe; Ali Ağar; Kamile Akça; Ezgi Dirgar", "year": 2026, "venue": "Nursing & Health Sciences", "doi": "10.1111/nhs.70358", "url": "https://doi.org/10.1111/nhs.70358", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "Only content validity and CFA fit are reported; no convergent, discriminant or criterion evidence. A subscale alpha of .99 is unusually high.", "verified": "2026-09-29" }, { "id": "chatgpt-reliance-scale-pakistan", "name": "ChatGPT Reliance Scale", "acronym": null, "summary": "A 24-item scale of how much university students rely on ChatGPT for their studies. It covers three things: leaning on ChatGPT instead of their own thinking, using it to get new ideas, and using it to put off work. Use it to study over-reliance on ChatGPT and procrastination in higher education.", "target": "ChatGPT", "constructs": [ "reliance", "dependence", "creativity" ], "populations": [ "graduate students", "university students" ], "items": 24, "response": "5-point Likert (strongly disagree to strongly agree)", "subscales": [ { "name": "Reliance", "items": 8, "description": "Using ChatGPT for tasks one could do alone and preferring its answers to one's own thinking" }, { "name": "Novelty", "items": 8, "description": "ChatGPT as a source of new viewpoints, ideas and creative content" }, { "name": "Procrastination", "items": 8, "description": "Using ChatGPT in ways that delay or distract from actual academic work" } ], "psychometrics": { "structure": "EFA (n = 409; PCA, varimax): 3 factors, 61.99% of variance, 24 items retained from 45. CFA (n = 400): χ²(235) = 1074, χ²/df 4.57, CFI .91, GFI .90, TLI .91, RMSEA .06; loadings .54–.85.", "reliability": "Total α .92, ω .93; subscales α .84/.86/.90; CR .83/.84/.90 (AVE .39/.41/.62)", "validity": [ "Convergent: ChatGPT Usage r = .85; positive attitudes toward AI (GAAIS) r = .48; Novelty with creativity r = .59; Procrastination with a student procrastination scale r = .48", "Discriminant: negative attitudes toward AI r = −.82; Digital Well-Being r = −.85", "Gender differences (boys higher, d = .47)" ], "samples": [ { "n": 809, "country": "Pakistan", "population": "university and postgraduate college students, Abbottabad and Peshawar (split 409 EFA / 400 CFA)" }, { "n": 220, "country": "Pakistan", "population": "students (convergent/discriminant validity subsample)" } ] }, "status": "published", "citation": "Kazmi, S. F., Ahmad, O., & Siddique, S. (2026). Development and validation of ChatGPT Reliance Scale. Pakistan Journal of Professional Psychology: Research and Practice, 16(2), 21–37. https://doi.org/10.62663/pjpprp.v16i2.256", "authors": "Syeda Farhana Kazmi; Owais Ahmad; Shamsa Siddique", "year": 2026, "venue": "Pakistan Journal of Professional Psychology: Research and Practice", "doi": "10.62663/pjpprp.v16i2.256", "url": "https://doi.org/10.62663/pjpprp.v16i2.256", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "caid", "name": "Conversational AI Dependence Scale (dual-dimensional)", "acronym": "CAID", "summary": "A 14-item scale that splits dependence on conversational AI (chatbots) into instrumental dependence (relying on it to get things done) and emotional dependence (relying on it for feelings and support). Use it when those two kinds of reliance may relate differently to distress, personality or wellbeing.", "target": "Conversational AI (chatbots)", "constructs": [ "dependence", "relationships" ], "populations": [ "general adults" ], "items": 14, "response": null, "subscales": [ { "name": "Instrumental dependence", "items": null, "description": "Reliance on conversational AI for tasks and practical functioning." }, { "name": "Emotional dependence", "items": null, "description": "Reliance on conversational AI for emotional needs." } ], "psychometrics": { "structure": "Two factors, refined from a 42-item pool generated from focus groups", "reliability": "Not verified (the abstract gives no coefficients; a narrative review says reliability was reported only in general terms)", "validity": [ "Criterion validity (N=986): the two dimensions related to psychological distress, personality traits and psychosocial constructs, and these associations differed between dimensions", "Convergent: strong correlations with existing CAI dependence measures", "Incremental validity via hierarchical regression" ], "samples": [ { "n": 16, "country": "China (inferred from affiliations; not confirmed)", "population": "focus group participants" }, { "n": 589, "country": "China (inferred from affiliations; not confirmed)", "population": "self-identified CAI-dependent users (development)" }, { "n": 986, "country": "China (inferred from affiliations; not confirmed)", "population": "criterion validity sample" } ] }, "status": "published", "citation": "Li, L., Wang, T., Wang, Y., Wang, Y., & Zeng, X. (2026). Development and validation of the Conversational AI Dependence Scale (CAID): A dual-dimensional measure of instrumental and emotional dependence. International Journal of Human–Computer Interaction, 42(18), 15359–15379. https://doi.org/10.1080/10447318.2026.2618563", "authors": "Lanbing Li; Tianyu Wang; Yanding Wang; Yunheng Wang; Xianglong Zeng", "year": 2026, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2026.2618563", "url": "https://doi.org/10.1080/10447318.2026.2618563", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "Factor structure and convergent, criterion and incremental validity are reported, but reliability coefficients could not be confirmed.", "verified": "2026-09-29" }, { "id": "critical-thinking-in-ai-use-scale", "name": "Critical Thinking in AI Use Scale", "acronym": null, "summary": "A 13-item measure of how much people check what generative AI tells them, want to understand how AI models work and fail, and think about what relying on AI means. Use it to study fact-checking of AI output, over-reliance, and responsible GenAI use.", "target": "Generative AI / LLM tools (e.g., ChatGPT)", "constructs": [ "reliance", "literacy", "credibility" ], "populations": [ "general adults", "university students" ], "items": 13, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Verification", "items": 5, "description": "Checking the sources and content of AI outputs rather than accepting them at face value." }, { "name": "Motivation", "items": 4, "description": "Wanting to understand how AI models work and where they fail." }, { "name": "Reflection", "items": 4, "description": "Reflecting on the ethical, societal and personal implications of relying on AI." } ], "psychometrics": { "structure": "Study 1: content validation (24 naive judges). Study 2: EFA (n=270) reduced 27 items to 13 across 3 factors (RMSEA=.04, TLI=.98, CFI=.99). Study 3: correlated and higher-order CFA (n=376), χ²(62)=131.75, CFI=1.00, TLI=1.00, RMSEA=.06, SRMR=.05. Study 4 (n=290): CFA χ²(62)=144.22, CFI=.97, TLI=.96, RMSEA=.07", "reliability": "Study 4 α total .92; Verification .90, Motivation .91, Reflection .87 (Study 3: .90/.90/.81). Test-retest: 1-week r/ICC .70 (subscales .63–.75); 2-week r .65 (subscales .61–.66)", "validity": [ "Sex invariance: configural, metric, scalar and residual (all ΔCFI ≤ .01)", "Convergent validity (AVE .59–.77), discriminant evidence, and a nomological network: openness, extraversion, positive affect, AI use frequency; weak link with CRT", "Criterion (Study 6): higher scores predicted more frequent and varied verification strategies, better veracity-judgement accuracy in a GPT-powered chatbot fact-checking task, and deeper reflection on responsible AI" ], "samples": [ { "n": 270, "country": "United States", "population": "Adults (Prolific), EFA" }, { "n": 376, "country": "United States", "population": "Adults (Prolific), CFA/invariance/nomological network" }, { "n": 290, "country": "United States", "population": "Adult AI users (Prolific), second validation" }, { "n": 92, "country": "Singapore", "population": "University students, 1-week test-retest" }, { "n": 122, "country": "Singapore", "population": "Young adults (Telegram recruitment), 2-week test-retest" }, { "n": 191, "country": "not stated (Prolific)", "population": "Adults, criterion study (200 recruited, 9 excluded)" } ] }, "status": "published", "citation": "Lau, G. R., Low, W. Y., Tay, L., Guevarra, Y. A., Gašević, D., & Hartanto, A. (2026). Understanding critical thinking in generative artificial intelligence use: Development, validation, and correlates of the critical thinking in AI use scale. Computers in Human Behavior Reports, 22, 101103. https://doi.org/10.1016/j.chbr.2026.101103", "authors": "Gabriel R. Lau, Wei Yan Low, Louis Tay, Ysabel A. Guevarra, Dragan Gašević, Andree Hartanto", "year": 2026, "venue": "Computers in Human Behavior Reports", "doi": "10.1016/j.chbr.2026.101103", "url": "https://doi.org/10.1016/j.chbr.2026.101103", "preprint_url": "https://arxiv.org/abs/2512.12413", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "deep-learning-approach-genai-scale", "name": "Deep Learning Approach Scale in Generative AI Context", "acronym": null, "summary": "A 16-item scale of whether university students take a deep approach to learning when they use generative AI: looking for meaning, connecting ideas, checking evidence and focusing on concepts rather than copying answers. Use it to study how students learn with GenAI tools such as ChatGPT.", "target": "generative AI tools in university learning", "constructs": [ "learning" ], "populations": [ "university students" ], "items": 16, "response": null, "subscales": [ { "name": "Seeking meaning", "items": 4, "description": "Trying to understand material when using GenAI (EFA label 'Seeking Meaning')" }, { "name": "Relating (connecting) ideas", "items": 4, "description": "Linking AI-provided ideas with prior knowledge (EFA label 'Linking Ideas')" }, { "name": "Analysing evidence", "items": 4, "description": "Checking and verifying AI-provided information before accepting it (EFA label 'Evidence Analysis')" }, { "name": "Attention to concepts", "items": 4, "description": "Interest in and focus on core concepts discovered with AI (EFA label 'Conceptual Interest')" } ], "psychometrics": { "structure": "EFA (PCA, varimax; KMO .822) 4 factors, 86.1% variance; first-order CFA (CFI .954, RMSEA .051) and second-order CFA with a higher-order 'Deep Learning' factor (CFI .949, RMSEA .079); EBICglasso network analysis showing 4 communities", "reliability": "α .79–.98 (subscales), α .848 (total); CR .835–.998", "validity": [ "Convergent validity: AVE .561–.991, CR > .70", "Discriminant validity: Fornell–Larcker criterion met; HTMT .09–.47", "Expert content review" ], "samples": [ { "n": 250, "country": "Egypt", "population": "Damanhour University Faculty of Education students with GenAI experience (EFA)" }, { "n": 515, "country": "Egypt", "population": "Damanhour University Faculty of Education students with GenAI experience (CFA)" } ] }, "status": "published", "citation": "Daha, E. S., & Altelwany, A. A. (2026). From prompting to understanding: Factorial structure and network psychometrics of a deep-learning scale in generative AI context. International Journal of Instruction, 19(2), 749–766. https://doi.org/10.29333/iji.2026.19240a", "authors": "Eman Salah Daha; Aml Altelwany Altelwany", "year": 2026, "venue": "International Journal of Instruction", "doi": "10.29333/iji.2026.19240a", "url": "https://doi.org/10.29333/iji.2026.19240a", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "edias", "name": "Emotional Delegation to AI Scale", "acronym": "EDIAS", "summary": "A 13-item measure of how much people hand over emotional processing to AI chatbots, such as asking a chatbot for validation, reassurance or perspective when upset. Useful in studies of chatbot use for emotional support and of digital empathy.", "target": "Conversational AI chatbots (e.g., ChatGPT, Gemini, Character.AI)", "constructs": [ "relationships", "dependence", "health" ], "populations": [ "general adults" ], "items": 13, "response": "6-point Likert (1 = strongly disagree to 6 = strongly agree)", "subscales": [ { "name": "(unidimensional)", "items": 13, "description": "Turning to AI chatbots for validation, reassurance, perspective-taking and reflective processing of emotions" } ], "psychometrics": { "structure": "15 candidate items. Parallel analysis and one-factor PAF EFA (n = 232) dropped 2 items. CFA (n = 233): CFI .998, RMSEA .013. AVE .48.", "reliability": "α .92; ω .92", "validity": [ "Convergent: r = .21–.35 with AI use intensity, parasocial attachment to AI and general technology dependence", "Discriminant: HTMT < .85 with those constructs; near-zero correlation with a marker variable (enjoyment of cooking)", "Nomological (Study 2, CB-SEM): negative direct association with digital empathy, with indirect paths through social presence, perceived AI empathy and digital emotional literacy" ], "samples": [ { "n": 465, "country": "India", "population": "adult chatbot users (Prolific and university recruitment; scale development)" }, { "n": 652, "country": "India", "population": "adult chatbot users (nomological study)" } ] }, "status": "preprint", "citation": "Khan, S. A., Hussain, S. A., Dsilva, A. B. M., & M, P. (2026). Emotional delegation to AI: Scale development and its association with digital empathy among Indian adults [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-9765754/v1", "authors": "Shoaib Ahamed Khan; Syed Akbar Hussain; Anitha BM Dsilva; Prerana M", "year": 2026, "venue": "Research Square", "doi": "10.21203/rs.3.rs-9765754/v1", "url": "https://doi.org/10.21203/rs.3.rs-9765754/v1", "preprint_url": "https://www.researchsquare.com/article/rs-9765754/latest.pdf", "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "elt-air", "name": "English Language Teachers' Generative AI Readiness Scale", "acronym": "ELT-AIR", "summary": "Measures how ready English teachers are to use generative AI, extending the TPACK framework. It covers GenAI technical knowledge, AI use and awareness, pedagogical integration of GenAI into English lessons, and AI ethics. Use it to spot whether a teacher needs technical, pedagogical or ethics training.", "target": "generative AI in English language teaching", "constructs": [ "literacy", "ethics-concerns", "workplace" ], "populations": [ "teachers & academics" ], "items": 39, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Technological Pedagogical Content Knowledge (TPACK integration)", "items": null, "description": "Integrating GenAI tools, teaching strategies and English content (includes TPK, TCK and TPACK items)" }, { "name": "AI Use and Awareness", "items": null, "description": "Recognising and applying GenAI tools to complete tasks" }, { "name": "Technological Knowledge (TK)", "items": null, "description": "Familiarity with and proficiency in using GenAI tools" }, { "name": "AI Ethics", "items": null, "description": "Awareness of ethical responsibilities and risks of GenAI (privacy, bias, fairness, policy)" } ], "psychometrics": { "structure": "41 items drafted in 8 a-priori dimensions (TK, TPK, TCK, TPACK, AI awareness, use, evaluation, ethics). EFA (promax, n=307): 4 factors, 67.3% variance. Second-order CFA on the same sample: 41 items CFI .889, SRMR .045, RMSEA .080; after dropping 2 items (39 items) CFI .901, SRMR .044, RMSEA .077", "reliability": "α .847–.970 across the 8 a-priori dimensions (n=307)", "validity": [ "Content validity (4-expert panel review; pilot n=16)", "Known-groups: pre-service teachers scored higher than in-service teachers on all 8 domains (Welch t-tests, p < .05)", "Qualitative triangulation through 10 teacher interviews" ], "samples": [ { "n": 201, "country": "Hong Kong, Macao and Greater Bay Area (China)", "population": "in-service English teachers" }, { "n": 106, "country": "Hong Kong", "population": "pre-service English teachers" } ] }, "status": "published", "citation": "Chan, K. K.-W., & Tang, W. K.-W. (2026). Extending TPACK for the GenAI era: Development and validation of an English Language Teachers' Generative AI Readiness Scale. Education Sciences, 16(6), 859. https://doi.org/10.3390/educsci16060859", "authors": "Kevin Kai-Wing Chan; William Ko-Wai Tang", "year": 2026, "venue": "Education Sciences", "doi": "10.3390/educsci16060859", "url": "https://doi.org/10.3390/educsci16060859", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ed-ail", "name": "Epistemic Dependence in AI-Mediated Learning Scale", "acronym": "ED-AIL", "summary": "A 23-item scale of how far students uncritically treat generative AI outputs as authoritative when learning. It covers five areas: getting information, interpreting concepts, judging claims, producing work, and steering their own study. Use it to tell productive AI support apart from uncritical handing-over of judgement to AI in higher education.", "target": "Generative AI in higher-education learning", "constructs": [ "dependence", "reliance", "learning" ], "populations": [ "university students" ], "items": 23, "response": "5-point agreement (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Knowledge acquisition dependence", "items": 5, "description": "Using AI output as the basis for knowledge without seeking or checking other sources" }, { "name": "Conceptual interpretation dependence", "items": 5, "description": "Accepting AI's interpretation of concepts and texts as one's own understanding" }, { "name": "Epistemic evaluation dependence", "items": 5, "description": "Letting AI feedback decide whether answers, arguments or sources are good or credible" }, { "name": "Knowledge production dependence", "items": 4, "description": "Letting AI outlines, ideas and wording shape one's academic work" }, { "name": "Epistemic regulation dependence", "items": 4, "description": "Letting AI decide what to study next and when understanding is sufficient (least stable subscale)" } ], "psychometrics": { "structure": "Ordinal EFA (Sample 1) and then CFA (Sample 2). Correlated five-factor model retained: CFI .944, TLI .936, RMSEA .051, SRMR .049. A bifactor sensitivity model gave ωH .64 and ECV .52. The fifth factor was empirically fragile.", "reliability": "Subscale α .70–.87 and ω .72–.88; total α .90 and ω .91; CR .65–.84 (Sample 2)", "validity": [ "Convergent/related: r = .48 with AI reliance, .37 with AI trust, .34 with cognitive offloading, and .46 with unverified AI-output acceptance", "Discriminant: r = .08 with AI literacy, .28 with GenAI use frequency, and −.27 with source verification", "Performance criteria: r = −.14 with AI error-detection accuracy and −.12 with source-corroboration performance", "Incremental: ΔR² = .05 for unverified output acceptance and .02 for error-detection, beyond trust, literacy, use frequency and SRL", "Measurement invariance: configural and loading invariance held across gender, degree level and use frequency; threshold invariance was less stable", "AVE was low (.32–.51)" ], "samples": [ { "n": 756, "country": "Multinational (UK 40%, US/Canada 33%, other Europe 16%; recruited via Prolific)", "population": "higher education students (Sample 1, EFA)" }, { "n": 789, "country": "Multinational (UK 38%, US/Canada 35%, other Europe 16%; recruited via Prolific)", "population": "higher education students (Sample 2, CFA/validity)" } ] }, "status": "preprint", "citation": "Du, Y., & Zou, B. (2026). Conceptualising and measuring epistemic dependence in AI-mediated learning: Development and initial validation of the ED-AIL Scale [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-10627520/v1", "authors": "Yiran Du; Bin Zou", "year": 2026, "venue": "Research Square", "doi": "10.21203/rs.3.rs-10627520/v1", "url": "https://www.researchsquare.com/article/rs-10627520/v1", "preprint_url": "https://doi.org/10.21203/rs.3.rs-10627520/v1", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ftgai", "name": "Fears Towards Generative Artificial Intelligence Scale", "acronym": "FTGAI", "summary": "Measures how worried people are about generative AI's effects on society, work, human autonomy and trustworthy information, as one overall fear score. Use it to compare GenAI fear across groups or link it to use and attitudes; a 4-item short form (FTGAI-SF) suits quick surveys.", "target": "Generative AI (GenAI) in general", "constructs": [ "anxiety", "attitudes", "ethics-concerns" ], "populations": [ "general adults" ], "items": 34, "response": "5-point worry rating (1 = 'I'm not worried at all' to 5 = 'I am extremely worried')", "subscales": [ { "name": "(unidimensional)", "items": 34, "description": "Overall fear of GenAI across blurred human/machine boundaries, replacement of humans, loss of control, inequality and polarization, job loss, regulation and responsibility, and unreliable or fake outputs" } ], "psychometrics": { "structure": "Factor analysis (WLSMV). Parallel analysis suggested 3 factors; a theoretically preferred unidimensional model was kept (CFI .92 initially; CFI .94, TLI .92, RMSEA .083, SRMR .084 after dropping 3 of 37 items). A 4-item short form was chosen by genetic algorithm; in a new sample (n = 101) a one-factor model fit well (CFI .99, TLI .99, RMSEA .060, SRMR .025).", "reliability": "α .92 (34 items); short-form α below conventional thresholds (few items)", "validity": [ "Convergent/nomological: r = .63 with GAAIS negative attitudes, .44 with perceived AI job threat; positive with technophobia and neuroticism", "Negative correlations with GenAI familiarity (r = −.22) and frequency of use (r = −.28)", "Short form correlates r = .89 (development) and r = .85 (independent sample) with the full scale; Bland–Altman agreement" ], "samples": [ { "n": 303, "country": "Spain", "population": "Adults aged 18–75 (Prolific)" }, { "n": 101, "country": "Spain", "population": "Adults aged 21–54, independent short-form sample (Prolific)" } ] }, "status": "published", "citation": "Corradi, G., Theirs, C., Martínez-Martí, M. L., Isern-Mas, C., & Villar, S. (2026). Who fears generative artificial intelligence? Scale development and predictors of fears towards GenAI. Scandinavian Journal of Psychology, 67(2), 396–412. https://doi.org/10.1111/sjop.70037", "authors": "Corradi, G., Theirs, C., Martínez-Martí, M. L., Isern-Mas, C., & Villar, S.", "year": 2026, "venue": "Scandinavian Journal of Psychology", "doi": "10.1111/sjop.70037", "url": "https://doi.org/10.1111/sjop.70037", "preprint_url": "https://doi.org/10.31234/osf.io/wvz9x_v1", "items_available": true, "language": "Spanish", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gecs", "name": "GAI-Assisted Engineering Creativity Scale", "acronym": "GECS", "summary": "A 16-item scale of how creatively engineering students work with generative AI: using it on purpose, keeping their own ideas, co-creating with it, and critically combining its output with their own work. Use it in engineering or design education research on GenAI and creativity.", "target": "generative AI in engineering education", "constructs": [ "creativity", "learning" ], "populations": [ "graduate students", "university students" ], "items": 16, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Intentionality", "items": 4, "description": "Using GAI purposefully and with clear creative goals" }, { "name": "Authenticity", "items": 4, "description": "Keeping one's own creative voice and ownership when using GAI" }, { "name": "Human–AI Collaboration", "items": 4, "description": "Co-creating and iterating ideas together with GAI" }, { "name": "Critical Integration", "items": 4, "description": "Critically evaluating and integrating GAI output into engineering work" } ], "psychometrics": { "structure": "Study 2 EFA (17 items) 4 factors; Study 3 CFA comparing alternative models (correlated four-factor model retained; higher-order model acceptable), e.g., CFI .982, TLI .978, RMSEA .053, SRMR .018; one item (Intentionality5) removed after test–retest in Study 4, giving 16 items", "reliability": "α .969 total, .908–.929 subscales (Study 6); α .912 for the 17-item version (Study 3); 1-week test–retest ICC(A,1) .900 for the 16-item total, r = .937 (the limitations section cites an overall ICC of .712)", "validity": [ "Measurement invariance (configural to strict) across gender", "Convergent and discriminant validity with the AI-Assisted Creativity Questionnaire and general creative attributes/behaviours (SCAB)", "Nomological associations with creative thinking, critical thinking, creative self-concept and general creativity" ], "samples": [ { "n": 2629, "country": "China", "population": "undergraduate engineering students (Study 2, EFA)" }, { "n": 1239, "country": "China", "population": "international undergraduate engineering students at Chinese universities (Study 3, CFA/invariance)" }, { "n": 987, "country": "China", "population": "undergraduate engineering students (Study 4, test–retest)" }, { "n": 978, "country": "China", "population": "engineering students (Study 5, convergent/discriminant)" }, { "n": 1940, "country": "China", "population": "undergraduate and graduate engineering students (Study 6, nomological)" } ] }, "status": "published", "citation": "Liu, Y., & Guo, H. (2026). Development and validation of a GAI-assisted engineering creativity scale for university students. Applied Sciences, 16(16), 8251. https://doi.org/10.3390/app16168251", "authors": "Ying Liu; Huifen Guo", "year": 2026, "venue": "Applied Sciences", "doi": "10.3390/app16168251", "url": "https://doi.org/10.3390/app16168251", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gpt-metacognitive-awareness-scale", "name": "GPT-Assisted Metacognitive Awareness Scale", "acronym": null, "summary": "A 22-item true/false self-report of how aware people are of their own thinking while using GPT tools. It covers strategic engagement with GPT and reflective self-regulation. Use it in exploratory research on metacognition during AI-assisted study or problem solving; its validity evidence is still thin.", "target": "GPT / ChatGPT-assisted cognition", "constructs": [ "learning", "other" ], "populations": [ "graduate students", "university students" ], "items": 22, "response": "True/False", "subscales": [ { "name": "Strategic & motivational engagement with GPT", "items": 9, "description": "Planning and monitoring while doing tasks with GPT (the Discussion calls it 'task-oriented metacognitive engagement')" }, { "name": "Metacognitive regulation and reflective processes", "items": 13, "description": "Regulating and reflecting on one's own thinking during GPT use" } ], "psychometrics": { "structure": "Two factors: EFA (n = 136), then CFA (n = 959): CFI .912, TLI .902, SRMR .034, RMSEA .034", "reliability": "Total α .811 and ω .814; factor α .709–.716 and ω .714–.718; CR .714–.717", "validity": [ "Discriminant between the two factors: HTMT = .78", "Convergent validity weak: AVE .165–.221 (below .50)", "No correlations with external measures reported" ], "samples": [ { "n": 136, "country": "India", "population": "university students aged 18–25 (EFA)" }, { "n": 959, "country": "India", "population": "university students aged 18–25 (CFA)" } ] }, "status": "published", "citation": "Varghese, M. A., & Sharma, P. (2026). Measuring human metacognition in GPT-assisted cognition: Development and psychometric validation of a novel scale. Frontiers in Psychology, 17, Article 1823525. https://doi.org/10.3389/fpsyg.2026.1823525", "authors": "Mahima Anna Varghese; Poonam Sharma", "year": 2026, "venue": "Frontiers in Psychology", "doi": "10.3389/fpsyg.2026.1823525", "url": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1823525/full", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "Validity evidence is internal only: convergent validity failed (low AVE) and no external measures were tested.", "verified": "2026-09-29" }, { "id": "genai-competence-idll", "name": "GenAI Competence Scale for Informal Digital Language Learning", "acronym": null, "summary": "A 24-item scale, with a 12-item short form, of learners' competence to use generative AI for independent, informal language learning: knowledge, skills, attitudes and self-management. Use it to diagnose how ready learners are to learn languages with GenAI outside class.", "target": "generative AI in informal digital language learning", "constructs": [ "literacy", "learning" ], "populations": [ "university students" ], "items": 24, "response": null, "subscales": [ { "name": "Knowledge", "items": null, "description": "Knowing how GenAI works for language learning" }, { "name": "Skills", "items": null, "description": "Interpreting, evaluating and using machine-generated output" }, { "name": "Attitudes", "items": null, "description": "Dispositions toward GenAI-supported learning" }, { "name": "Management", "items": null, "description": "Managing one's own GenAI-supported learning and balancing collaboration with GenAI" } ], "psychometrics": { "structure": "Study 1: EFA, exploratory graph analysis and CFA (24 items, 4 dimensions); Study 2: 12-item short form derived with genetic algorithms", "reliability": "Not reported in abstract (unverified)", "validity": [ "Structural validity via EFA/EGA/CFA (other validity evidence not reported in abstract)" ], "samples": [ { "n": null, "country": "not reported in abstract", "population": "language learners" } ] }, "status": "published", "citation": "Wang, X., & Zhang, L. J. (2026). Development, validation, and refinement of the GenAI competence scale for informal digital language learning: A network and machine learning approach. Innovation in Language Learning and Teaching. Advance online publication. https://doi.org/10.1080/17501229.2026.2718473", "authors": "Xiaoqi Wang; Lawrence Jun Zhang", "year": 2026, "venue": "Innovation in Language Learning and Teaching", "doi": "10.1080/17501229.2026.2718473", "url": "https://doi.org/10.1080/17501229.2026.2718473", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "Structure is reported (EFA, EGA, CFA), but no reliability, sample details or validity evidence beyond factor structure are visible.", "verified": "2026-09-29" }, { "id": "genai-competence-preservice-l2-teachers", "name": "GenAI Competence Scale for Pre-service L2 Teachers", "acronym": null, "summary": "A 21-item scale of pre-service second-language teachers' generative AI competence: awareness of and willingness to use GenAI, knowledge and practical application, and social responsibility. Use it to assess and profile language-teacher trainees' readiness to teach with GenAI.", "target": "generative AI in language teacher education", "constructs": [ "literacy", "ethics-concerns" ], "populations": [ "teachers & academics" ], "items": 21, "response": null, "subscales": [ { "name": "Awareness and Willingness", "items": null, "description": "Awareness of GenAI and willingness to use it" }, { "name": "Knowledge and Application", "items": null, "description": "Knowing about GenAI and applying it in teaching" }, { "name": "Social Responsibility", "items": null, "description": "Responsible and ethical use of GenAI" } ], "psychometrics": { "structure": "EFA (n = 350) gave 3 factors, 21 items; CFA (n = 358) showed good fit (indices not in abstract)", "reliability": "Reported as high (values not in abstract)", "validity": [ "Convergent validity", "Discriminant validity", "Criterion-related validity", "Cross-gender measurement invariance", "Latent profile analysis identified 3 profiles (n = 708)" ], "samples": [ { "n": 350, "country": "China", "population": "pre-service L2 teachers (EFA)" }, { "n": 358, "country": "China", "population": "pre-service L2 teachers (CFA and validity)" } ] }, "status": "published", "citation": "Wu, H., Wang, Y., & Lalli, G. S. (2026). Scale validation and latent profile identification of GenAI competence for pre-service second language teachers. International Review of Applied Linguistics in Language Teaching, 64(2), 1121–1150. https://doi.org/10.1515/iral-2024-0301", "authors": "Hanwei Wu; Yongliang Wang; Gurpinder Singh Lalli", "year": 2026, "venue": "International Review of Applied Linguistics in Language Teaching", "doi": "10.1515/iral-2024-0301", "url": "https://doi.org/10.1515/iral-2024-0301", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ai-l2-iles", "name": "GenAI L2 Interactive Learning Experience Scale", "acronym": "AI-L2-ILES", "summary": "A 16-item scale on how second-language learners rate their interactive learning experiences with generative AI tools (ChatGPT, DeepSeek, Kimi): how effective and social the interaction feels, how novel it is, how absorbed they get, and how smooth it is. Use it to study learner experience with GenAI conversation partners in language learning.", "target": "generative AI (ChatGPT, DeepSeek, Kimi) in L2 learning", "constructs": [ "learning", "social-presence" ], "populations": [ "university students" ], "items": 16, "response": "6-point Likert (strongly disagree to strongly agree)", "subscales": [ { "name": "Effectiveness", "items": 4, "description": "How efficiently and effectively GenAI supports L2 learning" }, { "name": "Sociability", "items": 3, "description": "GenAI felt as a socially responsive interaction partner" }, { "name": "Novelty", "items": 3, "description": "Newness and freshness of the interaction" }, { "name": "Flow", "items": 3, "description": "Absorption and focused involvement during the interaction" }, { "name": "Seamlessness", "items": 3, "description": "Smooth, continuous and uninterrupted interaction" } ], "psychometrics": { "structure": "EFA (n = 565; ML, promax): 5 factors, 16 items retained from 18. CFA (n = 565) supported 5 factors over one-factor, four-factor and higher-order alternatives. Gaussian graphical network analysis (full N = 1130): Flow was the most central node.", "reliability": "ω total .885; subscales ω .836–.889; CR .842–.893", "validity": [ "Convergent validity (AVE .635–.737)", "Discriminant validity (Fornell–Larcker; HTMT .103–.765)", "Criterion validity with GenAI-IDLE (r .268–.670)", "Configural, metric and scalar invariance across gender and academic discipline", "Qualitative content review by 6 experts and 27-student focus groups" ], "samples": [ { "n": 565, "country": "China", "population": "university students at 5 universities (EFA subsample)" }, { "n": 565, "country": "China", "population": "university students at 5 universities (CFA/validity subsample)" } ] }, "status": "published", "citation": "Wu, H., Lalli, G. S., & Wang, Y. (2026). Second language learners' cognitive evaluation of GenAI interactive learning experience: Scale development and network analysis. Journal of Intelligence, 14(9), 226. https://doi.org/10.3390/jintelligence14090226", "authors": "Hanwei Wu; Gurpinder Singh Lalli; Yongliang Wang", "year": 2026, "venue": "Journal of Intelligence", "doi": "10.3390/jintelligence14090226", "url": "https://doi.org/10.3390/jintelligence14090226", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "g-cavs", "name": "General Chatbot Acceptance, Enjoyment, Perceived Risk, and Value Scale", "acronym": "G-CAVS", "summary": "A 14-item scale of teachers' views of general-purpose AI chatbots such as ChatGPT, DeepSeek and Grok in education: acceptance, enjoyment, perceived value, and worry about personal data. Use it in teacher-education research on chatbot adoption.", "target": "General-purpose conversational AI chatbots (e.g., ChatGPT, DeepSeek, Grok) in education", "constructs": [ "acceptance-use", "attitudes", "privacy" ], "populations": [ "teachers & academics" ], "items": 14, "response": "5-point Likert", "subscales": [ { "name": "Acceptance", "items": 4, "description": "Seeing chatbot use in universities and education as appropriate and meaningful" }, { "name": "Enjoyment of use", "items": 3, "description": "Fun and curiosity when interacting with chatbots" }, { "name": "Perceived value", "items": 5, "description": "Chatbots save time, add value to learning and increase success" }, { "name": "Perceived risk", "items": 2, "description": "Worry that personal data may be shared or misused when using chatbots" } ], "psychometrics": { "structure": "EFA (n = 216): 4 factors, 14 items, 69.8% variance. CFA (subsample n = 184): χ²/df 1.58, CFI .96, TLI .95, RMSEA .06, SRMR .04. Turkish in-service sample (n = 263): χ² = 173, df = 71, CFI .95, TLI .94, RMSEA .07, SRMR .03.", "reliability": "α .80–.89; ω .81–.89; CR .81–.89; AVE .46–.81 (perceived value .461)", "validity": [ "Criterion: acceptance, enjoyment and value correlate with chatbot usage frequency (Spearman ρ = .43–.46); perceived risk does not (ρ = .09)", "Structure confirmed by CFA in an independent Turkish in-service teacher sample", "Discriminant evidence via AVE/MSV and low risk–value correlation" ], "samples": [ { "n": 216, "country": "Germany", "population": "Pre-service teachers (development sample; 224 surveyed, 216 retained; CFA subsample n = 184)" }, { "n": 263, "country": "Türkiye", "population": "In-service teachers (confirmatory sample)" } ] }, "status": "published", "citation": "Polat, S., & Renner, G. (2026). General chatbot acceptance, enjoyment, perceived risk, and value (G-CAVS): Scale development and validation. Contemporary Educational Technology, 18(1), ep627. https://doi.org/10.30935/cedtech/17878", "authors": "Polat, S., & Renner, G.", "year": 2026, "venue": "Contemporary Educational Technology", "doi": "10.30935/cedtech/17878", "url": "https://doi.org/10.30935/cedtech/17878", "preprint_url": null, "items_available": true, "language": "German", "adaptations": [ { "language": "Turkish", "country": "Türkiye", "citation": "Polat, S., & Renner, G. (2026). General chatbot acceptance, enjoyment, perceived risk, and value (G-CAVS): Scale development and validation. Contemporary Educational Technology, 18(1), ep627. https://doi.org/10.30935/cedtech/17878", "doi": "10.30935/cedtech/17878", "url": "https://doi.org/10.30935/cedtech/17878", "status": "published", "notes": "Turkish version tested by CFA in the same paper: n = 263 in-service teachers, CFI .95, TLI .94, RMSEA .07, SRMR .03." } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gaa-ls", "name": "Generative AI Assessment Literacy Scale", "acronym": "GAA-LS", "summary": "An 18-item scale of whether higher-education students know how to use GenAI appropriately around assessment. It covers understanding criteria, judging when AI use fits the task, checking evidence, crediting AI help honestly, and using AI feedback to revise. Useful for research on AI feedback, academic integrity and assessment literacy.", "target": "Generative AI (in assessment and feedback contexts)", "constructs": [ "literacy", "academic-integrity", "learning" ], "populations": [ "university students" ], "items": 18, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Assessment criteria awareness", "items": 4, "description": "Understanding assessment criteria when using GenAI" }, { "name": "AI-task appropriateness judgment", "items": 4, "description": "Judging when GenAI use is appropriate for a task" }, { "name": "Verification and evidence checking", "items": 4, "description": "Verifying GenAI outputs against evidence" }, { "name": "Ethical attribution and academic integrity", "items": 3, "description": "Disclosing and attributing AI assistance honestly" }, { "name": "Feedback uptake and revision literacy", "items": 3, "description": "Using AI feedback to revise work" } ], "psychometrics": { "structure": "Five correlated factors from EFA (Study 1), confirmed by CFA (Study 2)", "reliability": "Subscale α .83–.88 and total α .93; subscale ω .84–.88 and total ω .94; CR .84–.89", "validity": [ "AVE .60–.66 (convergent)", "HTMT .54–.76 (discriminant)", "Criterion: r = .60 with feedback engagement, .51 with academic integrity intention, and .59 with responsible AI use", "Configural, metric and scalar invariance across gender, discipline and AI-use frequency" ], "samples": [ { "n": 486, "country": "China", "population": "higher education students (Study 1, EFA)" }, { "n": 798, "country": "China", "population": "higher education students (Study 2, CFA/validation)" } ] }, "status": "published", "citation": "Nie, J., Zhang, Z., Lu, X., Zhang, Y., & Zhang, M. (2026). Development and validation of the generative AI assessment literacy scale for higher education students: Psychometric evidence and associations with feedback engagement and academic integrity. Frontiers in Education, 11, Article 1934632. https://doi.org/10.3389/feduc.2026.1934632", "authors": "Juntao Nie; Zeyu Zhang; Xiaomei Lu; Yinlan Zhang; Min Zhang", "year": 2026, "venue": "Frontiers in Education", "doi": "10.3389/feduc.2026.1934632", "url": "https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1934632/full", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genaicomp", "name": "Generative AI Competence Scale", "acronym": "GenAIComp", "summary": "A self-report of university students' competence with generative AI, modelled on the DigComp digital-competence areas: information/data literacy, communication and collaboration, content creation, safety and ethics, and problem-solving. Use it to profile students' GenAI competence or to evaluate AI-literacy training.", "target": "Generative AI", "constructs": [ "literacy" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Information and data literacy", "items": null, "description": "Finding, managing and evaluating information with GenAI" }, { "name": "Communication and collaboration", "items": null, "description": "Interacting and collaborating with or through GenAI" }, { "name": "Digital content creation", "items": null, "description": "Creating content with GenAI" }, { "name": "Safety and ethics", "items": null, "description": "Safe, ethical and responsible GenAI use" }, { "name": "Problem-solving", "items": null, "description": "Using GenAI to solve problems" } ], "psychometrics": { "structure": "Five factors confirmed by factor analysis, with excellent fit indices reported", "reliability": "Reported as reliable; numeric values not seen (paywalled)", "validity": [ "Expert validation and pilot testing", "Known-groups: technical-discipline students scored higher on problem-solving and content creation; small gender differences", "Correlations with perceived AI expertise and frequency of AI use (stronger for data literacy and problem-solving, weaker for ethics)" ], "samples": [ { "n": 1000, "country": "unknown (authors based in South Korea)", "population": "mainly university students" } ] }, "status": "published", "citation": "Lee, S. C., Baby, T., Vongvit, R., Lee, J., Kim, Y. W., Cha, M. C., & Yoon, S. H. (2026). Development and validation of Generative AI Competence Scale (GenAIComp) among university students. Technology in Society, 84, Article 103059. https://doi.org/10.1016/j.techsoc.2025.103059", "authors": "Seul Chan Lee; Tiju Baby; Rattawut Vongvit; Jieun Lee; Young Woo Kim; Min Chul Cha; Sol Hee Yoon", "year": 2026, "venue": "Technology in Society", "doi": "10.1016/j.techsoc.2025.103059", "url": "https://www.sciencedirect.com/science/article/pii/S0160791X25002490", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gaics-tr", "name": "Generative AI Competency Self-Efficacy Scale for Teachers and Researchers", "acronym": "GAICS-TR", "summary": "Measures how confident university academics feel using generative AI across four areas: basic understanding and ethics, teaching, research, and professional engagement. Use it for faculty-development needs assessment or higher-education research on GenAI adoption.", "target": "Generative AI in higher-education teaching and research", "constructs": [ "self-efficacy", "literacy", "workplace" ], "populations": [ "teachers & academics" ], "items": 35, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "GenAI knowledge (AIK)", "items": 4, "description": "Identifying, explaining and choosing GenAI tools (Domain 1: basic understanding)" }, { "name": "GenAI ethics (AIE)", "items": 3, "description": "Protecting sensitive data and teaching safe, responsible GenAI use (Domain 1)" }, { "name": "GenAI for student interaction (GSI)", "items": 4, "description": "Using GenAI to make classes interactive and engaging (Domain 2: teaching)" }, { "name": "GenAI for learning design (GLD)", "items": 4, "description": "Creating courseware and learning resources with GenAI (Domain 2)" }, { "name": "GenAI for assessment (GAS)", "items": 4, "description": "Designing and running assessment in GenAI-supported settings (Domain 2)" }, { "name": "GenAI for research design (GRD)", "items": 4, "description": "Using GenAI to formulate topics, plan research and choose methods (Domain 3: research)" }, { "name": "GenAI data analysis (GDA)", "items": 4, "description": "Analysing and interpreting qualitative or quantitative data with GenAI (Domain 3)" }, { "name": "GenAI and professional development (GPD)", "items": 4, "description": "Using GenAI to update professional, technological and pedagogical knowledge (Domain 4: professional engagement)" }, { "name": "GenAI and its knowledge (GKG)", "items": 4, "description": "Keeping up with GenAI through workshops, reading and discussion (Domain 4)" } ], "psychometrics": { "structure": "Nine first-order dimensions in four domains. PLS-SEM CFA, then CB-SEM CFA (χ²/df = 1.31, RMSEA = .020, CFI = .988, TLI = .986), with a second-order CFA as a robustness check (RMSEA = .034)", "reliability": "α .983–.989; CR .987–.992 (per dimension)", "validity": [ "Convergent: AVE .951–.973 for all dimensions", "Discriminant: Fornell–Larcker criterion reported as met, but inter-dimension correlations reach .95–.98, so the dimensions may not be well separated", "Group comparisons: no significant differences by gender or age" ], "samples": [ { "n": 787, "country": "China", "population": "academics (teachers and researchers) from nine universities; 676 male, 111 female; age 21–53" } ] }, "status": "published", "citation": "Xia, Q., Yang, Y., Weng, X., Cilsalar-Sagnak, H., Cheng, W. K., & Chiu, T. K. F. (2026). Generative artificial intelligence competency self-efficacy scale for teachers and researchers (GAICS-TR) in higher education. Journal of Computers in Education, 13(2), 769–793. https://doi.org/10.1007/s40692-025-00373-y", "authors": "Qi Xia; Yiming Yang; Xiaojing Weng; Hatice Cilsalar-Sagnak; Wing Kin Cheng; Thomas K. F. Chiu", "year": 2026, "venue": "Journal of Computers in Education", "doi": "10.1007/s40692-025-00373-y", "url": "https://link.springer.com/article/10.1007/s40692-025-00373-y", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genaid-employee", "name": "Generative AI Dependence Scale for Employees", "acronym": "GenAID", "summary": "A 20-item workplace measure of employees' instrumental, psychological and cognitive dependence on generative AI. Use it in organizational research, e.g., on how dependence relates to innovation or thriving at work.", "target": "Generative AI at work", "constructs": [ "dependence", "workplace" ], "populations": [ "employees & professionals" ], "items": 20, "response": null, "subscales": [ { "name": "Instrumental dependence", "items": null, "description": "Reliance on GenAI to perform work tasks." }, { "name": "Psychological dependence", "items": null, "description": "Emotional or psychological reliance on GenAI at work." }, { "name": "Cognitive dependence", "items": null, "description": "Delegating thinking and judgment to GenAI." } ], "psychometrics": { "structure": "Three interrelated factors; CITC, EFA and CFA over three survey rounds; comparison of correlated three-factor, second-order, bifactor and one-factor models supported the use of a total score", "reliability": "ω coefficients reported (values not seen)", "validity": [ "Content-validity indices", "Discriminant: HTMT ratios", "Nomological: inverted-U relation with innovation performance via thriving at work, moderated by mindfulness (three-wave, N=342)" ], "samples": [ { "n": 24, "country": "China (inferred from affiliations; not confirmed)", "population": "knowledge workers (grounded-theory interviews)" }, { "n": 233, "country": "China (inferred from affiliations; not confirmed)", "population": "employees (survey round 1)" }, { "n": 271, "country": "China (inferred from affiliations; not confirmed)", "population": "employees (survey round 2)" }, { "n": 269, "country": "China (inferred from affiliations; not confirmed)", "population": "employees (survey round 3)" }, { "n": 342, "country": "China (inferred from affiliations; not confirmed)", "population": "employees (three-wave time-lagged study)" } ] }, "status": "published", "citation": "Chai, M., & Zeng, Q. (2026). The double-edged sword of generative AI dependence: Scale development and its curvilinear impact on employee innovation. BMC Psychology. https://doi.org/10.1186/s40359-026-05530-1", "authors": "Maochang Chai; Qi Zeng", "year": 2026, "venue": "BMC Psychology", "doi": "10.1186/s40359-026-05530-1", "url": "https://doi.org/10.1186/s40359-026-05530-1", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-dependence-university-courses-kr", "name": "Generative AI Dependence Scale for University Courses (Korean)", "acronym": null, "summary": "A 22-item Korean measure of which core learning activities university students hand over to generative AI (ideas, structuring information, performing tasks, verification), plus emotional dependence. Use it to diagnose course-level GenAI dependence in higher education.", "target": "Generative AI in university coursework", "constructs": [ "dependence", "learning", "reliance" ], "populations": [ "university students" ], "items": 22, "response": null, "subscales": [ { "name": "Idea dependence", "items": null, "description": "Relying on GenAI to generate ideas." }, { "name": "Information-structuring dependence", "items": null, "description": "Relying on GenAI to organize and structure information." }, { "name": "Performance dependence", "items": null, "description": "Delegating the doing of learning tasks to GenAI." }, { "name": "Verification dependence", "items": null, "description": "Relying on GenAI to check or verify work." }, { "name": "Emotional dependence", "items": null, "description": "Emotional reliance on GenAI for coursework." } ], "psychometrics": { "structure": "Five factors; EFA and CFA on two randomly assigned halves", "reliability": "α reported as supported (values not seen)", "validity": [ "Content validity by educational technology experts; pilot with 25 students", "Convergent and discriminant validity (CR, AVE) supported", "Criterion validity supported" ], "samples": [ { "n": 388, "country": "South Korea", "population": "students at 9 four-year universities" } ] }, "status": "published", "citation": "Kim, M. J., & Park, I. (2026). Development and validation of a scale for measuring generative AI dependence in university courses. Journal of Educational Information and Media Research, 32(2), 911–936. https://doi.org/10.15833/kafeiam.32.2.911", "authors": "Min Jun Kim; Innwoo Park", "year": 2026, "venue": "Journal of Educational Information and Media Research (교육정보미디어연구)", "doi": "10.15833/kafeiam.32.2.911", "url": "https://doi.org/10.15833/kafeiam.32.2.911", "preprint_url": null, "items_available": false, "language": "Korean", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-dependency-msd-students", "name": "Generative AI Dependency Scale (Media System Dependency)", "acronym": null, "summary": "Measures how much university students depend on generative AI to meet everyday goals: understanding, orientation/action, play and emotional comfort. It is based on Media System Dependency theory. Useful for studying why and how students lean on GenAI tools.", "target": "generative AI", "constructs": [ "dependence", "reliance" ], "populations": [ "university students" ], "items": 26, "response": null, "subscales": [ { "name": "Understanding goals", "items": null, "description": "Depending on GenAI to understand oneself and the world" }, { "name": "Play goals", "items": null, "description": "Depending on GenAI for entertainment and relaxation" }, { "name": "Action-oriented orientation goals (4 dimensions)", "items": null, "description": "Depending on GenAI to decide and act; the abstract says the MSD orientation goal split into four dimensions but does not name them" }, { "name": "Emotional comfort and support", "items": null, "description": "Depending on GenAI for emotional support (a new dimension)" } ], "psychometrics": { "structure": "EFA (n=175): 9 factors, 26 items, 61.17% variance explained; CFA (n=380): 'satisfactory' fit (indices not given in abstract)", "reliability": "Not reported in abstract", "validity": [ "Content validity (expert panel review)", "Factorial validity (CFA)" ], "samples": [ { "n": 175, "country": "China", "population": "university students with GenAI experience (EFA)" }, { "n": 380, "country": "China", "population": "university students with GenAI experience (CFA)" } ] }, "status": "preprint", "citation": "Mai, L., Yassin, E., & Jung, J.-Y. (2026). Measuring generative AI dependency: Scale development and validation among university students [Preprint]. SSRN. https://doi.org/10.2139/ssrn.6878994", "authors": "Lisi Mai; Eiman Yassin; Joo-Young Jung", "year": 2026, "venue": "SSRN (preprint)", "doi": "10.2139/ssrn.6878994", "url": "https://doi.org/10.2139/ssrn.6878994", "preprint_url": "https://doi.org/10.2139/ssrn.6878994", "items_available": false, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "No reliability coefficients and no validity evidence beyond content validity and CFA fit are reported.", "verified": "2026-09-29" }, { "id": "genai-literacy-yan", "name": "Generative AI Literacy Scale (awareness–usage–evaluation–ethics)", "acronym": null, "summary": "A general-public GenAI literacy scale with four parts: awareness of GenAI, using it (including prompting), evaluating its outputs, and understanding its ethical, legal and bias risks. Use it in media or communication research linking GenAI literacy to privacy protection and fact-checking behaviour.", "target": "Generative AI", "constructs": [ "literacy", "privacy", "credibility" ], "populations": [ "general adults" ], "items": null, "response": null, "subscales": [ { "name": "Awareness", "items": null, "description": "Awareness of GenAI and how it works" }, { "name": "Usage", "items": null, "description": "Using GenAI, including writing effective prompts" }, { "name": "Evaluation", "items": null, "description": "Evaluating GenAI outputs, including misinformation" }, { "name": "Ethics/risks", "items": null, "description": "Legal, bias and ethical risks" } ], "psychometrics": { "structure": "Four dimensions from mixed methods (focus groups n = 47, then a nationwide survey n = 535); factor-analytic details not seen", "reliability": "Not seen (paywalled)", "validity": [ "Nomological: awareness and ethics/risks predicted privacy-protection and information-verification behaviours (SEM)", "Frequent AI users showed less information verification" ], "samples": [ { "n": 535, "country": "unknown (nationwide survey)", "population": "survey respondents" } ] }, "status": "published", "citation": "Yan, W., Liu, Y., Mamaeva, V., Dong, F., Tao, G., Li, R., & Yang, H. (2026). Generative AI literacy: Scale development and its influence on privacy protection behaviors and information verification behaviors. Telecommunications Policy, 50(2), Article 103117. https://doi.org/10.1016/j.telpol.2025.103117", "authors": "Wenjia Yan; Yu-li Liu; Valeriia Mamaeva; Fang Dong; Guannan Tao; Rubing Li; Heng Yang", "year": 2026, "venue": "Telecommunications Policy", "doi": "10.1016/j.telpol.2025.103117", "url": "https://www.sciencedirect.com/science/article/pii/S0308596125002149", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "Predicts privacy and verification behaviour (nomological validity), but factor analysis and reliability could not be confirmed.", "verified": "2026-09-29" }, { "id": "genait", "name": "Generative AI Literacy Test", "acronym": "GenAIT", "summary": "An 18-item multiple-choice test of high-school students' conceptual understanding of GenAI, covering technical, practical and human-impact topics. Best used for group-level research rather than individual high-stakes decisions.", "target": "Generative AI / LLMs", "constructs": [ "literacy" ], "populations": [ "school students", "university students" ], "items": 18, "response": "Multiple choice (4 options, one correct)", "subscales": [ { "name": "(unidimensional)", "items": 18, "description": "Conceptual GenAI knowledge across technical, practical and human-impact content domains" } ], "psychometrics": { "structure": "Approximately unidimensional (CFA). IRT 3PL fit best: RMSEA .013, CFI .990, TLI .987, SRMSR .021", "reliability": "Marginal reliability .72; KR-20 .69", "validity": [ "Content validity through expert relevance ratings", "Scores unrelated to perceived usefulness and ease of use of AI (discriminant)", "Scores negatively related to LLM use frequency", "DIF not tested (no demographic data)" ], "samples": [ { "n": 7432, "country": "Estonia", "population": "high school students" } ] }, "status": "preprint", "citation": "Puppart, B., Laak, K.-J., & Aru, J. (2026). GenAIT: Development and validation of an objective generative AI literacy test for high school students [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2608.25815", "authors": "Brett Puppart; Kristjan-Julius Laak; Jaan Aru", "year": 2026, "venue": "arXiv", "doi": "10.48550/arXiv.2608.25815", "url": "https://arxiv.org/abs/2608.25815", "preprint_url": "https://arxiv.org/abs/2608.25815", "items_available": true, "language": "Estonian", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-overreliance-scale", "name": "Generative AI Overreliance Scale", "acronym": null, "summary": "A 10-item, single-score measure of university students' overreliance on generative AI tools, with evidence that it links critical thinking and creativity. Use it when you want a short overreliance score rather than an addiction score.", "target": "Generative AI tools", "constructs": [ "reliance", "dependence", "creativity" ], "populations": [ "university students" ], "items": 10, "response": null, "subscales": [ { "name": "(unidimensional)", "items": 10, "description": "Excessive reliance on GenAI tools in university work." } ], "psychometrics": { "structure": "Single factor (53.965% variance); EFA and CFA across multiple student samples; item-total correlations and item discrimination", "reliability": "Cronbach's α, McDonald's ω and a stability (test-retest) analysis reported (values not in the abstract)", "validity": [ "Convergent validity assessed", "Nomological: overreliance on GenAI played a role (mediation) in the critical thinking–creativity relationship" ], "samples": [ { "n": null, "country": "Not stated (author affiliated with Amasya University, Turkey)", "population": "multiple university student samples" } ] }, "status": "preprint", "citation": "Gümüş, M. M. (2026). Developing a scale for generative AI overreliance and investigating its associations with critical thinking and creativity [Preprint]. SSRN. https://doi.org/10.2139/ssrn.6891552", "authors": "Muhammed Murat Gümüş", "year": 2026, "venue": "SSRN", "doi": "10.2139/ssrn.6891552", "url": "https://doi.org/10.2139/ssrn.6891552", "preprint_url": "https://doi.org/10.2139/ssrn.6891552", "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-rts", "name": "Generative AI Reliance Types Scale", "acronym": "GenAI-RTS", "summary": "A 20-item scale of how students rely on generative AI when writing: strategic (planned use and checking of outputs), instrumental, dependent, and dialogic reliance. Use it to tell productive reliance on GenAI apart from over-reliance in writing courses.", "target": "Generative AI tools used for academic writing (e.g., ChatGPT)", "constructs": [ "reliance", "learning", "academic-integrity" ], "populations": [ "university students" ], "items": 20, "response": "7-point Likert (1 = strongly disagree to 7 = strongly agree); authors recommend a 5-point format for the next version", "subscales": [ { "name": "Strategic – Deliberate Use", "items": 4, "description": "Planned, purposeful use of GenAI guided by writing goals." }, { "name": "Strategic – Critical Evaluation", "items": 4, "description": "Checking facts, logic and bias in GenAI text and revising it extensively." }, { "name": "Instrumental", "items": 4, "description": "Using GenAI as an efficiency tool for specific subtasks (outlines, rephrasing, summaries, prompts)." }, { "name": "Dependent", "items": 4, "description": "Accepting GenAI output by default with little revision or questioning, and feeling uneasy without it." }, { "name": "Dialogic", "items": 4, "description": "Iterative, conversational co-writing and brainstorming with GenAI." } ], "psychometrics": { "structure": "CFA of six competing models (n=375). Five-factor model with Strategic split into two facets: ML χ²/df=3.35, CFI=.916, TLI=.900, RMSEA=.079, SRMR=.088; DWLS CFI=.976, TLI=.972. Rasch rating-scale analysis showed disordered thresholds in the 7-point format and recommended a 5-point format", "reliability": "ω/α .85–.88 for four facets; Deliberate Use .75; CR > .70 for all facets", "validity": [ "Scalar measurement invariance across gender, first-generation status and STEM/non-STEM major", "Strategic reliance correlated with AI literacy (composite r = .61; core r = .51; prior exposure r = .55)", "Reliance types differed across writing processes and outcomes; response-process evidence from 14 interviews" ], "samples": [ { "n": 382, "country": "United States", "population": "Undergraduates at a Minority-Serving Institution" }, { "n": 14, "country": "United States", "population": "Interview participants" } ] }, "status": "preprint", "citation": "Hossain, S., & Nawmi, T. A. (2026). Measuring how students rely on generative AI in academic writing: Development and multi-source validation of the Generative AI Reliance Types Scale (GenAI-RTS) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.14301", "authors": "Shahin Hossain, Tukhbita Afroz Nawmi", "year": 2026, "venue": "arXiv", "doi": "10.48550/arXiv.2607.14301", "url": "https://arxiv.org/abs/2607.14301", "preprint_url": "https://arxiv.org/abs/2607.14301", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gai-ses", "name": "Generative AI Self-Efficacy Scale", "acronym": "GAI-SES", "summary": "Measures how confident people feel using generative AI for practical tasks (prompting, translating, writing, problem-solving, learning new features) and for social or emotional needs (chatting to feel connected, seeking support, easing loneliness). Use it when you need self-efficacy specific to GenAI rather than a general AI or general self-efficacy measure.", "target": "Generative AI (ChatGPT, DALL-E, Midjourney, Copilot, Gemini, etc.)", "constructs": [ "self-efficacy", "relationships" ], "populations": [ "general adults" ], "items": 11, "response": "4-point confidence scale (1 = not at all confident to 4 = very confident); stem asks about confidence 'within the next week'", "subscales": [ { "name": "GAI-assisted task self-efficacy", "items": null, "description": "Confidence using GenAI for information, translation, problem-solving, writing, work/study, daily tasks and learning new features" }, { "name": "GAI-assisted social self-efficacy", "items": null, "description": "Confidence using GenAI to meet social needs, get emotional support and relieve loneliness" } ], "psychometrics": { "structure": "Two correlated factors (task and social self-efficacy) from EFA, then CFA", "reliability": "Good internal consistency and test–retest reliability reported (numeric values not seen; paywalled)", "validity": [ "Convergent: moderate correlation with the AI Self-Efficacy Scale (AISES)", "Discriminant: weak correlation with the General Self-Efficacy Scale (GSES)", "Criterion/nomological: positive association with psychological well-being (PWB-18)", "No significant gender difference in GAI self-efficacy" ], "samples": [ { "n": 933, "country": "Taiwan", "population": "participants in Taiwan (composition not stated in the abstract)" } ] }, "status": "published", "citation": "Yu, S.-C., Liu, A.-C., & Sheu, H.-B. (2026). Development and validation of the Generative AI Self-Efficacy Scale (GAI-SES): Psychometric properties, gender differences, and well-being associations. AI & Society, 41(6), 6077–6087. https://doi.org/10.1007/s00146-026-02895-0", "authors": "Sen-Chi Yu; An-Chia Liu; Hung-Bin Sheu", "year": 2026, "venue": "AI & Society", "doi": "10.1007/s00146-026-02895-0", "url": "https://link.springer.com/article/10.1007/s00146-026-02895-0", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gai-travel-experience-scale", "name": "Generative AI-Mediated Travel Experience Scale", "acronym": null, "summary": "Measures how travellers experience trip planning and travel when they use generative AI: whether it feels hyper-personalized, effortless, broader in options and more engaging. Use it in tourism and consumer research on GenAI travel assistants.", "target": "Generative AI tools used for travel (e.g., ChatGPT-type travel planning)", "constructs": [ "acceptance-use", "other" ], "populations": [], "items": null, "response": null, "subscales": [ { "name": "Hyper-personalized experience", "items": null, "description": "Experiences tailored to the traveller's autonomously expressed preferences." }, { "name": "Effective experience", "items": null, "description": "More confidence and less cognitive effort in planning and decisions." }, { "name": "Expanded experience", "items": null, "description": "Broader access to information and travel possibilities." }, { "name": "Deep experience", "items": null, "description": "Rich engagement with, and co-creation of, travel experiences with GenAI." } ], "psychometrics": { "structure": "Multidimensional (four experiential dimensions) from a two-phase mixed-method design: qualitative interviews, then large-scale surveys. Factor-analytic details not verified.", "reliability": "Not verified (full text inaccessible; abstract gives no values)", "validity": [ "Nomological validity: GAI-enabled travel experiences increase satisfaction, which in turn increases reuse intention, loyalty and word-of-mouth", "Multi-country samples (South Korea, China, United States)" ], "samples": [ { "n": null, "country": "South Korea, China, United States", "population": "Travellers using generative AI" } ] }, "status": "published", "citation": "Shin, H., Xie, R., Yoon, H., & Lee, J. (2026). Measuring generative AI-mediated travel experiences: Development and validation of a multidimensional scale. Tourism Management Perspectives, 63, 101492. https://doi.org/10.1016/j.tmp.2026.101492", "authors": "Hakseung Shin, Rongxue Xie, Heewon Yoon, Junghee Lee", "year": 2026, "venue": "Tourism Management Perspectives", "doi": "10.1016/j.tmp.2026.101492", "url": "https://doi.org/10.1016/j.tmp.2026.101492", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "Four dimensions and nomological validity across three countries are reported, but the factor analyses, reliability and item counts could not be confirmed.", "verified": "2026-09-29" }, { "id": "genai-srl-l2-writing", "name": "Generative AI-Supported Self-Regulated Learning in L2 Writing Scale", "acronym": "GenAI-SRL", "summary": "Measures how learners of English as a foreign language manage their own writing process when using generative AI tools such as ChatGPT: thinking strategies, planning and monitoring, motivation, emotions, getting help from others, and managing their environment. Has a 34-item full form and a 12-item short form (GenAI-SRL-12) for large surveys.", "target": "generative AI (e.g., ChatGPT) in L2 writing", "constructs": [ "learning", "other" ], "populations": [ "graduate students", "university students" ], "items": 34, "response": "7-point scale", "subscales": [ { "name": "Cognitive regulation", "items": 5, "description": "Using GenAI to polish language, analyse errors and revise" }, { "name": "Metacognitive regulation", "items": 8, "description": "Using GenAI to set goals, monitor progress and reflect on writing" }, { "name": "Motivational regulation", "items": 5, "description": "Using GenAI feedback to sustain confidence and persistence" }, { "name": "Affective regulation", "items": 7, "description": "Using GenAI to cope with writing anxiety, frustration and stress" }, { "name": "Social-behavioral regulation", "items": 3, "description": "Combining GenAI with teacher and peer feedback and help-seeking" }, { "name": "Environmental regulation", "items": 6, "description": "Managing resources, focus, accuracy checks and integrity when using GenAI" } ], "psychometrics": { "structure": "EFA (n = 305; PAF, varimax): 6 factors, 34 items, 60.74% of variance. CFA (n = 342): χ²/df 1.970, CFI .934, TLI .928, RMSEA .053, SRMR .042. ACO 12-item short form (2 items per factor): CFI .959, RMSEA .068.", "reliability": "α .761–.943; CR .759–.943 (full form). Short form α .657–.849, CR .710–.854.", "validity": [ "Convergent validity (AVE .513–.675)", "Discriminant validity (HTMT < .85)", "Criterion validity: all dimensions correlate with perceived writing improvement (r .275–.338)", "Metric and scalar invariance across gender", "Content validity (I-CVI/S-CVI with 3 experts; pilot n = 50)", "Short form correlates r .910–.944 with full-form dimensions" ], "samples": [ { "n": 305, "country": "China", "population": "EFL undergraduate and postgraduate students with GenAI writing experience (EFA)" }, { "n": 342, "country": "China", "population": "EFL undergraduate and postgraduate students (CFA, validity, invariance)" } ] }, "status": "published", "citation": "Wang, X., Zhang, L. J., & Zhang, Y. (2026). Generative artificial intelligence-supported self-regulated learning (GenAI-SRL) in L2 writing: Scale development, validation, and short-form construction. Metacognition and Learning, 21(1), Article 33. https://doi.org/10.1007/s11409-026-09481-1", "authors": "Xiaoqi Wang; Lawrence Jun Zhang; Yanan Zhang", "year": 2026, "venue": "Metacognition and Learning", "doi": "10.1007/s11409-026-09481-1", "url": "https://doi.org/10.1007/s11409-026-09481-1", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-crt", "name": "Generative AI–Critical Thinking Scale (short form)", "acronym": "GenAI–CrT", "summary": "An 8-item short scale of how critically EFL learners think when working with AI-generated content: analysing it, reasoning about it, evaluating evidence and staying open-minded. Use it when you need a brief critical-thinking measure for GenAI-supported learning.", "target": "generative AI in EFL learning", "constructs": [ "learning", "literacy" ], "populations": [ "graduate students", "university students" ], "items": 8, "response": null, "subscales": [ { "name": "Analytical skills", "items": null, "description": "Analysing AI-generated output" }, { "name": "Logical reasoning", "items": null, "description": "Reasoning logically about AI output" }, { "name": "Evidence evaluation", "items": null, "description": "Evaluating the evidence behind AI output" }, { "name": "Open-mindedness", "items": null, "description": "Openness to alternative views and methods" } ], "psychometrics": { "structure": "EFA and CFA: 4 factors; CFA χ²(14) = 23.49, p = .053, CFI .990, TLI .979, RMSEA .054, SRMR .023", "reliability": "α .90", "validity": [ "Criterion/nomological: explained ~7% of variance in AI self-efficacy (n=189)" ], "samples": [ { "n": 233, "country": "Vietnam", "population": "EFL undergraduate and graduate students" } ] }, "status": "published", "citation": "Cong-Lem, N., Nguyen, T. T., Nguyen, K. N. H., & Nguyen, Q. N. H. (2026). Development and psychometric validation of a short-form critical thinking scale in generative AI contexts (GenAI–CrT): Evidence from Vietnamese EFL learners. Journal of Educational Technology Development and Exchange, 19(1), 1–24. https://doi.org/10.18785/jetde.1901.01", "authors": "Ngo Cong-Lem; Thang T. Nguyen; Khanh N. H. Nguyen; Quyen N. H. Nguyen", "year": 2026, "venue": "Journal of Educational Technology Development and Exchange", "doi": "10.18785/jetde.1901.01", "url": "https://doi.org/10.18785/jetde.1901.01", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gai-as", "name": "Generative Artificial Intelligence Addiction Scale", "acronym": "GAI-AS", "summary": "An 18-item scale of addictive patterns in university students' generative AI use: salience, excessive dependency, mood modification and functional impairment. Use it with undergraduate samples when you want an addiction-component profile of GenAI use.", "target": "Generative AI tools (items refer to 'AI tools')", "constructs": [ "dependence" ], "populations": [ "university students" ], "items": 18, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Salience", "items": 5, "description": "AI tools dominating thoughts and preferences; life feels empty without them." }, { "name": "Excessive dependency", "items": 4, "description": "Relying on AI even when able to work alone and spending increasing time on it." }, { "name": "Mood modification", "items": 4, "description": "Using AI tools to regulate mood." }, { "name": "Functional impairment", "items": 5, "description": "Negative effects on daily functioning and responsibilities." } ], "psychometrics": { "structure": "Four factors; EFA (n=429, 64.32% variance) then CFA (n=490; χ²/df 2.66, CFI .98, GFI .93, RMSEA .058)", "reliability": "α .90 total (subscales .81–.88); ω .91 total (subscales .83–.89); CR .884–.923", "validity": [ "Convergent: AVE .605–.753 and CR above thresholds", "Discriminant: Fornell-Larcker criterion satisfied", "Known-groups: daily GenAI users scored higher on all dimensions (implementation sample N=803); first-year students lower on excessive dependency" ], "samples": [ { "n": 429, "country": "Turkey", "population": "undergraduates (EFA)" }, { "n": 490, "country": "Turkey", "population": "undergraduates (CFA)" }, { "n": 803, "country": "Turkey", "population": "undergraduates (implementation)" } ] }, "status": "published", "citation": "Isbulan, O., Ergene, O., & Demirhan, E. (2026). Is generative artificial intelligence becoming addictive? An exploration among undergraduate students. Frontiers in Psychology, 17, 1887507. https://doi.org/10.3389/fpsyg.2026.1887507", "authors": "Onur Isbulan; Ozkan Ergene; Eda Demirhan", "year": 2026, "venue": "Frontiers in Psychology", "doi": "10.3389/fpsyg.2026.1887507", "url": "https://doi.org/10.3389/fpsyg.2026.1887507", "preprint_url": null, "items_available": true, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gaia-scale", "name": "Generative Artificial Intelligence Appropriation Scale", "acronym": "GAIA", "summary": "Measures how employees take up generative AI tools at work: adopting them, adapting them to their needs, building them into daily routines, handling the interface and using them ethically. Use it in workplace or business research on how people actually integrate GenAI into their jobs.", "target": "Generative AI tools (workplace)", "constructs": [ "acceptance-use", "workplace" ], "populations": [ "employees & professionals" ], "items": null, "response": null, "subscales": [ { "name": "Integrative appropriation", "items": null, "description": "Building GenAI tools into existing work routines and processes" }, { "name": "Adoptive appropriation", "items": null, "description": "Taking up and starting to use GenAI tools for work tasks" }, { "name": "Customised appropriation", "items": null, "description": "Adapting and tailoring GenAI tools to one's own needs" }, { "name": "Interface appropriation", "items": null, "description": "Engaging with and mastering the GenAI tool interface" }, { "name": "Ethical appropriation", "items": null, "description": "Using GenAI tools in an ethically responsible way" } ], "psychometrics": { "structure": "Second-order reflective-reflective model with five first-order dimensions, developed across four studies (item generation, scale purification, scale refinement, nomological validation)", "reliability": "Not reported in abstract", "validity": [ "Nomological validation study (Study 4)", "Mixed-method item generation with qualitative input" ], "samples": [ { "n": null, "country": "not stated in abstract", "population": "GenAI users in the workplace (multiple samples)" } ] }, "status": "published", "citation": "Khatri, P., Duggal, H. K., Thomas, A., Corvello, V., Prałat, E., & Shiva, A. (2026). Development and validation of the generative artificial intelligence appropriation (GAIA) Scale: A comprehensive measurement tool for assessing user engagement and utilisation. Technovation, 150, 103379. https://doi.org/10.1016/j.technovation.2025.103379", "authors": "Puja Khatri; Harshleen Kaur Duggal; Asha Thomas; Vincenzo Corvello; Ewa Prałat; Atul Shiva", "year": 2026, "venue": "Technovation", "doi": "10.1016/j.technovation.2025.103379", "url": "https://doi.org/10.1016/j.technovation.2025.103379", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "Second-order structure and nomological validity are reported, but reliability values and item counts could not be confirmed.", "verified": "2026-09-29" }, { "id": "genai-concept-test", "name": "Generative Artificial Intelligence Concept Test", "acronym": null, "summary": "A multiple-choice knowledge test of generative AI concepts for teenagers and adults, calibrated with item response theory. Use it to measure what learners know about GenAI before and after a GenAI literacy course.", "target": "generative AI concepts (knowledge)", "constructs": [ "literacy", "learning" ], "populations": [ "school students", "teachers & academics", "employees & professionals" ], "items": null, "response": "knowledge test items (format not confirmed; guessing parameter implies selected-response)", "subscales": [ { "name": "(unidimensional)", "items": null, "description": "Understanding of generative AI concepts" } ], "psychometrics": { "structure": "IRT: 3PL model fit best vs other IRT models; difficulty, discrimination and pseudo-guessing parameters examined", "reliability": "Not confirmed (abstract only; reference list cites conditional reliability and Cronbach 1951)", "validity": [ "Sensitivity to instruction: significant pre–post gains after a 30-hour GenAI literacy course (larger for adults)" ], "samples": [ { "n": 635, "country": "Hong Kong", "population": "355 adolescents (<18) and 280 adults" } ] }, "status": "published", "citation": "Kong, S. C., & Hou, C. (2026). Validation of a generative artificial intelligence learning progression instrument using item response theory: Unleashing the potential of adult learners. Technology, Knowledge and Learning. Advance online publication. https://doi.org/10.1007/s10758-026-10003-w", "authors": "Siu Cheung Kong; Chunyu Hou", "year": 2026, "venue": "Technology, Knowledge and Learning", "doi": "10.1007/s10758-026-10003-w", "url": "https://doi.org/10.1007/s10758-026-10003-w", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "A GenAI knowledge test validated with IRT and sensitivity to instruction; reliability and item count could not be confirmed.", "verified": "2026-09-29" }, { "id": "genai-ears", "name": "Generative Artificial Intelligence Ethical Awareness and Responsibility Scale", "acronym": "GenAI-EARS", "summary": "An 18-item scale of how aware and responsible people are about the ethics of using generative AI in education, covering autonomy, transparency, privacy and fairness. Use it to evaluate ethics education or compare groups in educational settings.", "target": "Generative AI in educational contexts", "constructs": [ "ethics-concerns", "privacy", "academic-integrity" ], "populations": [ "teachers & academics" ], "items": 18, "response": null, "subscales": [ { "name": "Autonomy", "items": null, "description": "Awareness of preserving human agency and autonomy when using GenAI (label only; item content not seen)" }, { "name": "Transparency", "items": null, "description": "Awareness of being transparent about GenAI use (label only; item content not seen)" }, { "name": "Privacy", "items": null, "description": "Awareness of data privacy when using GenAI (label only; item content not seen)" }, { "name": "Fairness", "items": null, "description": "Awareness of bias and fairness in GenAI use (label only; item content not seen)" } ], "psychometrics": { "structure": "Item generation, pilot, EFA and CFA supporting 18 items in 4 factors with adequate fit; item discrimination analyses", "reliability": "Reported as satisfactory; coefficients not seen", "validity": [ "Convergent and discriminant validity assessed", "Measurement invariance across gender", "Scores negatively related to age; no gender differences" ], "samples": [ { "n": null, "country": "not stated in abstract", "population": "not stated in abstract" } ] }, "status": "published", "citation": "Çınar Yağcı, Ş., Orhan, A., Aydın Yıldız, T., & Bozkurt, A. (2026). Generative artificial intelligence in education: Development and validation of a scale for ethical awareness and responsibility. Interactive Learning Environments, 34(7), 5095–5108. https://doi.org/10.1080/10494820.2026.2617482", "authors": "Şule Çınar Yağcı; Ali Orhan; Tuğba Aydın Yıldız; Aras Bozkurt", "year": 2026, "venue": "Interactive Learning Environments", "doi": "10.1080/10494820.2026.2617482", "url": "https://doi.org/10.1080/10494820.2026.2617482", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gails-genai-literacy", "name": "Generative Artificial Intelligence Literacy Scale", "acronym": "GAILS", "summary": "A 34-item self-report scale of adults' generative-AI literacy: operating and prompting GenAI tools, using them legally and ethically, and critically evaluating outputs while staying independent of the tools. Use it to compare GenAI skills across students and workers.", "target": "generative AI tools", "constructs": [ "literacy" ], "populations": [ "general adults", "university students", "employees & professionals" ], "items": 34, "response": "5-point Likert (1 = completely disagree to 5 = completely agree)", "subscales": [ { "name": "Adaptive Operational Skills", "items": 20, "description": "Knowing, prompting, customizing and choosing GenAI tools for tasks" }, { "name": "Responsible GenAI Literacy", "items": 9, "description": "Awareness of and compliance with legal and ethical issues in GenAI use" }, { "name": "Critical Evaluation & Autonomous Use", "items": 5, "description": "Judging accuracy and relevance of outputs and not over-relying on GenAI" } ], "psychometrics": { "structure": "EFA (n=171; KMO .959) 3 factors, 66.9% variance; CFA (n=170) CFI .967, TLI .965, SRMR .058, RMSEA .087; semantic embedding analysis of item content", "reliability": "α .973 (total); CR .945–.977", "validity": [ "Scalar measurement invariance across sex and student vs workforce", "Convergent correlations with GenAI acceptance (r = .81) and GenAI trust (r = .47)", "AVE .685–.774; Fornell–Larcker met for Factors 2 and 3 but not Factor 1 vs Factor 3" ], "samples": [ { "n": 341, "country": "North America (USA/Canada)", "population": "Prolific adults (38% students, 62% workforce; M age 39)" } ] }, "status": "preprint", "citation": "Zhang, Y., Qi, J., He, X., Feng, Z., & Ji, F. (2026). The Generative Artificial Intelligence Literacy Scale (GAILS): Development, validation, and measurement invariance across sex and occupational status groups [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/bg6pq_v2", "authors": "Yuchen Zhang; Jia Qi; Xinyi He; Zhe Feng; Feng Ji", "year": 2026, "venue": null, "doi": "10.31234/osf.io/bg6pq_v2", "url": "https://doi.org/10.31234/osf.io/bg6pq_v2", "preprint_url": "https://doi.org/10.31234/osf.io/bg6pq_v2", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genails", "name": "Generative Artificial Intelligence Literacy Scale for Nurses", "acronym": "GenAILS", "summary": "A GenAI literacy scale written for nurses. It covers responsible use in care, keeping skills current, spotting risks such as hallucinations, basic knowledge of how GenAI works, critically checking outputs, and ethics and law. Use it for needs assessment or to evaluate GenAI training in nursing.", "target": "Generative AI in clinical nursing", "constructs": [ "literacy", "health", "ethics-concerns" ], "populations": [ "health professionals" ], "items": 24, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Responsible use", "items": 5, "description": "Using GenAI responsibly in nursing work (e.g., educational materials)" }, { "name": "Updated competencies", "items": 4, "description": "Keeping GenAI skills current" }, { "name": "Risk identification", "items": 4, "description": "Spotting GenAI risks such as hallucinations" }, { "name": "Fundamental knowledge", "items": 4, "description": "Basic knowledge of how GenAI works" }, { "name": "Critical evaluation", "items": 4, "description": "Critically appraising GenAI outputs against clinical judgment" }, { "name": "Ethics and law", "items": 3, "description": "Ethical and legal accountability" } ], "psychometrics": { "structure": "Six factors (EFA on n = 561, 53.1% of variance). First-order CFA on n = 561: RMSEA .035, SRMR .032, CFI .99. Second-order CFA: RMSEA .039, CFI .99", "reliability": "Total α .92, ω .92; subscale α and ω .73–.85; split-half .81; second-order CR .91", "validity": [ "Content validity: S-CVI/Ave .92; cognitive interviews with 7 nurses", "Convergent: CR .70–.85; AVE .44–.53 (second-order AVE .63)", "Discriminant: HTMT .53–.83", "Criterion: r = .57 with the Short Form Meta AI Literacy Scale" ], "samples": [ { "n": 1122, "country": "Taiwan", "population": "registered nurses (nationwide online survey)" } ] }, "status": "published", "citation": "Chu, K.-L., Wang, C.-L., Chang, C.-M., Liang, J.-C., Chen, L.-Y. A., Liu, C.-Y., & Lin, C.-P. (2026). Generative artificial intelligence literacy scale for nurses: Development and psychometric evaluation. Journal of Medical Internet Research, 28, e95547. https://doi.org/10.2196/95547", "authors": "Kuan-Lin Chu; Ching-Ling Wang; Che-Ming Chang; Jyh-Chong Liang; Lu-Yen Anny Chen; Chieh-Yu Liu; Cheng-Pei Lin", "year": 2026, "venue": "Journal of Medical Internet Research", "doi": "10.2196/95547", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13386122/", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gail-pt", "name": "Generative Artificial Intelligence Literacy Scale for Preservice Teachers", "acronym": "GAIL-PT", "summary": "Measures pre-service teachers' generative AI literacy in two parts: applying GenAI in practice and in instruction, and using it responsibly and reflectively. Use it in teacher-education programs to gauge readiness to teach with GenAI.", "target": "generative AI in teacher education", "constructs": [ "literacy", "ethics-concerns" ], "populations": [ "teachers & academics" ], "items": null, "response": null, "subscales": [ { "name": "Applied Engagement and Instructional Integration", "items": null, "description": "Practical and pedagogical use of GenAI" }, { "name": "Responsible and Reflective Use", "items": null, "description": "Ethical, critical and reflective GenAI use" } ], "psychometrics": { "structure": "42-item pool across 6 conceptual domains (conceptual understanding, use and application, evaluation and verification, ethics and responsibility, pedagogical integration, affective readiness). EFA (ML, promax) gave 2 factors explaining nearly half of the variance; CFA showed acceptable fit.", "reliability": "'Acceptable reliability' per abstract; values not seen", "validity": [ "Content validity (CVI, expert panel)", "Factorial validity via CFA" ], "samples": [ { "n": 402, "country": "Philippines", "population": "pre-service teachers, Bukidnon State University" } ] }, "status": "published", "citation": "Pasco, J. C. (2026). Psychometric properties of Generative AI Literacy Scale for Preservice Teachers: Evidence from a scoping review and factor analytic validation. International Journal of Technology in Education, 9(3), 914–939. https://doi.org/10.46328/ijte.6082", "authors": "Joseph C. Pasco", "year": 2026, "venue": "International Journal of Technology in Education", "doi": "10.46328/ijte.6082", "url": "https://doi.org/10.46328/ijte.6082", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "Only content validity (CVI) and factorial validity are reported; no convergent, discriminant or criterion evidence.", "verified": "2026-09-29" }, { "id": "grad-genai-literacy-scale", "name": "Graduate Students' Generative AI Literacy Scale", "acronym": null, "summary": "A short 15-item scale of how well graduate students think they can use generative AI in research. Based on Marzano's taxonomy, it covers background knowledge, operational skills such as prompting, higher-order thinking, metacognitive reflection, and ethical responsibility. Use it for quick assessments in graduate-education research.", "target": "Generative AI in graduate research", "constructs": [ "literacy", "academic-integrity" ], "populations": [ "graduate students", "university students" ], "items": 15, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Cognitive foundation", "items": 3, "description": "Technical principles, tool knowledge and use scenarios" }, { "name": "Operational skills", "items": 3, "description": "Prompt design, multi-turn dialogue and output processing" }, { "name": "Higher-order thinking", "items": 3, "description": "Critical verification, hypothesis generation and human–AI collaboration" }, { "name": "Metacognitive reflection", "items": 3, "description": "Monitoring use, evaluating outcomes and transferring strategies" }, { "name": "Ethical responsibility", "items": 3, "description": "Academic integrity, social responsibility and attribution" } ], "psychometrics": { "structure": "Five factors (EFA n = 154; CFA n = 154), better fit than 1–4-factor models: CFI = .934, TLI = .919, SRMR = .048, RMSEA = .083 (marginal)", "reliability": "α .796–.842; ω .831–.848; CR .752–.786", "validity": [ "Content validity: I-CVI .87–1.00 (3 experts)", "Convergent: AVE .501–.551", "Discriminant: Fornell–Larcker met; HTMT .402–.726" ], "samples": [ { "n": 308, "country": "China", "population": "full-time graduate students from nine universities (Shanxi and Hubei)" } ] }, "status": "published", "citation": "Zhi, Y., Yang, W., & Huang, K. (2026). Modeling and measuring graduate students' generative AI literacy: A study based on Marzano's taxonomy. Frontiers in Psychology, 17, Article 1883978. https://doi.org/10.3389/fpsyg.2026.1883978", "authors": "Yaozheng Zhi; Wei Yang; Kaili Huang", "year": 2026, "venue": "Frontiers in Psychology", "doi": "10.3389/fpsyg.2026.1883978", "url": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1883978/full", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "hallucination-awareness-hcp", "name": "Hallucination Awareness Scale for Healthcare Professionals", "acronym": null, "summary": "Measures how aware healthcare practitioners are of generative AI hallucination risks: poor input data, documentation errors, biased outputs, overgeneralized patterns and distorted interpretations. It comes with a short diagnostic-confidence measure. Useful for studying safe clinical use of LLM tools.", "target": "Generative AI / LLM tools used in diagnosis and clinical decision-making", "constructs": [ "literacy", "health", "reliance" ], "populations": [ "health professionals", "employees & professionals" ], "items": 21, "response": "Likert scale (number of points not confirmed)", "subscales": [ { "name": "Awareness of data quality risks", "items": 4, "description": "Recognizing errors arising from poor input data (extrinsic hallucination)" }, { "name": "Awareness of documentation errors", "items": 3, "description": "Recognizing errors in AI-generated documentation (extrinsic hallucination)" }, { "name": "Awareness of biases", "items": 4, "description": "Recognizing biased or faulty outputs (intrinsic hallucination)" }, { "name": "Awareness of pattern overgeneralizations", "items": 3, "description": "Recognizing overgeneralized patterns (intrinsic hallucination)" }, { "name": "Perceptual distortion", "items": 4, "description": "Recognizing distorted interpretations (intrinsic hallucination)" }, { "name": "Diagnostic confidence (outcome)", "items": 3, "description": "Clinician's diagnostic confidence (separate outcome construct, adapted from Ng & Palmer)" } ], "psychometrics": { "structure": "Qualitative interviews and expert review, then a pilot (n = 35). Split sample: EFA (PCA, varimax, 6 components, 74.5% variance; KMO .899). 'CFA' done as a PLS-SEM measurement model (SmartPLS) with a higher-order extrinsic/intrinsic hallucination structure.", "reliability": "α .74–.88; ρA .74–.88; ρc .85–.92", "validity": [ "Convergent: AVE .65–.80", "Discriminant: Fornell–Larcker criterion", "Nomological: awareness of extrinsic hallucinations predicts diagnostic confidence (β = .22); intrinsic does not" ], "samples": [ { "n": 452, "country": "India (inferred from author affiliation and recruitment; not stated explicitly)", "population": "healthcare professionals using GenAI for diagnosis and clinical decisions" } ] }, "status": "published", "citation": "Tandon, U. (2026). Development and evaluation of a Hallucination Awareness Scale for healthcare professionals and its impact on diagnostic confidence. Frontiers in Digital Health, 8, 1772345. https://doi.org/10.3389/fdgth.2026.1772345", "authors": "Urvashi Tandon", "year": 2026, "venue": "Frontiers in Digital Health", "doi": "10.3389/fdgth.2026.1772345", "url": "https://doi.org/10.3389/fdgth.2026.1772345", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-ethical-risks-perception-scale", "name": "Higher Education Students' Perceptions of Generative AI Ethical Risks Scale", "acronym": null, "summary": "A 20-item scale of how higher-education students perceive the ethical risks of generative AI across four areas: subjective, algorithmic, relational and ecological risks. Use it for risk assessment or to inform GenAI policy in higher education.", "target": "Generative AI in higher education", "constructs": [ "ethics-concerns", "academic-integrity" ], "populations": [ "university students" ], "items": 20, "response": null, "subscales": [ { "name": "Subjective ethical risk", "items": null, "description": "Perceived ethical risks at the level of the student user (label only; item content not seen)" }, { "name": "Algorithmic ethical risk", "items": null, "description": "Perceived ethical risks arising from GenAI algorithms (label only; item content not seen)" }, { "name": "Relational ethical risk", "items": null, "description": "Perceived ethical risks to relationships in education (label only; item content not seen)" }, { "name": "Ecological ethical risk", "items": null, "description": "Perceived broader ecosystem/societal ethical risks (label only; item content not seen)" } ], "psychometrics": { "structure": "EFA (n = 290) then CFA (n = 676) with competing models; 4 first-order factors with a supported second-order factor", "reliability": "α .934 (total)", "validity": [ "Convergent, discriminant and criterion validity reported in the abstract (details not seen)" ], "samples": [ { "n": 290, "country": "not stated in abstract (authors based in China)", "population": "Higher education students (EFA sample)" }, { "n": 676, "country": "not stated in abstract (authors based in China)", "population": "Higher education students (CFA sample)" } ] }, "status": "published", "citation": "Wang, J., Wang, L., Zhang, M., Dong, J., & Xiong, J. (2026). Development and validation of a measurement scale for higher education students' perceptions of generative artificial intelligence ethical risks. Education and Information Technologies, 31(14), 6207–6226. https://doi.org/10.1007/s10639-026-14044-7", "authors": "Jing Wang; Limin Wang; Mengping Zhang; Junjing Dong; Jingjing Xiong", "year": 2026, "venue": "Education and Information Technologies", "doi": "10.1007/s10639-026-14044-7", "url": "https://doi.org/10.1007/s10639-026-14044-7", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "hacd", "name": "Human-AI Collaboration Dynamics Scale", "acronym": "HACD", "summary": "A flexible set of scale versions, from a core 2-factor form to an expanded 5-factor form, measuring how academics and research students collaborate with generative AI in their research work.", "target": "Generative AI used in academic research", "constructs": [ "workplace", "acceptance-use" ], "populations": [ "university students", "employees & professionals", "health professionals", "graduate students" ], "items": null, "response": null, "subscales": [ { "name": "Four versions (core 2-factor up to expanded 5-factor)", "items": null, "description": "Dimensions of human–GenAI collaboration in research; factor names not given in the abstract" } ], "psychometrics": { "structure": "EFA (n = 344), then CFA in two independent subsamples (n = 344; n = 447); four versions from 2 to 5 factors", "reliability": "Described as excellent (values not in abstract)", "validity": [ "The abstract only says 'excellent psychometric properties'; specific validity evidence not confirmed" ], "samples": [ { "n": 1135, "country": "Mainland China, Hong Kong, Macau", "population": "staff and students at 16 English-medium-instruction universities" } ] }, "status": "published", "citation": "Mahy, T., & Li, H. (2026). GenAI meets psychometrics: Development and validation of the Human-AI Collaboration Dynamics Scale and the Generative AI-Research Augmentation Scale. International Journal of Human–Computer Interaction, 42(19), 16812–16858. https://doi.org/10.1080/10447318.2026.2623216", "authors": "Trevor Mahy; Hua Li", "year": 2026, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2026.2623216", "url": "https://doi.org/10.1080/10447318.2026.2623216", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "EFA/CFA across three subsamples; the abstract says only \"excellent psychometric properties\", so reliability and validity could not be checked.", "verified": "2026-09-29" }, { "id": "inpade", "name": "Intention to Pay and Dependence on Generative Artificial Intelligence Scale", "acronym": "INPADE", "summary": "A 10-item Spanish-language scale for university students that measures (1) willingness to pay for premium generative AI tools and (2) dependence on AI for academic work. Use it to study how GenAI subscription models relate to students' reliance on AI.", "target": "Generative AI tools (paid/premium versions of GenAI services)", "constructs": [ "dependence", "acceptance-use" ], "populations": [ "teachers & academics" ], "items": 10, "response": "5-point Likert (totally disagree to totally agree)", "subscales": [ { "name": "Intention to pay", "items": 4, "description": "Belief that paid GenAI versions are worth their cost and would help academic work." }, { "name": "Dependence on generative AI (two correlated factors: dependence on the tool; use of the tool)", "items": 6, "description": "Cognitive and operational reliance on AI for schoolwork (e.g., copy-pasting AI output, hard to be productive without AI)." } ], "psychometrics": { "structure": "EFA (PCA, varimax) on an 18-item pool, then ML CFA per subscale. Final 10 items: Intention to pay has 4 items on one factor (χ²(2)=14.31, CFI=.993, SRMR=.019, RMSEA=.076, AGFI=.966). Dependence has 6 items on two factors (χ²(8)=37.71, CFI=.991, SRMR=.020, RMSEA=.059, AGFI=.970).", "reliability": "α > .852 for final 10-item factors (abstract); 18-item pool α .892 (pay), .933 (dependence), .914 total", "validity": [ "Scalar measurement invariance across sex (ΔCFI = .001)", "SEM predictive evidence: intention to pay predicts GenAI dependence (β = .297, p < .001)" ], "samples": [ { "n": 1047, "country": "Mexico", "population": "Undergraduate Basic Education students, Universidad Pedagógica Veracruzana (78.6% female)" } ] }, "status": "published", "citation": "Torres-Gastelú, C. A., & Torres-Real, C. (2026). Intención de pago y dependencia hacia Inteligencia Artificial Generativa. Validación de escala y modelo estructural. Revista Panamericana de Pedagogía, 43, e3920. https://doi.org/10.21555/rpp.3920", "authors": "Carlos Arturo Torres-Gastelú, Carlos Torres-Real", "year": 2026, "venue": "Revista Panamericana de Pedagogía", "doi": "10.21555/rpp.3920", "url": "https://doi.org/10.21555/rpp.3920", "preprint_url": null, "items_available": true, "language": "Spanish", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "tam-genai-italian", "name": "Italian TAM-GenAI Scale", "acronym": "TAM-GenAI-IT", "summary": "An 18-item Italian version of a technology-acceptance scale on teachers' views of generative AI tools for teaching. It covers perceived usefulness, ease of use, attitude, intention, self-efficacy and social norms. Use it with Italian (pre-service) teachers, keeping in mind that some factors overlap strongly and the evidence is preliminary.", "target": "Generative AI tools in teaching", "constructs": [ "acceptance-use", "attitudes", "workplace" ], "populations": [ "teachers & academics" ], "items": 18, "response": "5-point Likert (1 = completely disagree to 5 = completely agree)", "subscales": [ { "name": "Perceived Usefulness (PU)", "items": 3, "description": "Belief that GenAI tools improve teaching performance and efficiency" }, { "name": "Perceived Ease of Use (PEU)", "items": 3, "description": "Belief that GenAI tools are easy to learn and use" }, { "name": "Attitude toward use (ATT)", "items": 3, "description": "Liking GenAI tools and seeing them as important for teaching quality" }, { "name": "Behavioural Intention (BI)", "items": 3, "description": "Intending and planning to use GenAI tools in the classroom" }, { "name": "Self-Efficacy (SE)", "items": 3, "description": "Confidence in being able to use GenAI tools for teaching" }, { "name": "Subjective Norms (SN)", "items": 3, "description": "Perceived pressure from important others and the media to use GenAI tools" } ], "psychometrics": { "structure": "Ordinal CFA of the six-factor model. Conventional fit CFI = .997, TLI = .996, RMSEA = .074, SRMR = .065; robust fit weaker (CFI = .875, TLI = .840, RMSEA = .142). The authors call the six-factor structure 'partially supported'", "reliability": "CR .747-.931; ω total .98; ω hierarchical .79 (ECV .55)", "validity": [ "Convergent validity: AVE .592-.938", "Discriminant validity problematic: HTMT > .85 for PU-ATT (.921) and ATT-SE (.927)", "Nomological: exploratory SEM and mediation (PU mediates SE→ATT; ATT mediates PU→BI)" ], "samples": [ { "n": 198, "country": "Italy", "population": "pre-service secondary teachers in university initial-training courses, Northern Italy" } ] }, "status": "published", "citation": "Giganti, M., Baroni, F., Barabanti, P., & Lazzari, M. (2026). Acceptance of generative artificial intelligence in education: Preliminary evidence on the Italian TAM-GenAI Scale. Form@re – Open Journal per la formazione in rete, 26(1), 249–266. https://doi.org/10.36253/form-19737", "authors": "Marco Giganti; Federica Baroni; Paolo Barabanti; Marco Lazzari", "year": 2026, "venue": "Form@re – Open Journal per la formazione in rete", "doi": "10.36253/form-19737", "url": "https://doi.org/10.36253/form-19737", "preprint_url": null, "items_available": true, "language": "Italian", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "kuai-kids", "name": "KUAI-Kids: Knowledge, Use, and Attitudes toward Generative AI Chatbots (children)", "acronym": "KUAI-Kids", "summary": "A child-friendly German questionnaire (roughly ages 8-14) that measures what children know about ChatGPT, how often they use it, and whether their attitudes are enthusiastic or cautious. Use it for school-age AI-literacy research or to evaluate classroom interventions about AI chatbots.", "target": "ChatGPT / generative AI chatbots", "constructs": [ "literacy", "acceptance-use", "attitudes" ], "populations": [ "school students" ], "items": 36, "response": "Knowledge: yes / no / I don't know (scored correct = 1); Use: never / rarely / often; Attitudes: 4-point Likert (strongly disagree to strongly agree), with visually graded check-boxes", "subscales": [ { "name": "Knowledge", "items": 9, "description": "True/false-style statements about how ChatGPT works, including common misconceptions" }, { "name": "Use", "items": 13, "description": "Frequency of past uses of ChatGPT (e.g., homework help, image generation)" }, { "name": "Enthusiastic Attitude", "items": 6, "description": "Positive, friendly views of ChatGPT (e.g., 'To me, ChatGPT is like a friend')" }, { "name": "Cautious Attitude", "items": 8, "description": "Critical or cautious views (privacy, misinformation, bias, preferring adult help); reverse-coded" } ], "psychometrics": { "structure": "CFA: unidimensional Knowledge (CFI 1.00, RMSEA .000) and Use (CFI .998, RMSEA .033). Attitudes one-factor model rejected (CFI .833); EFA plus CFA two-factor model (CFI .968, RMSEA .061) with 1 item dropped. Four-factor model CFI .972, RMSEA .051.", "reliability": "Knowledge α .73, ω .80; Use α .96, ω .97; Enthusiastic α .86, ω .92; Cautious α .81, ω .85", "validity": [ "Hypothesised latent correlations: Knowledge–Use r = .33; Enthusiastic–Use r = .74; Knowledge–Cautious r = −.58", "Group differences: boys higher on Enthusiastic Attitude; secondary students higher Knowledge and Use and more cautious", "Content adaptation with a pre-test (N = 20)" ], "samples": [ { "n": 310, "country": "Germany", "population": "children aged 7–14 (M = 9.93) from a Children's University event and schools; n = 295 for Use and 222 for Attitudes after missing-data exclusions" } ] }, "status": "preprint", "citation": "Malone, S., Koehler, C., Altmeyer, K., Thüs, D., Dombrovskaia, M., & Hartig, J. (2026). KUAI-Kids: Developing a measurement instrument for children's knowledge, use, and attitudes toward generative AI chatbots [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/tqgcm_v3", "authors": "Sarah Malone; Carmen Koehler; Kristin Altmeyer; Dominik Thüs; Marina Dombrovskaia; Johannes Hartig", "year": 2026, "venue": "PsyArXiv", "doi": "10.31234/osf.io/tqgcm_v3", "url": "https://doi.org/10.31234/osf.io/tqgcm_v3", "preprint_url": "https://osf.io/preprints/psyarxiv/tqgcm_v3/", "items_available": true, "language": "German", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "pc-aicbs-kas", "name": "Knowledge and Attitude Scale toward AI-Based Chatbot Systems in Preconception Counseling", "acronym": "PC-AICBS-KAS", "summary": "A 19-item questionnaire on women's knowledge of and attitudes toward using AI chatbots for preconception (pre-pregnancy) health advice. It covers willingness to use them, knowledge of what they can offer, and awareness that their advice needs checking. Use it in digital-health or midwifery research on AI chatbots.", "target": "AI-based chatbot systems for preconception health counseling (item wording refers to 'artificial intelligence')", "constructs": [ "acceptance-use", "trust", "health" ], "populations": [ "patients & health consumers" ], "items": 19, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree); no reverse-coded items; total 19-95", "subscales": [ { "name": "Acceptance and Intention to Use", "items": 9, "description": "Positive attitude toward and willingness to use AI for preconception advice, and seeing it as a supportive guide" }, { "name": "Knowledge and Awareness", "items": 7, "description": "Knowing that AI can give preconception information on lifestyle, exercise, nutrition, weight, harmful habits, medication and environmental risks" }, { "name": "Critical Evaluation and Trust", "items": 3, "description": "Recognising that AI information does not replace health professionals and should be verified before making decisions" } ], "psychometrics": { "structure": "30-item draft cut to 23 after expert review. EFA (PCA, varimax; n = 237) gave 3 factors and 19 items, explaining 63.16% of variance. First-order CFA (ML, AMOS; independent n = 237) with correlated errors: χ²/df = 2.668, RMSEA = .084, CFI = .928, TLI = .914, GFI = .855, IFI = .928 (marginal fit)", "reliability": "α total .942 (subscales .925, .925, .770); ω total .946 (subscales .926, .928, .801)", "validity": [ "Content validity: 13 experts, Lawshe CVR", "Criterion validity: r = .265 with the Digital Health Literacy Scale total; subscale r = .08 to .21" ], "samples": [ { "n": 237, "country": "Türkiye (inferred from author affiliation; not stated explicitly)", "population": "women aged 18+, EFA subsample (online, social-media recruitment, Feb-Apr 2026)" }, { "n": 237, "country": "Türkiye (inferred from author affiliation; not stated explicitly)", "population": "women aged 18+, CFA subsample" } ] }, "status": "preprint", "citation": "Ekrem, E. C., & Ada, G. (2026). Women's knowledge and attitude scale toward AI-based chatbot systems in preconception counseling: A scale development study [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-9832908/v1", "authors": "Ebru Cirban Ekrem; Güleser Ada", "year": 2026, "venue": "Research Square", "doi": "10.21203/rs.3.rs-9832908/v1", "url": "https://doi.org/10.21203/rs.3.rs-9832908/v1", "preprint_url": "https://www.researchsquare.com/article/rs-9832908/v1", "items_available": true, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "l2-cails", "name": "L2 Writers' Critical AI Literacy Scale", "acronym": "L2-CAILS", "summary": "A 25-item scale of second-language writers' critical AI literacy when using generative AI for academic writing. It covers understanding how GenAI works, ethical use, writing strategies, critically checking AI output, and self-monitoring. Use it to assess L2 students' GenAI competencies or to evaluate literacy interventions.", "target": "generative AI in L2 academic writing", "constructs": [ "literacy", "ethics-concerns", "learning" ], "populations": [ "university students" ], "items": 25, "response": null, "subscales": [ { "name": "Awareness and Understanding of AI", "items": null, "description": "Knowledge of how GenAI works and its limits" }, { "name": "Ethical and Responsible Use", "items": null, "description": "Responsible, honest and transparent GenAI use" }, { "name": "AI Application Strategies", "items": null, "description": "Strategic use of GenAI in writing" }, { "name": "Critical Evaluation of AI Outputs", "items": null, "description": "Judging accuracy and quality of AI outputs" }, { "name": "Metacognitive Engagement and Self-Regulation", "items": null, "description": "Monitoring and regulating one's own AI-assisted writing" } ], "psychometrics": { "structure": "Literature review, expert validation and pilot, then EFA and CFA, giving a 25-item 5-factor model", "reliability": "Reported as reliable; values not seen", "validity": [ "Content validity via expert validation", "Construct (factorial) validity via EFA/CFA" ], "samples": [ { "n": 420, "country": "not stated in abstract (authors based in China)", "population": "L2 university students" } ] }, "status": "published", "citation": "Yao, G., & Fan, L. (2026). L2 writers' critical AI literacy in AI-assisted academic writing: Scale development and validation. International Review of Applied Linguistics in Language Teaching. Advance online publication. https://doi.org/10.1515/iral-2025-0218", "authors": "Guangyuan Yao; Lingxi Fan", "year": 2026, "venue": "International Review of Applied Linguistics in Language Teaching", "doi": "10.1515/iral-2025-0218", "url": "https://doi.org/10.1515/iral-2025-0218", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "Only expert validation and EFA/CFA construct validity are reported; no convergent, discriminant or criterion evidence.", "verified": "2026-09-29" }, { "id": "latcs", "name": "Learners' Attitudes Toward ChatGPT Scale", "acronym": "LATCS", "summary": "A 16-item scale of university students' attitudes toward ChatGPT, covering both positive and negative feelings and both emotional and rational judgments. Use it when you need more than a single 'like/dislike' score for students' views of ChatGPT.", "target": "ChatGPT", "constructs": [ "attitudes" ], "populations": [ "university students" ], "items": 16, "response": null, "subscales": [ { "name": "(multidimensional; names not given in abstract)", "items": null, "description": "Positive and negative, emotional and rational attitude dimensions toward ChatGPT" } ], "psychometrics": { "structure": "EFA and CFA on the exploratory half (n = 425) produced 16 items; CFA on the independent validation half (n = 425)", "reliability": "Reported (values not in abstract)", "validity": [ "Validity assessed via CFA in an independent sample (type not specified in abstract)" ], "samples": [ { "n": 425, "country": "China", "population": "university students, exploratory subsample" }, { "n": 425, "country": "China", "population": "university students, validation subsample (115 males)" } ] }, "status": "published", "citation": "Zhang, Y., Xue, X., Ding, M., & Yang, X. (2026). Mapping the complexity of learners' attitudes toward ChatGPT: Preliminary validation of a new scale. Behaviour & Information Technology, 45(3), 463–477. https://doi.org/10.1080/0144929X.2025.2520595", "authors": "Yuchi Zhang; Xinru Xue; Min Ding; Xianmin Yang", "year": 2026, "venue": "Behaviour & Information Technology", "doi": "10.1080/0144929X.2025.2520595", "url": "https://doi.org/10.1080/0144929X.2025.2520595", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "Reliability and validity were checked only through CFA; no convergent or criterion evidence. The authors call it a preliminary validation.", "verified": "2026-09-29" }, { "id": "machine-companionship-scale", "name": "Machine Companionship Scale (AI Companionship Scale)", "acronym": "MC", "summary": "A 12-item measure of what companionship with an AI companion feels like (Replika, Character.AI, or a general LLM such as ChatGPT used as a companion). It covers a fulfilling, meaningful exchange and a sense of being together and bonded. Use it to study how people experience relationships with AI companions.", "target": "Text-based AI companions (general-purpose LLMs such as ChatGPT, Claude or Gemini used as companions; companion apps such as Replika, Character.AI and Kindroid; social-platform AI)", "constructs": [ "relationships", "social-presence" ], "populations": [ "general adults" ], "items": 12, "response": "7-point Likert-style, forced-response; item stem 'My connection with [Name] ...'", "subscales": [ { "name": "Eudaimonic Exchange", "items": 7, "description": "The connection feels purposeful, collaborative, fulfilling, inspiring, meaningful and satisfying, and it makes the user better" }, { "name": "Connective Coordination", "items": 5, "description": "Being in each other's presence, focusing on one another, experiencing things together, mutual care and a strong bond" } ], "psychometrics": { "structure": "EFA (principal axis factoring, direct oblimin; 54-item pool reduced over 3 rounds) gave 2 correlated factors (r = .70). CFA in an independent sample: χ²(53) = 179.97, SRMR .051, RMSEA .098 [.083, .114], CFI .942. The two-factor model fit better than a one-factor model.", "reliability": "ω .93 (Eudaimonic Exchange), .92 (Connective Coordination); confirmatory sample: both scales > .80", "validity": [ "Convergent: canonical correlations with agentic and experiential mind perception, Inclusion of Other in the Self, positive affect (Affect Grid), relatedness need satisfaction, a single companionship item and weekly hours", "Relations with utilitarian motives and loneliness examined; patterns largely replicated in the confirmatory sample" ], "samples": [ { "n": 467, "country": "UK and USA (Prolific) plus Reddit recruits", "population": "adults using text-based AI for social purposes (74.9% general-purpose LLMs, 19.7% companion apps)" }, { "n": 249, "country": "UK and USA (Prolific; same sampling parameters)", "population": "AI companion users (confirmatory sample)" } ] }, "status": "published", "citation": "Banks, J. (2026). Measuring machine companionship experiences: Scale development and validation for AI companions. Computers in Human Behavior, 179, 108945. https://doi.org/10.1016/j.chb.2026.108945", "authors": "Jaime Banks", "year": 2026, "venue": "Computers in Human Behavior", "doi": "10.1016/j.chb.2026.108945", "url": "https://doi.org/10.1016/j.chb.2026.108945", "preprint_url": "https://arxiv.org/abs/2511.00654", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "maladaptive-ai-use-for-learning", "name": "Maladaptive AI Use for Learning Scale", "acronym": null, "summary": "A 12-item scale of whether learners use generative AI in ways that bypass real learning: accepting its output unchecked, skipping deep processing, and feeling they understand more than they do. Use it to study counterproductive student AI use and its links to learning outcomes.", "target": "generative AI tools (e.g., ChatGPT, Gemini, Claude) used for learning/studying", "constructs": [ "reliance", "learning", "dependence" ], "populations": [ "university students" ], "items": 12, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Uncritical Reliance", "items": 4, "description": "Using AI facts, sources or explanations without verifying them" }, { "name": "Shallow Processing", "items": 4, "description": "Letting AI do the work instead of engaging with the learning material" }, { "name": "Illusory Competence", "items": 4, "description": "Feeling more knowledgeable or capable after using AI than one really is" } ], "psychometrics": { "structure": "EFA (PAF, oblimin; n = 200) gave 3 factors, 12 items, about 57% of variance; CFA (n = 279) gave robust CFI .97, TLI .96, RMSEA .05, SRMR .04; a second CFA was run in Study 4", "reliability": "α total .86–.90 across Studies 3, 4 and 6; subscales .74–.88; 1-week test-retest ICC(2,1) .77 total (subscales .65–.76)", "validity": [ "Content validity (psa .94, csv .88)", "Convergent validity (GenAI cognitive preoccupation, GenAI dependency consequences, surface motive/strategy)", "Discriminant validity (positive and negative affect)", "Measurement invariance across sex (multi-group CFA)", "Nomological network (AI attachment, procrastination, critical thinking in AI use, personality, etc.)", "Criterion validity: lower GPA, poorer independent writing performance, greater overconfidence" ], "samples": [ { "n": 200, "country": "USA", "population": "higher education students (Prolific), EFA" }, { "n": 279, "country": "USA", "population": "higher education students (Prolific), CFA" }, { "n": 283, "country": "USA", "population": "higher education students (Prolific), validity and invariance" }, { "n": 64, "country": "Singapore", "population": "university students, 1-week test-retest" }, { "n": 83, "country": "Singapore", "population": "university students, criterion validity (analytic sample of 106 recruited)" } ] }, "status": "preprint", "citation": "Lau, G. R., Liow, C. J. M., Gasevic, D., & Hartanto, A. (2026). Maladaptive AI use for learning: A regulatory-failure framework, scale development, nomological network, and links to reduced learning outcomes [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/qdjuz_v1", "authors": "Gabriel Rongyang Lau; Caresse Jia Min Liow; Dragan Gasevic; Andree Hartanto", "year": 2026, "venue": "PsyArXiv", "doi": "10.31234/osf.io/qdjuz_v1", "url": "https://doi.org/10.31234/osf.io/qdjuz_v1", "preprint_url": "https://doi.org/10.31234/osf.io/qdjuz_v1", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "moral-suspension-scale", "name": "Moral Suspension Scale", "acronym": "MSS", "summary": "A 38-item scale of 'moral suspension' during GenAI-assisted tasks: experiencing GenAI as opaque, being unsure who is responsible for ethical problems in its output, and passively exempting oneself from responsibility. Use it to study moral disengagement or responsibility ambiguity in human–GenAI collaboration.", "target": "Generative AI tools used in academic or creative tasks", "constructs": [ "ethics-concerns", "other" ], "populations": [ "general adults" ], "items": 38, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree); 8 items reverse-scored", "subscales": [ { "name": "Interactive Environmental Complexity", "items": 13, "description": "Perceived opacity, randomness and non-traceability of GenAI outputs and processes" }, { "name": "Ambiguity in Responsibility Attribution", "items": 11, "description": "Uncertainty about how much moral responsibility one bears for GenAI output" }, { "name": "Passive Responsibility Exemption", "items": 14, "description": "Not checking, correcting or reflecting on ethical risks in GenAI content; leaving it to the platform" } ], "psychometrics": { "structure": "Expert review and pilot, then EFA and CFA supporting 3 dimensions; measurement invariance across gender (fit indices not seen)", "reliability": "Reported as strong in the abstract; exact coefficients not seen", "validity": [ "Measurement invariance across gender", "Content validity via expert review", "Other validity evidence in the full text not verified (article inaccessible)" ], "samples": [ { "n": 785, "country": "China", "population": "Emerging adults aged 18-29 with GenAI-assisted academic/creative experience" } ] }, "status": "published", "citation": "Chen, Z., Guo, W., Zhang, Y., Liu, X., Li, B., & Wang, H. (2026). Moral suspension in human-GenAI interaction: Scale development and validation for Chinese emerging adults. International Journal of Human–Computer Interaction, 1–24. https://doi.org/10.1080/10447318.2026.2634251", "authors": "Zhirui Chen; Wenchen Guo; Yiyang Zhang; Xihui Liu; Biao Li; Hailiang Wang", "year": 2026, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2026.2634251", "url": "https://doi.org/10.1080/10447318.2026.2634251", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-language-learning-motivation-sdt", "name": "Motivation for Generative AI-based Language Learning Scale", "acronym": null, "summary": "A 17-item scale, based on self-determination theory, of EFL learners' motivation when learning a language with generative AI: feeling autonomous, competent and connected. Use it to study why and how learners engage with GenAI for language learning.", "target": "generative AI in (EFL) language learning", "constructs": [ "learning", "other" ], "populations": [ "university students" ], "items": 17, "response": null, "subscales": [ { "name": "Autonomy", "items": null, "description": "Sense of choice and volition in GenAI-based language learning" }, { "name": "Competence", "items": null, "description": "Feeling capable when learning with GenAI" }, { "name": "Relatedness", "items": null, "description": "Sense of connection when learning with GenAI" } ], "psychometrics": { "structure": "EFA (sample 1) and CFA (sample 2): 3 factors, 17 items", "reliability": "Reliability reported in both samples plus test-retest (values not in abstract)", "validity": [ "Criterion validity: strong associations with generative AI acceptance dimensions" ], "samples": [ { "n": null, "country": "Türkiye", "population": "EFL university learners (sample 1: EFA, test-retest)" }, { "n": null, "country": "Türkiye", "population": "EFL university learners (sample 2: CFA, criterion validity)" } ] }, "status": "published", "citation": "Demirci, H., Kara, M., & Korkmaz, Ö. (2026). Measuring motivation for generative AI-based language learning: Scale development and validation based on the self-determination theory. International Journal of Applied Linguistics, 36(2), 1425–1436. https://doi.org/10.1111/ijal.12868", "authors": "Hamdiye Demirci; Mehmet Kara; Özgen Korkmaz", "year": 2026, "venue": "International Journal of Applied Linguistics", "doi": "10.1111/ijal.12868", "url": "https://doi.org/10.1111/ijal.12868", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "motivations-to-ai-use-scale", "name": "Motivations to AI Use Scale", "acronym": null, "summary": "A 30-item measure of why people use generative AI: to learn, to be productive, to explore, for social support, to manage impressions, or to avoid effort. Use it to profile users and to link motives to outcomes such as companionship use or over-reliance.", "target": "Generative AI", "constructs": [ "acceptance-use", "relationships", "other" ], "populations": [ "general adults" ], "items": 30, "response": null, "subscales": [ { "name": "Learning", "items": null, "description": "Using GenAI to learn" }, { "name": "Productivity", "items": null, "description": "Using GenAI to get work done efficiently" }, { "name": "Exploration", "items": null, "description": "Using GenAI out of curiosity or to explore" }, { "name": "Social support", "items": null, "description": "Using GenAI for social or emotional support" }, { "name": "Impression management", "items": null, "description": "Using GenAI to manage how one comes across to others" }, { "name": "Effort avoidance", "items": null, "description": "Using GenAI to avoid effort" } ], "psychometrics": { "structure": "Qualitative item generation (Study 1) and content validation (Study 2); EFA (Study 3) found 6 dimensions; CFA (Study 4a) confirmed the six-factor structure", "reliability": "1-week test–retest: moderate-to-good absolute agreement and strong rank-order stability (internal consistency values not in abstract)", "validity": [ "Measurement invariance across sex and age groups", "Internal discriminant validity among dimensions", "External construct validity: differentiated associations with AI-related constructs and social networking site use motives", "Correlational profiles across psychological needs, personality and affect" ], "samples": [ { "n": 1165, "country": "Not reported in abstract", "population": "Adults across four quantitative samples (total)" } ] }, "status": "preprint", "citation": "Guevarra, Y. A., Lau, G. R., Tsai, M.-H., & Hartanto, A. (2026). Understanding why people use generative artificial intelligence: Development, validation, and psychological correlates of the Motivations to AI Use Scale [Preprint]. SSRN. https://doi.org/10.2139/ssrn.7441945", "authors": "Guevarra, Y. A., Lau, G. R., Tsai, M.-H., & Hartanto, A.", "year": 2026, "venue": "SSRN", "doi": "10.2139/ssrn.7441945", "url": "https://doi.org/10.2139/ssrn.7441945", "preprint_url": "https://doi.org/10.2139/ssrn.7441945", "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "pca-scale", "name": "Perceived Cognitive Assistance Scale", "acronym": "PCA", "summary": "A 9-item measure of how much users feel an LLM expands their thinking during complex decisions (structuring plans, spotting gaps, simulating scenarios). It was validated with retail traders who use LLMs. Useful for studying LLMs as reasoning aids in decision-making or at work.", "target": "LLMs used as reasoning aids in decision-making (retail trading)", "constructs": [ "reliance", "workplace", "other" ], "populations": [ "employees & professionals" ], "items": 9, "response": "7-point Likert (1 = strongly disagree to 7 = strongly agree)", "subscales": [ { "name": "(unidimensional)", "items": 9, "description": "Perceived expansion of one's cognitive capability (structuring, comparing, simulating and reflecting) during LLM-supported decisions" } ], "psychometrics": { "structure": "Pilot EFA (N = 69). Preregistered CFA (N = 283), one factor: CFI .966, TLI .955, SRMR .049, RMSEA .089. ESEM used to triangulate.", "reliability": "ω .863–.883; α .859–.881", "validity": [ "Discriminant vs perceived usefulness: HTMT .845; five-factor model better than merged PCA–PU model; ESEM correlation .775 (Fornell–Larcker not met)", "Criterion: modest non-redundant association with complexity intentions; exploratory mediation not confirmed" ], "samples": [ { "n": 69, "country": "mainly UK and US (Prolific)", "population": "LLM-using retail traders (pilot)" }, { "n": 283, "country": "mainly UK and US (Prolific)", "population": "LLM-using retail traders (preregistered CFA)" } ] }, "status": "preprint", "citation": "Gimmelberg, D., & Ludviga, I. (2026). Psychometric validation of the Perceived Cognitive Assistance Scale: Exploratory and confirmatory evidence from LLM-supported decision-making in retail trading [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/2dp7v_v1", "authors": "Dmitrii Gimmelberg; Iveta Ludviga", "year": 2026, "venue": "PsyArXiv", "doi": "10.31234/osf.io/2dp7v_v1", "url": "https://doi.org/10.31234/osf.io/2dp7v_v1", "preprint_url": "https://doi.org/10.31234/osf.io/2dp7v_v1", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "preservice-chemistry-teachers-genai-acceptance", "name": "Pre-Service Chemistry Teachers' Acceptance and Use of Generative AI Scale", "acronym": null, "summary": "A 35-item questionnaire, based on UTAUT2 and the theory of planned behaviour, on pre-service chemistry teachers' acceptance and use of generative AI. It covers expectations, social norms, enjoyment, habit, attitude, control, intention and actual use. Use it in science-teacher-education research, but note that several of its factors are almost indistinguishable empirically.", "target": "Generative AI (e.g., ChatGPT, Gemini, Claude, Copilot)", "constructs": [ "acceptance-use", "attitudes" ], "populations": [ "teachers & academics" ], "items": 35, "response": null, "subscales": [ { "name": "Subjective Norms (SN)", "items": 5, "description": "Perceived social pressure to use GenAI" }, { "name": "Performance Expectancy (PE)", "items": 3, "description": "Belief that GenAI improves performance" }, { "name": "Effort Expectancy (EE)", "items": 3, "description": "Perceived ease of using GenAI" }, { "name": "Hedonic Motivation (HM)", "items": 3, "description": "Enjoyment of using GenAI" }, { "name": "Facilitating Conditions (FC)", "items": 4, "description": "Resources and support available for using GenAI" }, { "name": "Habit (H)", "items": 4, "description": "Habitual use of GenAI" }, { "name": "Attitude (AT)", "items": 4, "description": "Overall evaluation of using GenAI" }, { "name": "Perceived Behavioral Control (PBC)", "items": 3, "description": "Perceived control over using GenAI" }, { "name": "Behavioral Intention (BI)", "items": 3, "description": "Intention to use GenAI" }, { "name": "AI Use (AIU)", "items": 3, "description": "Self-reported actual GenAI use" } ], "psychometrics": { "structure": "First-order CFA with 10 factors (χ² = 1423.409, RMSEA = .067, CFI = .917, TLI = .902). Second-order CFA with a higher-order 'AttitudeUse' factor (χ² = 1502.040, RMSEA = .067, CFI = .912, TLI = .902)", "reliability": "α .738-.898 (total .971); CR > .70 (total .982)", "validity": [ "Content validity: CVI = .91 (2 experts)", "Convergent validity: AVE .532-.685", "Discriminant validity doubtful: inter-construct correlations .65-.98 (e.g., BI-AIU .980, AT-BI .976)" ], "samples": [ { "n": 240, "country": "Indonesia", "population": "Generation Z pre-service chemistry teachers" } ] }, "status": "published", "citation": "Adam, W., Tari, S. R., Qudratuddarsi, H., Meivawati, E., & Yanti, M. (2026). Validation of pre-service chemistry teachers' acceptance and use of generative artificial intelligence scale: Confirmatory factor analysis. Chemistry Education Practice, 9(1), 78–88. https://doi.org/10.29303/cep.v9i1.11992", "authors": "Wahyuni Adam; Suci Rizkina Tari; Hilman Qudratuddarsi; Eli Meivawati; Meili Yanti", "year": 2026, "venue": "Chemistry Education Practice", "doi": "10.29303/cep.v9i1.11992", "url": "https://doi.org/10.29303/cep.v9i1.11992", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "pgaius", "name": "Problematic Generative Artificial Intelligence Use Scale", "acronym": "PGAIUS", "summary": "An 18-item scale of addiction-like problematic use of chatbot-style generative AI among university students, covering loss of control, escapism, absorption, preoccupation, withdrawal and negative consequences. Use it to screen or study overuse of GenAI tools.", "target": "generative AI tools (chatbot-based)", "constructs": [ "dependence", "health" ], "populations": [ "university students" ], "items": 18, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree); total 18–90", "subscales": [ { "name": "Compulsive Use / Loss of Control", "items": 3, "description": "Using GAI longer than intended and struggling to cut down" }, { "name": "Mood Regulation / Escapism", "items": 3, "description": "Using GAI to feel better or escape problems and stress" }, { "name": "Cognitive Absorption", "items": 3, "description": "Losing track of time and surroundings while using GAI" }, { "name": "Salience / Preoccupation", "items": 3, "description": "Frequent urges to use GAI and thinking about it when not using it" }, { "name": "Withdrawal Symptoms", "items": 3, "description": "Discomfort when unable to use GAI" }, { "name": "Negative Outcomes / Functional Impairment", "items": 3, "description": "Harm to studies, tasks or daily functioning from GAI use" } ], "psychometrics": { "structure": "EFA (n=500; oblique rotation) 6 factors, 71.1% variance; CFA (n=500) six correlated factors: χ²/df 2.301, CFI .95, TLI .95, RMSEA .075, SRMR .079", "reliability": "α .932 total, .751–.869 subscales; ω .880; Spearman–Brown .842; 4-week test–retest r = .987 (n=153)", "validity": [ "Convergent validity: AVE .50, CR .83", "Discriminant validity: HTMT .65–.81", "Content validity: expert I-CVI ≥ .80" ], "samples": [ { "n": 1000, "country": "Türkiye", "population": "undergraduate students at public universities (500 EFA / 500 CFA)" }, { "n": 153, "country": "Türkiye", "population": "test–retest subsample" } ] }, "status": "published", "citation": "Besalti, M., Akdeniz Kudubeş, A., Kudubeş, İ. E., Özbay, Ö., Durmuş Sarıkahya, S., & Çınar Özbay, S. (2026). Development and psychometric evaluation of the Problematic Generative Artificial Intelligence Use Scale among Turkish university students. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2026.2652063", "authors": "Metin Besalti; Aslı Akdeniz Kudubeş; İsmet Emir Kudubeş; Özkan Özbay; Selma Durmuş Sarıkahya; Sevil Çınar Özbay", "year": 2026, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2026.2652063", "url": "https://doi.org/10.1080/10447318.2026.2652063", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "teachers-gai-uses-questionnaire", "name": "Questionnaire on Teachers' Uses of Generative Artificial Intelligence", "acronym": null, "summary": "A 30-item questionnaire on how secondary school teachers use generative AI across six areas, from managing their workload and creating materials to assessment, supporting diverse learners and motivating students. Use it to map GenAI use in schools or to plan teacher training.", "target": "Generative AI (teachers' professional uses)", "constructs": [ "acceptance-use", "workplace", "learning" ], "populations": [ "teachers & academics" ], "items": 30, "response": null, "subscales": [ { "name": "Teacher management", "items": null, "description": "Using GenAI for administrative and planning tasks" }, { "name": "Creation of materials", "items": null, "description": "Producing teaching resources with GenAI" }, { "name": "Student assessment", "items": null, "description": "Using GenAI to assess students" }, { "name": "Student empowerment", "items": null, "description": "Using GenAI in ways that build student autonomy" }, { "name": "Attention to diversity", "items": null, "description": "Using GenAI to adapt teaching to diverse learners" }, { "name": "Motivation", "items": null, "description": "Using GenAI to motivate students" } ], "psychometrics": { "structure": "CFA supported a six-factor model with good fit (indices not seen)", "reliability": "High internal consistency (values not seen)", "validity": [ "Content validity via expert judgement", "Adequate convergent and discriminant validity" ], "samples": [ { "n": 486, "country": "Spain", "population": "secondary school teachers" } ] }, "status": "published", "citation": "Pérez-Montesdeoca, H., Rodríguez-Rodríguez, D., Stendardi, D., & Fernández-Sogorb, A. (2026). Design and validation of a questionnaire on teachers' uses of generative artificial intelligence. Computers and Education Open, 10, 100332. https://doi.org/10.1016/j.caeo.2026.100332", "authors": "Héctor Pérez-Montesdeoca; Daniel Rodríguez-Rodríguez; David Stendardi; Aitana Fernández-Sogorb", "year": 2026, "venue": "Computers and Education Open", "doi": "10.1016/j.caeo.2026.100332", "url": "https://doi.org/10.1016/j.caeo.2026.100332", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "rocs-ns", "name": "Reliance on ChatGPT Scale for Nursing Students", "acronym": "ROCS-NS", "summary": "A 29-item, four-dimension scale of how much nursing students rely on ChatGPT for learning. Use it to sort nursing students by their level of reliance on ChatGPT.", "target": "ChatGPT (use in learning)", "constructs": [ "reliance", "learning", "dependence" ], "populations": [ "university students" ], "items": 29, "response": null, "subscales": [ { "name": "(four sub-dimensions; names not seen)", "items": null, "description": "Four sub-dimensions of reliance on ChatGPT in learning; names not given in the abstract." } ], "psychometrics": { "structure": "PCA gave 29 items in 4 sub-dimensions (70.03% variance); CFA fit indices acceptable", "reliability": "Internal consistency good or acceptable; test-retest showed stability (coefficients not seen)", "validity": [ "Content validity index .89; face validity", "Construct validity via PCA/CFA (SEM)" ], "samples": [ { "n": 633, "country": "not reported in abstract (authors in Saudi Arabia and Egypt)", "population": "Nursing students (data collected Aug–Dec 2024)" } ] }, "status": "published", "citation": "Elzeky, M. E. H., & Shahine, N. F. M. (2026). Reliance on ChatGPT in learning: Construction and validation of a reliance on ChatGPT scale for nursing students (ROCS-NS): Using structural equation modeling (SEM). Nurse Education in Practice, 91, 104696. https://doi.org/10.1016/j.nepr.2025.104696", "authors": "Mohamed E. H. Elzeky, Noha F. M. Shahine", "year": 2026, "venue": "Nurse Education in Practice", "doi": "10.1016/j.nepr.2025.104696", "url": "https://doi.org/10.1016/j.nepr.2025.104696", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "evidence": "partial", "flag_reason": "Only face, content and factorial validity are reported; no convergent, discriminant or criterion evidence.", "verified": "2026-09-29" }, { "id": "sagai", "name": "Scale for Attitudes Towards Generative AI", "acronym": "SAGAI", "summary": "A 23-item scale of university students' attitudes toward generative AI in education: how useful they find it, what they expect of it, how competent they feel, and how anxious it makes them. Use it to survey learner attitudes toward GenAI in education.", "target": "generative AI in education", "constructs": [ "attitudes", "anxiety", "self-efficacy" ], "populations": [ "university students" ], "items": 23, "response": null, "subscales": [ { "name": "Perceived usefulness", "items": null, "description": "Benefits of GenAI for learning" }, { "name": "Expectancy", "items": null, "description": "Expectations about GenAI's future role and potential" }, { "name": "Competency", "items": null, "description": "Perceived competence in using GenAI" }, { "name": "Anxiety", "items": null, "description": "Apprehension and concerns about GenAI" } ], "psychometrics": { "structure": "EFA (n=244) and CFA (n=243): 4 factors, 23 items", "reliability": "Reported as reliable (values not in abstract)", "validity": [ "Cross-validation of factor structure in an independent sample", "Expert review and pilot testing" ], "samples": [ { "n": 244, "country": "Türkiye", "population": "undergraduate students (EFA)" }, { "n": 243, "country": "Türkiye", "population": "undergraduate students (CFA)" } ] }, "status": "published", "citation": "Durak, G., Öncü, S., Çankaya, S., & Çiğdem, H. (2026). Development and validation of the Scale for Attitudes Towards Generative AI (SAGAI). European Journal of Education, 61(1), e70415. https://doi.org/10.1111/ejed.70415", "authors": "Gürhan Durak; Semiral Öncü; Serkan Çankaya; Harun Çiğdem", "year": 2026, "venue": "European Journal of Education", "doi": "10.1111/ejed.70415", "url": "https://doi.org/10.1111/ejed.70415", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "sgate-ce", "name": "Scale for Generative AI Technology Acceptance in Conservation Education", "acronym": "SGATE-CE", "summary": "A 17-item scale of architecture students' acceptance of generative AI in conservation and heritage coursework: usefulness, intention to use, hands-on engagement, and conservation-specific usefulness. Use it in design and architecture education research.", "target": "Generative AI tools (respondents instructed to consider GenAI; some items say 'AI technologies')", "constructs": [ "acceptance-use", "learning" ], "populations": [ "university students" ], "items": 17, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Perceived Usefulness", "items": 4, "description": "Usefulness of GenAI for coursework" }, { "name": "Behavioral Intention", "items": 5, "description": "Intention to use and learn about GenAI" }, { "name": "Applied Educational Engagement", "items": 4, "description": "Self-reported hands-on use of GenAI in learning tasks" }, { "name": "Perceived Conservation-Specific Utility", "items": 4, "description": "Usefulness for conservation and restoration tasks (heritage identity, materials, standards)" } ], "psychometrics": { "structure": "35-item pool cut to 27 by qualitative expert review. PAF with oblimin (n = 157) gave 4 factors, 17 items. CFA of the unmodified 4-factor model (n = 201): CMIN/df 2.04, CFI .942, TLI .930, RMSEA .072, SRMR .058. The 4-factor model beat 1-, 2- and 3-factor alternatives.", "reliability": "Validation sample α .84–.90, ω .84–.90, CR .84–.90; EFA sample α .80–.89", "validity": [ "Convergent: AVE .57–.64", "Discriminant: Fornell–Larcker met, HTMT < .85 (BI–PCSU .74, upper CI slightly above .85)", "Theory-specified structural associations (PCSU→PU, PCSU→BI, BI→AEE strong; PU→BI weak and unstable under bootstrap)" ], "samples": [ { "n": 157, "country": "Türkiye", "population": "Undergraduate architecture students (EFA)" }, { "n": 201, "country": "Türkiye", "population": "Undergraduate architecture students (CFA)" } ] }, "status": "published", "citation": "Koca, M., & Büyükmıhcı, G. (2026). A context-sensitive scale for assessing GenAI-contextualized acceptance in conservation-related architectural education: Development and initial validation. Frontiers in Education, 11, 1861972. https://doi.org/10.3389/feduc.2026.1861972", "authors": "Koca, M., & Büyükmıhcı, G.", "year": 2026, "venue": "Frontiers in Education", "doi": "10.3389/feduc.2026.1861972", "url": "https://doi.org/10.3389/feduc.2026.1861972", "preprint_url": null, "items_available": true, "language": "Turkish", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-dependency-programming", "name": "Scale of Generative AI Dependency on Programming among University Students", "acronym": null, "summary": "A 13-item scale of students' affective, cognitive and behavioral dependence on generative AI when learning to program. Use it in computing-education studies of AI coding assistants.", "target": "Generative AI in programming learning (e.g., ChatGPT, coding assistants)", "constructs": [ "dependence", "learning" ], "populations": [ "university students" ], "items": 13, "response": null, "subscales": [ { "name": "Affect", "items": 4, "description": "Emotional reliance on GenAI during programming (e.g., unease when it does not respond)." }, { "name": "Cognition", "items": 5, "description": "Beliefs that GenAI's code and problem-solving are superior to one's own." }, { "name": "Behavior", "items": 4, "description": "Habitually turning to GenAI for coding tasks, even easy ones." } ], "psychometrics": { "structure": "Three factors (ABC model); EFA (n=661) then CFA (n=634; χ²/df 4.15, CFI .981, TLI .976, RMSEA .070, SRMR .025); three-factor model beat a one-factor model", "reliability": "α .963 total; subscales .951–.959; CR .957–.961", "validity": [ "Content validity by 10 experts", "Convergent: AVE .824–.862", "Discriminant: Fornell-Larcker criterion met", "Criterion: r .464–.734 with a general Generative AI Dependency Scale" ], "samples": [ { "n": 1295, "country": "China", "population": "university students with GenAI-supported programming experience (split 661 EFA / 634 CFA)" } ] }, "status": "published", "citation": "Zhang, H., Yang, Y., Zhang, Y., Qiu, B., & Yang, J. (2026). Generative AI dependency on programming among university students: A scale development and validation study. BMC Psychology, 14, 1245. https://doi.org/10.1186/s40359-026-05011-5", "authors": "Hao Zhang; Yanchao Yang; Ying Zhang; Baishuang Qiu; Jiongzhao Yang", "year": 2026, "venue": "BMC Psychology", "doi": "10.1186/s40359-026-05011-5", "url": "https://doi.org/10.1186/s40359-026-05011-5", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "se-ai-l2aws", "name": "Self-Efficacy in AI-Assisted L2 Academic Writing Scale", "acronym": "SE-AI-L2AWS", "summary": "An 11-item scale of how confident EFL university students feel using generative AI for academic writing in English: managing the writing process, improving their language, and using AI ethically. Useful for studying motivation and writing support where students use AI.", "target": "generative AI in L2 (English) academic writing", "constructs": [ "self-efficacy", "learning", "academic-integrity" ], "populations": [ "university students" ], "items": 11, "response": "7-point scale (1 = not at all confident to 7 = very confident)", "subscales": [ { "name": "AI-Assisted Process Regulation Efficacy", "items": 4, "description": "Confidence managing and regulating the writing process with AI" }, { "name": "AI-Assisted Language Efficacy", "items": 5, "description": "Confidence using AI to improve the language of one's writing" }, { "name": "Ethical and Integrity Efficacy", "items": 2, "description": "Confidence using AI ethically and with academic integrity" } ], "psychometrics": { "structure": "EFA (Sample A, n=340): 3 factors, 82.55% variance, loadings .75–.89; CFA (Sample B, n=340): CFI .988, TLI .985, RMSEA .049, SRMR .038", "reliability": "α .94 total; subscales α .87–.92; CR .88–.93", "validity": [ "Convergent validity: AVE .70–.79", "Discriminant validity: HTMT < .85", "Concurrent validity: r = .61 with L2 writing self-efficacy, r = −.65 with writing anxiety", "Measurement invariance across gender and academic major (STEM vs non-STEM)" ], "samples": [ { "n": 340, "country": "China", "population": "non-English-major university EFL students (EFA sample)" }, { "n": 340, "country": "China", "population": "non-English-major university EFL students (CFA sample)" } ] }, "status": "published", "citation": "Yao, G., & Fan, L. (2026). Self-efficacy in AI-assisted L2 academic writing: Scale development and validation. BMC Psychology, 14(1), 814. https://doi.org/10.1186/s40359-026-04503-8", "authors": "Guangyuan Yao; Lingxi Fan", "year": 2026, "venue": "BMC Psychology", "doi": "10.1186/s40359-026-04503-8", "url": "https://doi.org/10.1186/s40359-026-04503-8", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "sag-ai", "name": "Shame and Guilt related to AI Tools Scale", "acronym": "SAG-AI", "summary": "A 9-item scale of self-conscious moral emotions about using AI tools such as ChatGPT: shame (fear of being judged), guilt (environmental, economic or moral costs) and feeling like an impostor. Use it to study stigma and moral discomfort around GenAI use in education or knowledge work.", "target": "AI tools such as ChatGPT (generative AI tools)", "constructs": [ "ethics-concerns", "anxiety", "other" ], "populations": [ "general adults" ], "items": 9, "response": "7-point Likert (1 = strongly disagree to 7 = strongly agree); subscale scores are item means", "subscales": [ { "name": "AI Shame", "items": 3, "description": "Feeling judged, mocked or criticized by others for relying on AI" }, { "name": "AI Guilt", "items": 3, "description": "Guilt or moral unease about AI's environmental, economic and ethical impacts" }, { "name": "AI Impostor syndrome", "items": 3, "description": "Feeling inauthentic, like a faker, or like one is pretending to have skills when using AI" } ], "psychometrics": { "structure": "Content validity (CVI) and then iterative EFA (principal axis, oblimin) gave 3 factors: RMSR .02, RMSEA .069, TLI .957, 60% variance, factor r .59–.65. Multigroup CFA (MLR) supported scalar invariance across gender (CFI .969, TLI .967, RMSEA .052) and across two independent samples (CFI .986, TLI .983, RMSEA .048, SRMR .036).", "reliability": "α Impostor .82, Guilt .85, Shame .75 (Study 1)", "validity": [ "Convergent: all subscales correlate with the Experiential Shame Scale and TOSCA-3 Shame (|r| .20–.29); Guilt shows no correlation with GASP/TOSCA guilt, only a weak link with GASP Negative Self-Evaluation (r = .15)", "Correlates negatively with AI attitudes (AIAS-4) and AI literacy (AILS) (r −.16 to −.46)", "Criterion: subscales predict UTAUT2 AI-tool use frequency (guilt and impostor negative, shame positive; R² = .096)", "Scalar measurement invariance across gender and across samples" ], "samples": [ { "n": 301, "country": "United Kingdom", "population": "Representative adults via Prolific (Study 1; M age 46.7)" }, { "n": 301, "country": "United Kingdom", "population": "Representative adults via Prolific (Study 2; M age 46.2; cross-sample invariance)" } ] }, "status": "published", "citation": "Cipriani, E., Menicucci, D., Greco, A., & Grassini, S. (2026). Emotional responses to AI use: Development of the SAG-AI scale for shame and guilt. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2026.2648803", "authors": "Enrico Cipriani; Danilo Menicucci; Alberto Greco; Simone Grassini", "year": 2026, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2026.2648803", "url": "https://doi.org/10.1080/10447318.2026.2648803", "preprint_url": "https://doi.org/10.31234/osf.io/3w4cy_v1", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "strategic-prompting-scale", "name": "Strategic Prompting Scale", "acronym": "SPS", "summary": "A 15-item measure of how strategically people manage their prompting of GenAI: planning prompts, adapting them in response to outputs, and evaluating outputs. Use it to study metacognitive skill in human–AI interaction.", "target": "Generative AI chatbots / LLMs", "constructs": [ "literacy", "learning" ], "populations": [ "university students" ], "items": 15, "response": "5-point (1 = not at all true for me to 5 = very true for me)", "subscales": [ { "name": "Planning", "items": 5, "description": "Planning goals and structure before prompting" }, { "name": "Adaptation", "items": 5, "description": "Changing prompts in response to outputs" }, { "name": "Evaluation", "items": 5, "description": "Critically evaluating AI outputs" } ], "psychometrics": { "structure": "Three correlated factors (EFA in Study 1, CFA in Study 2): χ²/df ≈ 1.98, CFI .975, GFI .987, RMSEA .049", "reliability": "Study 1: total α .87, subscale α .81–.85. Study 2: total ω .92 and α .867; subscale ω .845–.858 and α .812–.847", "validity": [ "Convergent: r = .41 with metacognition (MCPS) and .24 with critical thinking (CTAS)", "Discriminant: r = −.23 with disengagement" ], "samples": [ { "n": 187, "country": "Italy", "population": "university students aged 18–30 (mean 21.8)" }, { "n": 406, "country": "Italy", "population": "university students aged 18–30 (mean 22.1)" } ] }, "status": "published", "citation": "Suriano, R., & Plebe, A. (2026). The Strategic Prompting Scale (SPS) for measuring metacognitive regulation in human–AI interaction. Journal of Intelligence, 14(7), 141. https://doi.org/10.3390/jintelligence14070141", "authors": "Rossella Suriano; Alessio Plebe", "year": 2026, "venue": "Journal of Intelligence", "doi": "10.3390/jintelligence14070141", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13413385/", "preprint_url": null, "items_available": true, "language": "Italian", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-sfls", "name": "Student Feedback Literacy Scale for the GenAI Context", "acronym": "GenAI-SFLS", "summary": "Measures how well college students understand, seek, judge and act on feedback when generative AI is part of the feedback process, including prompting for feedback and ethical use. Use it in higher-education research on AI-supported feedback and assessment.", "target": "feedback involving generative AI", "constructs": [ "literacy", "learning", "ethics-concerns" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Cognitive (feedback knowledge; appreciation)", "items": null, "description": "Understanding and valuing feedback" }, { "name": "Behavioral (feedback prompting skills; feedback judgment; enaction of feedback)", "items": null, "description": "Prompting GenAI for feedback, judging it and acting on it" }, { "name": "Affective (emotional recognition; readiness to engage; commitment to change)", "items": null, "description": "Emotional engagement with feedback" }, { "name": "Ethical (feedback ethics)", "items": null, "description": "Ethical use of GenAI feedback" } ], "psychometrics": { "structure": "EFA (n=255); first- and second-order CFA (n=666); 4 dimensions, 9 factors", "reliability": "Not reported in abstract (unverified)", "validity": [ "Multi-group CFA invariance across gender, major and educational background" ], "samples": [ { "n": 255, "country": "China (inferred from author affiliation)", "population": "college students (EFA)" }, { "n": 666, "country": "China (inferred from author affiliation)", "population": "college students (CFA)" } ] }, "status": "published", "citation": "Cui, Y., Meng, Y., Qian, X., & Tang, L. (2026). Developing a college student feedback literacy scale for the GenAI context. Assessment & Evaluation in Higher Education, 51(5), 835–848. https://doi.org/10.1080/02602938.2025.2495048", "authors": "Yu Cui; Yaru Meng; Xi Qian; Lingjie Tang", "year": 2026, "venue": "Assessment & Evaluation in Higher Education", "doi": "10.1080/02602938.2025.2495048", "url": "https://doi.org/10.1080/02602938.2025.2495048", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "EFA, second-order CFA and measurement invariance are reported, but reliability could not be confirmed.", "verified": "2026-09-29" }, { "id": "genai-programming-self-efficacy-scale", "name": "Student Self-Efficacy for Programming with Generative AI Scale", "acronym": null, "summary": "A short 5-item scale measuring how confident introductory programming students feel about learning to program while using generative AI coding tools. Use it in computing-education studies alongside standard programming self-efficacy measures such as Steinhorst et al. (2020).", "target": "Generative AI coding tools in programming courses", "constructs": [ "self-efficacy", "learning" ], "populations": [ "university students" ], "items": 5, "response": null, "subscales": [ { "name": "(unidimensional)", "items": 5, "description": "Self-efficacy for learning and doing programming while using GenAI tools" } ], "psychometrics": { "structure": "11 initial items reduced to 5 through statistical analysis and cognitive probing interviews. Modelled on the Steinhorst et al. (2020) instrument, which was re-validated in the same GenAI-integrated course. The specific factor-analytic method is not stated in the abstract.", "reliability": "Strong internal reliability reported; value not given in abstract", "validity": [ "Discriminant validity against items in the Steinhorst self-efficacy subscales", "Criterion validity with students' GenAI usage patterns", "Cognitive probing interviews (response-process evidence)" ], "samples": [ { "n": null, "country": "not stated in abstract", "population": "students in an introductory programming course that fully integrates GenAI" } ] }, "status": "published", "citation": "Prather, J., Margulieux, L., Kharitonova, Y., Yang, Y., Reeves, B. N., Denny, P., Gorson Benario, J., Holmes, E. D. V., Spaulding, E., Barbre, G., Blake, M., & Leinonen, J. (2026). A validated scale measuring student self-efficacy for programming with generative AI. In Proceedings of the 2026 ACM Conference on International Computing Education Research (ICER '26) Vol. 1 (pp. 59–72). ACM. https://doi.org/10.1145/3765964.3811645", "authors": "James Prather; Lauren Margulieux; Yekaterina Kharitonova; Yonggao Yang; Brent N. Reeves; Paul Denny; Jamie Gorson Benario; Ernest D.V. Holmes; Erin Spaulding; Gweneth Barbre; Musa Blake; Juho Leinonen", "year": 2026, "venue": "Proceedings of the 2026 ACM Conference on International Computing Education Research (ICER)", "doi": "10.1145/3765964.3811645", "url": "https://doi.org/10.1145/3765964.3811645", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "Reliability, discriminant and criterion validity are reported, but the factor-analytic method could not be confirmed.", "verified": "2026-09-29" }, { "id": "samr-genai-integration-scale", "name": "Students' Generative AI Integration Scale (SAMR)", "acronym": null, "summary": "Measures how deeply students integrate generative AI into their learning, using the four SAMR levels (Substitution, Augmentation, Modification, Redefinition), grouped into Enhancement and Transformation. Use it to tell apart students who use GenAI only as a replacement tool from those who use it to transform tasks.", "target": "generative AI in learning", "constructs": [ "acceptance-use", "learning" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Substitution", "items": null, "description": "GenAI replaces an existing tool with no functional change (Enhancement)" }, { "name": "Augmentation", "items": null, "description": "GenAI replaces a tool with functional improvement (Enhancement)" }, { "name": "Modification", "items": null, "description": "GenAI allows significant task redesign (Transformation)" }, { "name": "Redefinition", "items": null, "description": "GenAI enables previously inconceivable tasks (Transformation)" } ], "psychometrics": { "structure": "Item analysis, then EFA (4 factors matching SAMR); CFA supported 4 first-order factors and a second-order structure with Enhancement and Transformation", "reliability": "Item-level discrimination/reliability analysed; scale coefficients not visible in abstract", "validity": [ "Factorial validity (first- and second-order CFA)" ], "samples": [ { "n": 1295, "country": "not stated in abstract (authors based in Hebei, China and Macau)", "population": "students (convenience sample)" } ] }, "status": "published", "citation": "Zhang, J., Yang, Y., & Zhang, H. (2026). Development and validation of a scale of assessing students' integration of generative AI in learning based on the SAMR model. Acta Psychologica, 269, 107581. https://doi.org/10.1016/j.actpsy.2026.107581", "authors": "Jing Zhang; Yanchao Yang; Hao Zhang", "year": 2026, "venue": "Acta Psychologica", "doi": "10.1016/j.actpsy.2026.107581", "url": "https://doi.org/10.1016/j.actpsy.2026.107581", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "evidence": "partial", "flag_reason": "No validity evidence beyond factor structure is reported, and reliability coefficients could not be confirmed.", "verified": "2026-09-29" }, { "id": "genai-task-strategy-scale", "name": "Task–Strategy Scale for Profiling Generative AI Use", "acronym": null, "summary": "A 15-item scale describing how undergraduates use generative AI for learning: which tasks they use it for (text vs. media/data) and which strategies they use (prompting/selection, critical verification). It gives a profile of four subscale scores rather than relying on one total score.", "target": "Generative AI tools for learning", "constructs": [ "acceptance-use", "literacy", "learning" ], "populations": [ "university students" ], "items": 15, "response": "5-point Likert-type", "subscales": [ { "name": "Text-based task use", "items": 3, "description": "Using AI for drafts, summaries and checking understanding of learning content." }, { "name": "Media and data task use", "items": 5, "description": "Using AI for images/visuals, data analysis and document formatting." }, { "name": "Prompting and selection strategy", "items": 4, "description": "Refining prompts and choosing among AI outputs/tools." }, { "name": "Critical verification strategy", "items": 3, "description": "Checking the accuracy and sources of AI-generated content." } ], "psychometrics": { "structure": "Four factors, 15 items. EFA on polychoric correlations (PAF, oblimin), then CFA with WLSMV and two residual covariances: χ²(82)=158.78, CFI=.923, TLI=.901, RMSEA=.068, SRMR=.065. EFA and CFA used the same sample.", "reliability": "α total .836; media/data .853; critical verification .828; text-based .654; prompting/selection .623; CR .626–.844", "validity": [ "Expert content validity (5 experts; I-CVI)", "Convergent validity via AVE/CR (AVE .520 and .569 for two factors; weak for text-based .397 and prompting/selection .297)", "Discriminant validity via Fornell–Larcker and HTMT (< .85)" ], "samples": [ { "n": 201, "country": "South Korea", "population": "Undergraduates at a technology university in the Seoul metropolitan area" } ] }, "status": "published", "citation": "Lee, D., & Tang, L. (2026). Development and validation of a task-strategy scale for profiling generative AI use among undergraduates at an engineering-oriented university. The Journal of Korean Association of Computer Education, 29(6), 114–126. https://doi.org/10.32431/kace.2026.29.6.010", "authors": "Daeyeong Lee, Liu Tang", "year": 2026, "venue": "The Journal of Korean Association of Computer Education", "doi": "10.32431/kace.2026.29.6.010", "url": "https://doi.org/10.32431/kace.2026.29.6.010", "preprint_url": null, "items_available": true, "language": "Korean", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "teacher-ai-use-scale-loti", "name": "Teacher AI-use Scale (Levels of Technology Integration)", "acronym": null, "summary": "A 22-item scale of the ways teachers use generative AI: making materials, differentiating instruction, supporting students' self-regulated learning, their own professional development, and ethical awareness. Use it to describe how, not just whether, teachers use GenAI.", "target": "generative AI in teaching", "constructs": [ "acceptance-use", "workplace" ], "populations": [ "teachers & academics" ], "items": 22, "response": null, "subscales": [ { "name": "Instructional material development", "items": null, "description": "Using GenAI to create teaching materials" }, { "name": "Differentiated instruction", "items": null, "description": "Using GenAI to tailor instruction to different learners" }, { "name": "Student self-regulated learning support", "items": null, "description": "Using GenAI to support students' self-regulated learning" }, { "name": "Professional development", "items": null, "description": "Using GenAI for one's own professional learning" }, { "name": "Ethical awareness", "items": null, "description": "Awareness of ethical issues in using GenAI" } ], "psychometrics": { "structure": "Not reported in abstract (22 items, 5 dimensions)", "reliability": "Described as 'strong' (values not in abstract)", "validity": [ "Described as 'strong' (type not specified in abstract)" ], "samples": [ { "n": 1116, "country": "not reported", "population": "teachers" } ] }, "status": "published", "citation": "Jin, F., Deng, H., Yang, Z., Sun, Z., & Lin, C.-H. (2026, April). Measuring teachers' generative AI use: Scale development and validation [Paper presentation]. AERA Annual Meeting, Los Angeles, CA, United States. https://doi.org/10.3102/2282945", "authors": "Fangzhou Jin; Hongyun Deng; Zhengyu Yang; Zhong Sun; Chin-Hsi Lin", "year": 2026, "venue": "Proceedings of the 2026 AERA Annual Meeting", "doi": "10.3102/2282945", "url": "https://doi.org/10.3102/2282945", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "Conference paper; the abstract claims strong reliability and validity but gives no methods or values, and the full paper needs a login.", "verified": "2026-09-29" }, { "id": "teachers-genai-acceptance-utaut-chatgpt", "name": "Teachers' Generative AI Acceptance Scale (UTAUT, ChatGPT)", "acronym": null, "summary": "A 22-item scale based on UTAUT that measures how far teachers accept ChatGPT for teaching. It covers expected usefulness, ease of use, supporting conditions and social influence. Use it to study teachers' readiness to adopt generative AI in the classroom.", "target": "ChatGPT (educational use by teachers)", "constructs": [ "acceptance-use", "workplace" ], "populations": [ "teachers & academics" ], "items": 22, "response": "5-point Likert (strongly disagree to strongly agree); total score 22–110; no reverse items", "subscales": [ { "name": "Performance expectancy", "items": 7, "description": "Belief that ChatGPT improves teaching performance" }, { "name": "Social influence", "items": 5, "description": "Perceived social environment encouraging ChatGPT use" }, { "name": "Effort expectancy", "items": 5, "description": "Perceived ease of using ChatGPT" }, { "name": "Facilitating conditions", "items": 5, "description": "Resources, knowledge and technical support for using ChatGPT" } ], "psychometrics": { "structure": "PCA/EFA with varimax (n=200): 4 components, 80.19% of variance. CFA (n=215): χ²/df 1.653, CFI .97, TLI .97, IFI .97, NFI .94, GFI .88, RMSEA .055, SRMR .055.", "reliability": "α .95 total (PE .96, SI .95, EE .96, FC .90); test-retest .97 total (.97/.95/.97/.92)", "validity": [ "Content validity via expert review", "Item discrimination (upper vs lower 27%; item-total r .55–.76)", "Factorial validity via CFA; subscale intercorrelations .28–.71" ], "samples": [ { "n": 200, "country": "Türkiye", "population": "teachers experienced with ChatGPT (EFA)" }, { "n": 215, "country": "Türkiye", "population": "teachers (CFA)" }, { "n": 37, "country": "Türkiye", "population": "teachers (test-retest)" } ] }, "status": "published", "citation": "Karaoğlan Yılmaz, F. G., Yılmaz, R., & Gencel, N. (2026). Development and validation of a teachers' generative AI acceptance scale within the UTAUT framework: The case of ChatGPT. Journal of Teacher Education and Lifelong Learning, 8(1), 14–25. https://doi.org/10.51535/tell.1860597", "authors": "Fatma Gizem Karaoğlan Yılmaz; Ramazan Yılmaz; Nurgun Gencel", "year": 2026, "venue": "Journal of Teacher Education and Lifelong Learning", "doi": "10.51535/tell.1860597", "url": "https://doi.org/10.51535/tell.1860597", "preprint_url": null, "items_available": false, "language": "Turkish", "adaptations": [], "evidence": "partial", "flag_reason": "The full text confirms validity evidence is limited to content validity, factorial validity and item discrimination.", "verified": "2026-09-29" }, { "id": "tgaipcs", "name": "Teachers' Generative AI Professional Competence Scale", "acronym": null, "summary": "Measures teachers' professional competence with generative AI beyond basic literacy: judging when AI helps learning, leading ethical use, and keeping professional autonomy while using AI. Use it in teacher PD, school-leadership or teacher-education research.", "target": "Generative AI in teaching", "constructs": [ "workplace", "ethics-concerns", "literacy" ], "populations": [ "teachers & academics" ], "items": 30, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Generative AI pedagogical judgment", "items": 10, "description": "Judging when and how GenAI supports learning" }, { "name": "Ethical leadership", "items": 10, "description": "Leading ethical, responsible GenAI use" }, { "name": "Professional agency", "items": 10, "description": "Keeping professional autonomy and responsibility while using AI as support" } ], "psychometrics": { "structure": "Three correlated factors. EFA (n = 324) explained 64.1% of variance; CFA (n = 324, WLSMV): robust CFI = 1.00, RMSEA = .000, SRMR = .032, far better than a one-factor model. Exploratory graph analysis recovered the same three communities", "reliability": "α .926–.932; ordinal α .939–.944; ω .927–.933; CR .926–.934", "validity": [ "Content validity from a 10-expert panel (I-CVI/S-CVI, CVR) and cognitive pretesting", "Convergent: AVE .619–.645", "Discriminant: Fornell–Larcker (√AVE .787–.803 > inter-factor r .498–.525)", "Configural and metric invariance across gender (scalar/strict not conclusive)" ], "samples": [ { "n": 648, "country": "Saudi Arabia", "population": "K-12 classroom teachers" } ] }, "status": "published", "citation": "Aldighrir, W. M. (2026). Development and validation of the teachers generative AI professional competence scale. Scientific Reports, 16, Article 28496. https://doi.org/10.1038/s41598-026-65021-6", "authors": "Wafa Mohammed Aldighrir", "year": 2026, "venue": "Scientific Reports", "doi": "10.1038/s41598-026-65021-6", "url": "https://www.nature.com/articles/s41598-026-65021-6", "preprint_url": null, "items_available": true, "language": "Arabic", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "t-gase", "name": "Text-Based Generative AI Literacy Scale", "acronym": "T-GASE", "summary": "A 23-item self-efficacy ('I can...') measure of university students' literacy with text-based generative AI such as ChatGPT, Gemini and DeepSeek. It covers applying the tools (including prompting), expectations about GenAI, ethical awareness, and critically evaluating outputs. Use it to assess GenAI literacy or evaluate GenAI literacy training in higher education.", "target": "Text-based generative AI tools (e.g., ChatGPT, Gemini, DeepSeek)", "constructs": [ "literacy", "self-efficacy", "ethics-concerns" ], "populations": [ "university students" ], "items": 23, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Application", "items": 8, "description": "Using text-based GenAI effectively: prompting, giving context, adapting outputs, creative and collaborative use" }, { "name": "Expectations", "items": 6, "description": "Beliefs about GenAI's future value (professional skills, education, jobs, equal opportunity)" }, { "name": "Ethics", "items": 3, "description": "Awareness of misuse/unethical-use risks and using GenAI ethically" }, { "name": "Evaluation", "items": 6, "description": "Questioning the reliability and accuracy of GenAI outputs, spotting shortcomings, judging societal impacts and comparing tools" } ], "psychometrics": { "structure": "EFA (ML, oblimin; n = 332) gave 4 factors from 25 items (61.3% variance). CFA (n = 313) dropped 2 items and added 2 residual covariances; final 23-item model: χ²/df 1.47, CFI .95, TLI .95, RMSEA .039, SRMR .022.", "reliability": "CFA sample: α .76–.85 (Application .85, Expectations .76, Ethics .76, Evaluation .79); CR .76–.85; ω > .70 reported; EFA-stage 25-item α .94", "validity": [ "Convergent: AVE .53–.63 (Appendix Table 7)", "Discriminant: Fornell-Larcker criterion reported as met (Table 4)", "Group differences: descriptive discipline means (engineering M = 4.04 vs education 3.51, social sciences 3.55); STEM > non-STEM per abstract" ], "samples": [ { "n": 332, "country": "Türkiye", "population": "undergraduate GenAI users (EFA sample)" }, { "n": 313, "country": "Türkiye", "population": "undergraduate GenAI users from five universities (CFA sample)" } ] }, "status": "published", "citation": "Durak, G., Çankaya, S., & Öncü, S. (2026). A theory-driven scale for assessing text-based generative AI literacy from a self-efficacy perspective (T-GASE). Education and Information Technologies, 31(14), 5671–5694. https://doi.org/10.1007/s10639-026-14023-y", "authors": "Gürhan Durak; Serkan Çankaya; Semiral Öncü", "year": 2026, "venue": "Education and Information Technologies", "doi": "10.1007/s10639-026-14023-y", "url": "https://link.springer.com/article/10.1007/s10639-026-14023-y", "preprint_url": null, "items_available": true, "language": "Turkish", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "tai-e", "name": "Trust in AI-Epistemic Scale", "acronym": "TAI-E", "summary": "A 12-item measure of epistemic trust in generative AI as a source of knowledge, with three separately scored parts: treating AI as an authority, seeing its answers as reliable, and suspending one's own critical judgment. Use it to study well-founded versus uncritical reliance on GenAI for information. The author advises against using a single total score.", "target": "Generative AI as an information source. Items say 'AI', but the questionnaire context explicitly refers to ChatGPT, Claude, Gemini, Copilot and Midjourney", "constructs": [ "trust", "credibility", "reliance" ], "populations": [ "university students" ], "items": 12, "response": "6-point Likert (1 = strongly disagree to 6 = strongly agree); Suspension of Critical Judgment items reverse-coded", "subscales": [ { "name": "Attribution of epistemic authority", "items": 4, "description": "Treating GenAI as a legitimate authority, e.g., more competent than many human experts." }, { "name": "Perceived epistemic reliability", "items": 4, "description": "Judging GenAI answers as accurate, up to date and a stable reference point." }, { "name": "Suspension of critical judgment", "items": 4, "description": "Taking in AI answers without checking them (reverse-coded); related negatively to the authority and reliability subscales." } ], "psychometrics": { "structure": "22-item pool reduced to 12 by EFA. Calibration EFA (PAF, oblimin, n=206): 3 factors, 66.7% of variance, loadings .59–.92. CFA (MLR): validation half (n=206) CFI=.916, TLI=.891, RMSEA=.099; full sample (N=412) CFI=.944, TLI=.927, SRMR=.048, RMSEA=.085. Total score not supported (12-item α .47)", "reliability": "Authority α .85 / ω .90; Reliability α .84 / ω .89; Suspension α .87 / ω .91", "validity": [ "Discriminant validity: HTMT ≤ .73 among subscales; Fornell–Larcker criterion met; Suspension distinct from self-reported reflective processing (HTMT .40)", "Criterion associations: authority (β = −.23) and reliability (β = −.15) predicted AI-use frequency; suspension predicted age (β = .15)", "Construct-validity correlations with self-reported reflective processing, sense of coherence, and the Cognitive Reflection Test (self-report vs CRT r = .14)" ], "samples": [ { "n": 412, "country": "Hungary", "population": "Part-time higher-education students who use GenAI, ages 18–63 (M 36.6; 62% women)" } ] }, "status": "published", "citation": "Balázs, L. (2026). Epistemic trust in generative AI as an information source: Development and validation of the Trust in AI-Epistemic Scale (TAI-E). Social Sciences, 15(7), 433. https://doi.org/10.3390/socsci15070433", "authors": "László Balázs", "year": 2026, "venue": "Social Sciences (MDPI)", "doi": "10.3390/socsci15070433", "url": "https://www.mdpi.com/2076-0760/15/7/433", "preprint_url": null, "items_available": true, "language": "Hungarian", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "taigha", "name": "Trust in AI-Generated Health Advice Scale (and short form TAIGHA-S)", "acronym": "TAIGHA", "summary": "A 10-item measure of how much a person trusts, and separately distrusts, a specific piece of health advice written by an AI such as an LLM chatbot. A 4-item short form (TAIGHA-S) is also available. Use it in studies where people receive AI health advice and you want trust and distrust as separate outcomes.", "target": "AI-generated health advice from LLMs (the validation study used advice generated with OpenAI o3)", "constructs": [ "trust", "health", "reliance" ], "populations": [ "general adults" ], "items": 10, "response": "5-point Likert", "subscales": [ { "name": "Trust", "items": 5, "description": "Cognitive and affective trust in the AI's advice, e.g., feeling comfortable following it and believing it leads to better health decisions." }, { "name": "Distrust", "items": 5, "description": "Cognitive and affective distrust, e.g., feeling uneasy about following the advice, seeing health risks, or feeling tense about relying on it." } ], "psychometrics": { "structure": "Items generated with GenAI (AI-GENIE approach), then reduced from 28 to 10 using expert ratings. Content validity from experts (S-CVI/Ave = .99), face validity from lay raters (S-FVI/Ave = .99). CFA two-factor model: CFI = .98, TLI = .98, NFI = .97, RMSEA = .07, SRMR = .03. TAIGHA-S (2 trust + 2 distrust items): acceptable CFA fit, loadings .84–.90", "reliability": "Trust α .94 / ω .94; distrust α .93 / ω .93; total α .95 / ω .96. TAIGHA-S: trust α .88 / ω .89; distrust α .84 / ω .85; overall α .88 / ω .93", "validity": [ "Convergent: Trust in Automated Systems r = .67 (trust) / −.66 (distrust); Propensity to Trust r = .53 / −.46", "Criterion: correlation with actual reliance on the AI advice, r = .35 (trust) and −.19 (distrust); stronger than general trust scales", "Divergent: |r| ≤ .25 with reading flow and NASA-TLX mental demand", "Short form correlates r = .96 with the full scale" ], "samples": [ { "n": 385, "country": "United Kingdom", "population": "Adults given AI health advice in a symptom-assessment vignette (393 recruited, 8 excluded)" }, { "n": 30, "country": "United Kingdom", "population": "Lay participants (face validity)" }, { "n": 10, "country": "not reported", "population": "Domain experts (content validity)" } ] }, "status": "published", "citation": "Kopka, M., Majeed, A., Spinelli, G., El-Osta, A., & Feufel, M. (2026). The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study. PLOS Digital Health, 5(7), e0001488. https://doi.org/10.1371/journal.pdig.0001488", "authors": "Marvin Kopka, Azeem Majeed, Gabriella Spinelli, Austen El-Osta, Markus Feufel", "year": 2026, "venue": "PLOS Digital Health", "doi": "10.1371/journal.pdig.0001488", "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001488", "preprint_url": "https://arxiv.org/abs/2512.14278", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "tiai", "name": "Trust in Automation adapted for Generative AI", "acronym": "TiAI", "summary": "An adaptation of Körber's Trust in Automation questionnaire to generative AI. It has three dimensions: competence and reliability, familiarity, and caution and uncertainty awareness. Use it when you want to separate confident trust in GenAI from a careful, verification-minded stance (calibrated reliance).", "target": "Generative AI tools", "constructs": [ "trust", "reliance" ], "populations": [ "general adults" ], "items": null, "response": null, "subscales": [ { "name": "Competence and reliability", "items": null, "description": "Belief that GenAI outputs are competent and reliable." }, { "name": "Familiarity", "items": null, "description": "Familiarity with GenAI systems." }, { "name": "Caution and uncertainty awareness", "items": null, "description": "Verification-minded caution about GenAI outputs (built from the originally negatively worded items)." } ], "psychometrics": { "structure": "The original TiA factor structure did not replicate in the GenAI context; EFA gave 3 dimensions", "reliability": "Reported as higher when negatively worded items were kept in their original direction (coefficients not seen)", "validity": [ "Differing associations with GenAI experience: experience predicted competence and familiarity, but was more weakly related to caution" ], "samples": [ { "n": 2169, "country": "not reported in abstract (authors based in Israel)", "population": "Large adult sample" } ] }, "status": "published", "citation": "Alon, L., & Levkovich, I. (2026). Trusting the black box: Adapting a multidimensional measure of trust in generative AI. Computers in Human Behavior: Artificial Humans, 8, 100295. https://doi.org/10.1016/j.chbah.2026.100295", "authors": "Lilach Alon, Inbar Levkovich", "year": 2026, "venue": "Computers in Human Behavior: Artificial Humans", "doi": "10.1016/j.chbah.2026.100295", "url": "https://doi.org/10.1016/j.chbah.2026.100295", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "academic-dependence-genai-korea", "name": "University Students' Academic Dependence on Generative AI Scale (Korea)", "acronym": null, "summary": "A 19-item Korean scale of how far university students functionally depend on generative AI for coursework (not addiction): handing thinking over to AI, over-trusting AI, avoiding evaluation, and feeling helpless without it. Use it to screen dependence levels and plan learning support.", "target": "generative AI in academic tasks", "constructs": [ "dependence", "reliance", "learning" ], "populations": [ "university students" ], "items": 19, "response": null, "subscales": [ { "name": "Cognitive outsourcing (인지적 외주화)", "items": null, "description": "Handing over thinking in academic tasks to GenAI" }, { "name": "Overconfidence in AI (AI에 대한 과신)", "items": null, "description": "Excessive trust in GenAI outputs" }, { "name": "Evaluation-avoidance tendency (평가 회피 경향)", "items": null, "description": "Tendency to avoid evaluation or checking (exact referent not given in abstract)" }, { "name": "Cognitive helplessness (인지적 무력감)", "items": null, "description": "Feeling unable to think or work without GenAI" } ], "psychometrics": { "structure": "72-item pool, cut to 24 after expert content validation; EFA (pilot survey) and CFA (main survey) gave 4 factors and 19 items with adequate loadings", "reliability": "Acceptable (values not in abstract)", "validity": [ "Expert content validity", "Convergent validity (acceptable)", "Discriminant validity (acceptable)" ], "samples": [ { "n": null, "country": "South Korea", "population": "university students using generative AI" } ] }, "status": "published", "citation": "Kim, G.-Y., Oh, H.-H., Yoon, S.-E., Lee, K.-N., & Han, C.-Y. (2026). Development and validation of a scale to measure university students' academic dependence on generative artificial intelligence [대학생의 생성형 AI 학업 의존도 측정 도구 개발 연구]. Journal of Social Sciences, 19(1), 315–355. https://doi.org/10.54540/jss19.1.10", "authors": "Ga-young Kim; Hyun-ha Oh; So-eun Yoon; Kyu-na Lee; Chae-yeon Han", "year": 2026, "venue": "Journal of Social Sciences (사회과학연구), Institute of Social Sciences, Incheon National University", "doi": "10.54540/jss19.1.10", "url": "https://doi.org/10.54540/jss19.1.10", "preprint_url": null, "items_available": false, "language": "Korean", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "aifl-ai-feedback-literacy", "name": "AI Feedback Literacy Scale", "acronym": "AIFL", "summary": "Measures how well EFL university students can interpret, evaluate and act on AI-generated feedback on their English writing. It captures both behavioural engagement and attitudes. Useful for research on students' uptake of AI feedback.", "target": "generative AI feedback on writing", "constructs": [ "literacy", "learning" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Behavioural engagement", "items": null, "description": "Actively working with AI-generated feedback (named in abstract as a component; exact subscale structure not verified)" }, { "name": "Attitudinal disposition", "items": null, "description": "Attitudes and readiness toward AI-generated feedback (named in abstract; exact subscale structure not verified)" } ], "psychometrics": { "structure": "CFA (details not in abstract)", "reliability": "Not reported in abstract", "validity": [ "Predictive validity: AIFL predicts uptake of AI-generated feedback directly and through motivational appraisals", "AIFL related to frequency of AI use but not to demographics" ], "samples": [ { "n": 486, "country": "China (inferred from author affiliation; not stated in abstract)", "population": "EFL university students" } ] }, "status": "published", "citation": "Liu, K., & Deris, F. D. (2025). AI feedback literacy in higher education: Understanding, measuring, and predicting student feedback uptake. Assessment & Evaluation in Higher Education. Advance online publication. https://doi.org/10.1080/02602938.2025.2587924", "authors": "Ke Liu; Farhana Diana Deris", "year": 2025, "venue": "Assessment & Evaluation in Higher Education", "doi": "10.1080/02602938.2025.2587924", "url": "https://doi.org/10.1080/02602938.2025.2587924", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "Validated with CFA and predicts uptake of AI feedback, but the factor structure, item count and reliability could not be confirmed.", "verified": "2026-09-29" }, { "id": "aicds", "name": "Artificial Intelligence Chatbot Dependence Scale", "acronym": "AICDS / AICD-8", "summary": "A brief 8-item, one-factor measure of everyday dependence on AI chatbots. Use it when you need a very short chatbot-dependence score; Turkish and Spanish versions exist.", "target": "AI chatbots (daily-life use)", "constructs": [ "dependence" ], "populations": [ "general adults", "university students" ], "items": 8, "response": "7-point Likert-type (as described by the Spanish adaptation); the Turkish AICD-8-T used a 5-point format", "subscales": [ { "name": "(unidimensional)", "items": 8, "description": "Psychological and behavioral dependence on AI chatbots in daily life." } ], "psychometrics": { "structure": "Single factor; 17 items went through item analysis and EFA, leaving 8 items (58.42% variance); CFA acceptable fit (standardized loadings .50–.76)", "reliability": "α .88; CR .79 (as reported by the Turkish adaptation citing the original)", "validity": [ "The abstract states that validity analyses were performed and were 'good'; the specific evidence (e.g., convergent or criterion) was not seen" ], "samples": [ { "n": null, "country": "China", "population": "adults/chatbot users (sample size not seen)" } ] }, "status": "published", "citation": "Zhang, X., Yin, M., Zhang, M., Li, Z., & Li, H. (2025). The development and validation of an artificial intelligence chatbot dependence scale. Cyberpsychology, Behavior, and Social Networking, 28(2), 126–131. https://doi.org/10.1089/cyber.2024.0240", "authors": "Xing Zhang; Mingyue Yin; Mingyang Zhang; Zhaoqian Li; Hansen Li", "year": 2025, "venue": "Cyberpsychology, Behavior, and Social Networking", "doi": "10.1089/cyber.2024.0240", "url": "https://doi.org/10.1089/cyber.2024.0240", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [ { "language": "Spanish", "country": "Paraguay", "citation": "Barrios, I., Torales, J., Blanco, S., Giménez-Barboza, C. M., Monges-Guerrero, B. M., Verón-Otero, A., Estigarribia Sanabria, G. M., Giménez-Legal, E. N., Castaldelli-Maia, J. M., & Ventriglio, A. (2026). Artificial intelligence chatbot dependence, anxiety, and depressive symptoms in university students: Initial psychometric evaluation of the Artificial Intelligence Chatbot Dependence Scale (AICDS) in Spanish. Alpha Psychiatry, 27(4). https://doi.org/10.31083/ap53298", "doi": "10.31083/ap53298", "url": "https://doi.org/10.31083/ap53298", "status": "published", "notes": "N=200 university students (Paraguay). CFA supported one factor (CFI .994, TLI .992, RMSEA .081, SRMR .054); loadings .538–.873; α .833, ω .903, CR .881. Higher scores in students screening positive for anxiety (d=.29), not significant after adjustment. Full text seen (PMC13540032)." }, { "language": "Turkish", "country": "Turkey", "citation": "Özsoy, E., Arslan, K. Ş., Onay, Ö. A., Koç, S., Yiğit, B., & Griffiths, M. D. (2026). Psychometric evaluation of the Turkish version of the AI Chatbot Dependence Scale (AICD-8). Discover Psychology, 6(1), 256. https://doi.org/10.1007/s44202-026-00801-9", "doi": "10.1007/s44202-026-00801-9", "url": "https://link.springer.com/article/10.1007/s44202-026-00801-9", "status": "published", "notes": "AICD-8-T; N=600 (249 employees, 351 students). CFA single factor (CFI .97, TLI .96, RMSEA .07); α .86, ω and CR .86–.89. Convergent r=.68 with PCUS; nomological correlations with social media addiction, internet addiction and loneliness; criterion validity with daily chatbot and ChatGPT use. Publisher page seen." }, { "language": "Turkish", "country": "Turkey", "citation": "Çakmak, B., Özdemir, E., Koç, Ö., & Doğutepe, E. (2026). From prompts to dependency: Standardization of the AI Chatbot Dependence Scale in a Turkish context and associations with Big Five personality traits. International Journal of Human–Computer Interaction, 1–15. https://doi.org/10.1080/10447318.2026.2629519", "doi": "10.1080/10447318.2026.2629519", "url": "https://doi.org/10.1080/10447318.2026.2629519", "status": "published", "notes": "N=819 university students aged 18–36; refined to a 6-item version; α .86; temporal stability reported; women scored higher; neuroticism a predictor. OpenAlex abstract only." } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "aimhs", "name": "Artificial Intelligence in Mental Health Scale", "acronym": "AIMHS", "summary": "A short 5-item measure of attitudes toward using AI chatbots for mental health support, covering technical advantages and personal advantages such as easier access to care. Useful for quick surveys of openness to chatbot-based mental health help.", "target": "AI chatbots for mental health support", "constructs": [ "attitudes", "health", "acceptance-use" ], "populations": [ "general adults" ], "items": 5, "response": "5-point Likert (strongly disagree to strongly agree)", "subscales": [ { "name": "Technical advantages", "items": 2, "description": "Perceived technical strengths of mental health chatbots (e.g., problem-solving compared with a human therapist)" }, { "name": "Personal advantages", "items": 3, "description": "Chatbots widen access to mental health care (geographic, financial, continuous availability)" } ], "psychometrics": { "structure": "14 candidate items; EFA (n = 214) gave a two-factor, five-item model (81.3% variance); CFA (n = 214) confirmed it", "reliability": "α .798 total (.728 technical, .881 personal); 5-day test–retest ICC .938; item κ .76–.85", "validity": [ "Concurrent: r = .405 with AIAS-4, .401 with ATAI acceptance, −.151 with ATAI fear, .450 with S-TIAS", "Configural, metric and scalar invariance across gender, age and daily use" ], "samples": [ { "n": 428, "country": "Greece", "population": "Adults using AI chatbots, social media or websites ≥ 30 min/day (split 214/214 for EFA/CFA)" }, { "n": 50, "country": "Greece", "population": "Test–retest subsample" } ] }, "status": "published", "citation": "Katsiroumpa, A., Konstantakopoulou, O., Moisoglou, I., Gallos, P., Galani, O., Lialiou, P., Tsiachri, M., & Galanis, P. (2025). Development and validation of the Artificial Intelligence in Mental Health Scale: Application for AI mental health chatbots. Healthcare, 13(24), 3269. https://doi.org/10.3390/healthcare13243269", "authors": "Katsiroumpa, A., Konstantakopoulou, O., Moisoglou, I., Gallos, P., Galani, O., Lialiou, P., Tsiachri, M., & Galanis, P.", "year": 2025, "venue": "Healthcare", "doi": "10.3390/healthcare13243269", "url": "https://doi.org/10.3390/healthcare13243269", "preprint_url": null, "items_available": true, "language": "Greek", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "at-llm", "name": "Attitudes Toward General LLMs and Attitudes Toward Primary LLM scales", "acronym": "AT-GLLM / AT-PLLM", "summary": "Two parallel 5-item scales, re-worded from the ATAI, of acceptance (trust, benefit) and fear (destruction, job loss) toward LLMs in general and toward the LLM a person uses most. Use them as brief attitude measures in surveys of LLM users.", "target": "Large language models (general; respondent's primary LLM)", "constructs": [ "attitudes", "trust", "anxiety" ], "populations": [ "general adults" ], "items": 10, "response": "11-point (0 = strongly disagree to 10 = strongly agree)", "subscales": [ { "name": "Acceptance (each version)", "items": 2, "description": "Trust in LLMs and belief they will benefit humankind" }, { "name": "Fear (each version)", "items": 3, "description": "Fear of LLMs, belief they will destroy humankind, and expected job losses" } ], "psychometrics": { "structure": "Split-half design: EFA, then CFA (n = 263) supporting a two-factor (acceptance, fear) structure for each 5-item version; nonparametric isotonic monotonicity checks", "reliability": "AT-GLLM α .79/.78, ω .79/.78, CR .83/.79; AT-PLLM α .75/.74, ω .78/.75, CR .75/.86", "validity": [ "Convergent validity (AVE) and discriminant validity (Fornell–Larcker)", "Configural, metric, scalar and strict invariance across gender (MG-CFA)", "Associations with general AI attitudes (ATAI)" ], "samples": [ { "n": 526, "country": "United Kingdom", "population": "Adult LLM users aged 18–45 (Prolific)" } ] }, "status": "preprint", "citation": "Liebherr, M., Almourad, M. B., Alshakhsi, S., Montag, C., Xu, G., Ali, R., & Yankouskaya, A. (2025). Developing and validating the Attitudes Toward Large Language Models Scale [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/6yb5h_v2", "authors": "Liebherr, M., Almourad, M. B., Alshakhsi, S., Montag, C., Xu, G., Ali, R., & Yankouskaya, A.", "year": 2025, "venue": "PsyArXiv", "doi": "10.31234/osf.io/6yb5h_v2", "url": "https://doi.org/10.31234/osf.io/6yb5h_v2", "preprint_url": "https://doi.org/10.31234/osf.io/6yb5h_v2", "items_available": true, "language": "English", "adaptations": [ { "language": "Chinese", "country": "China", "citation": "AlShakhsi, S., Yankouskaya, A., Yang, H., Wang, X., Chen, J., Ma, T. Y., Xu, G., Montag, C., & Ali, R. (2026). Developing and validating the Chinese version of the Attitudes Toward Large Language Models Scale (AT-LLM Chinese) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-8457517/v1", "doi": "10.21203/rs.3.rs-8457517/v1", "url": "https://doi.org/10.21203/rs.3.rs-8457517/v1", "status": "preprint", "notes": "576 Chinese LLM users; CFA two-factor for both scales; α .54–.74 (lowest for the 2-item acceptance subscale); configural to strict invariance across low- vs high-frequency users; convergent validity with ATAI. Crossref abstract only." }, { "language": "Arabic", "country": "Arabic-speaking countries (not specified)", "citation": "Barajeeh, B., Yankouskaya, A., AlShakhsi, S., Ho, C. S. M., Xu, G., & Ali, R. (2026). Developing and validating the Arabic version of the Attitudes Toward Large Language Models Scale. SN Computer Science, 7(5), 434. https://doi.org/10.1007/s42979-026-04855-3", "doi": "10.1007/s42979-026-04855-3", "url": "https://doi.org/10.1007/s42979-026-04855-3", "status": "published", "notes": "249 Arabic-speaking adults; two-factor structure; gender invariance; α .67–.75; convergent and discriminant validity. Crossref abstract only." } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "chatgpt-proficiency-scale-cps", "name": "ChatGPT Proficiency Scale", "acronym": "CPS", "summary": "A 46-item, 7-dimension self-report scale of how skilled undergraduates are at using ChatGPT: communication, research, language tasks, skill-building, advanced features, assignments/exams, and checking whether ChatGPT's output is valid.", "target": "ChatGPT", "constructs": [ "literacy", "self-efficacy", "academic-integrity" ], "populations": [ "university students" ], "items": 46, "response": null, "subscales": [ { "name": "Communication and collaboration proficiency", "items": null, "description": "Using ChatGPT to communicate and collaborate" }, { "name": "Research-related proficiency", "items": null, "description": "Using ChatGPT for research tasks" }, { "name": "Language-related proficiency", "items": null, "description": "Using ChatGPT for language tasks" }, { "name": "(Soft) skills development", "items": null, "description": "Developing skills with ChatGPT" }, { "name": "Advanced features utilization proficiency", "items": null, "description": "Using ChatGPT's advanced features" }, { "name": "Assignments and exam-related proficiency", "items": null, "description": "Using ChatGPT for assignments and exams" }, { "name": "Assessing the validity of ChatGPT-generated text", "items": null, "description": "Judging the accuracy and validity of ChatGPT output" } ], "psychometrics": { "structure": "Content validation cut 55 items to 46; EFA; CFA (n=347): CFI .890, NFI .804, GFI .898, RMSEA .044, SRMR .046; loadings .34–.75", "reliability": "α .86–.92 per factor", "validity": [ "Content validity (expert opinion and student feedback)", "Construct (factorial) validity via EFA/CFA" ], "samples": [ { "n": 347, "country": "Pakistan (inferred from journal domain; not verified)", "population": "undergraduate students" } ] }, "status": "published", "citation": "Akram, M., Malik, M. A., & Ilgan, A. (2025). Development and validation of ChatGPT Proficiency Scale (CPS). Educational Research and Innovation. https://doi.org/10.66857/eri2-cffa", "authors": "M. Akram; M. A. Malik; A. Ilgan", "year": 2025, "venue": "Educational Research and Innovation", "doi": "10.66857/eri2-cffa", "url": "https://doi.org/10.66857/eri2-cffa", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "Only content and factorial validity are reported, and CFA fit (CFI .89) is below conventional cut-offs.", "verified": "2026-09-29" }, { "id": "chatgpt-usage-scale-fl-learners", "name": "ChatGPT Usage Scale for Foreign Language Learners", "acronym": null, "summary": "An 18-item scale of how foreign-language students use ChatGPT and see its value for usability, learning and language development. Use it in language-education research.", "target": "ChatGPT", "constructs": [ "acceptance-use", "learning" ], "populations": [ "university students" ], "items": 18, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Overall Usability", "items": 3, "description": "How easy, regular and effective one's ChatGPT use is" }, { "name": "Learning", "items": 7, "description": "Using ChatGPT to learn new information, study for exams, access materials and build linguistic/communication skills" }, { "name": "Development", "items": 8, "description": "Using ChatGPT to build critical/creative thinking, motivation, enjoyment, self-confidence and self-efficacy in language learning" } ], "psychometrics": { "structure": "EFA gave 3 factors (62.9% variance; KMO .927); CFA supported the 18-item, 3-factor model after 2 modifications (CMIN/df 5.54, CFI .906, IFI .906, GFI .87, RMSEA .086)", "reliability": "α .93 (total); Overall Usability .83, Learning .89, Development .84; CR > .70; test–retest r = .82, ICC .785 single / .897 average (4 weeks, n=102)", "validity": [ "Convergent validity (AVE > .50, CR > AVE)", "Discriminant validity (MSV/ASV < AVE; √AVE > inter-factor correlations)", "Content validity (Lawshe CVR, 10 experts)" ], "samples": [ { "n": 1006, "country": "Türkiye", "population": "students in Arabic, German, French and English language departments at three state universities" } ] }, "status": "published", "citation": "Çobanoğulları, F., & Özbek, Ö. (2025). AI-powered language learning: Developing the ChatGPT usage scale for foreign language learners. Education and Information Technologies, 30(9), 12517–12534. https://doi.org/10.1007/s10639-025-13342-w", "authors": "Ferdiye Çobanoğulları, Özge Özbek", "year": 2025, "venue": "Education and Information Technologies", "doi": "10.1007/s10639-025-13342-w", "url": "https://doi.org/10.1007/s10639-025-13342-w", "preprint_url": null, "items_available": true, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "chatgpt-usage-scale-education", "name": "ChatGPT Usage Scale in Education", "acronym": null, "summary": "A 12-item scale of attitudes toward using ChatGPT, split into perceived opportunities and challenges. Use it with educators, school administrators or organisations.", "target": "ChatGPT", "constructs": [ "attitudes", "acceptance-use" ], "populations": [ "employees & professionals", "teachers & academics" ], "items": 12, "response": null, "subscales": [ { "name": "Opportunities", "items": null, "description": "Perceived benefits of ChatGPT use" }, { "name": "Challenges", "items": null, "description": "Perceived problems and risks of ChatGPT use" } ], "psychometrics": { "structure": "EFA on a 13-item draft (n=213) reduced it to 12 items; CFA in a second sample (n=175) supported a two-factor model with acceptable fit", "reliability": "α .71 (total); test–retest correlations reported (values not in abstract)", "validity": [ "Criterion validity examined with 67 school administrators and teachers", "Independent-samples t-tests, item-total/item-remainder correlations and inter-factor correlations" ], "samples": [ { "n": 213, "country": "Not stated in abstract", "population": "EFA sample" }, { "n": 175, "country": "Not stated in abstract", "population": "CFA sample" }, { "n": 67, "country": "Not stated in abstract", "population": "school administrators and teachers (test–retest/criterion)" } ] }, "status": "published", "citation": "Taktak, M., & Bafrali, G. (2025). ChatGPT usage scale in education: Validity and reliability study. International Journal of Technology in Education, 8(1), 193–207. https://doi.org/10.46328/ijte.1024", "authors": "Mustafa Taktak, Gorsev Bafrali", "year": 2025, "venue": "International Journal of Technology in Education", "doi": "10.46328/ijte.1024", "url": "https://doi.org/10.46328/ijte.1024", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "cl-ai-l2w", "name": "Cognitive Load Scale for AI-assisted L2 Writing", "acronym": "CL-AI-L2W", "summary": "An 18-item scale of how much mental effort second-language writers spend when writing with generative AI chatbots. It covers managing prompts, checking AI output, blending AI text into their own, and keeping their own authorship. Use it in studies of how GenAI changes the demands of L2 writing.", "target": "generative AI chatbots (e.g., ChatGPT, Bing Chat, DeepSeek) in L2 writing", "constructs": [ "learning", "other" ], "populations": [ "university students" ], "items": 18, "response": "7-point mental-effort rating (1 = very, very low to 7 = very, very high mental effort)", "subscales": [ { "name": "Prompt Management", "items": 5, "description": "Effort spent phrasing, refining and managing prompts" }, { "name": "Critical Evaluation", "items": 5, "description": "Effort judging the accuracy and quality of AI output" }, { "name": "Integrative Synthesis", "items": 4, "description": "Effort integrating AI content into one's own text" }, { "name": "Authorial Core Processing", "items": 4, "description": "Effort on one's own ideas, voice and authorship" } ], "psychometrics": { "structure": "EFA (n=241, 35-item pool) gave 4 factors and 18 items. CFA (n=305): χ²/df 2.06, CFI .97, TLI .96, RMSEA .059, WRMR .95.", "reliability": "α .94 total (PM .91, CE .92, IS .89, ACP .87); ω .87–.93", "validity": [ "Convergent validity: AVE .63–.70", "Discriminant validity: HTMT .59–.81", "Criterion validity: r = .72 with overall perceived mental effort, .45 with writing anxiety, -.51 with writing self-efficacy" ], "samples": [ { "n": 241, "country": "China", "population": "non-English-major university students, intermediate-high English proficiency (EFA)" }, { "n": 305, "country": "China", "population": "non-English-major students at a different university (CFA)" } ] }, "status": "published", "citation": "Yao, G., & Fan, L. (2025). Cognitive load scale for AI-assisted L2 writing: Scale development and validation. Frontiers in Psychology, 16, 1666974. https://doi.org/10.3389/fpsyg.2025.1666974", "authors": "Guangyuan Yao; Lingxi Fan", "year": 2025, "venue": "Frontiers in Psychology", "doi": "10.3389/fpsyg.2025.1666974", "url": "https://doi.org/10.3389/fpsyg.2025.1666974", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "collaborative-ai-literacy-scale", "name": "Collaborative AI Literacy Scale", "acronym": "CAIL", "summary": "Measures how well people work with conversational GenAI as a collaborator: judging its outputs, steering it to get what they need, and making ethical decisions. Use it with adult users of tools like ChatGPT or Copilot, often alongside the companion Collaborative AI Metacognition Scale.", "target": "Collaborative/conversational generative AI tools", "constructs": [ "literacy", "learning", "other" ], "populations": [ "general adults" ], "items": null, "response": null, "subscales": [ { "name": "AI evaluation", "items": null, "description": "Critically evaluating and building on AI output" }, { "name": "AI usage", "items": null, "description": "Directing the AI to get the output you want" }, { "name": "AI ethics", "items": null, "description": "Making ethical decisions when working with AI" } ], "psychometrics": { "structure": "Three factors tested with SEM/CFA (factor names from a secondary summary)", "reliability": "The abstract reports 'strong reliability'. A secondary summary reports α .92 and Raykov's ρ .94 (not verified in the primary text).", "validity": [ "Convergent and discriminant validity supported by SEM", "Predictive validity: correlated with user-reported benefits of AI collaboration", "Incremental: explained variance beyond general metacognition" ], "samples": [ { "n": 292, "country": "Not confirmed (authors at CSIRO, Australia)", "population": "users of collaborative AI tools" } ] }, "status": "published", "citation": "Sidra, S., & Mason, C. (2025). Generative AI in human-AI collaboration: Validation of the Collaborative AI Literacy and Collaborative AI Metacognition scales for effective use. International Journal of Human–Computer Interaction, 42(7), 5084–5108. https://doi.org/10.1080/10447318.2025.2543997", "authors": "Sidra Sidra; Claire Mason", "year": 2025, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2025.2543997", "url": "https://www.tandfonline.com/doi/full/10.1080/10447318.2025.2543997", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "caids", "name": "Conversational AI Dependence Scale", "acronym": "CAIDS", "summary": "A 20-item, addiction-framework measure of conversational AI dependence in Chinese college students, covering uncontrollability, withdrawal, mood modification and negative impacts. Use it when you want a detailed, symptom-style profile of chatbot dependence in students.", "target": "Conversational AI (chatbots, intelligent assistants, LLM-based CAI)", "constructs": [ "dependence", "health" ], "populations": [ "university students" ], "items": 20, "response": "6-point Likert (1 = completely disagree to 6 = completely agree); 'In the past year' reference", "subscales": [ { "name": "Uncontrollability", "items": 5, "description": "Salience, tolerance and using CAI longer or more often than intended." }, { "name": "Withdrawal symptoms", "items": 5, "description": "Irritability, anxiety, loss or helplessness when CAI is unavailable." }, { "name": "Mood modification", "items": 4, "description": "Using CAI to relieve negative emotions, irritability or loneliness." }, { "name": "Negative impact", "items": 6, "description": "Harms to studies, social relationships, hobbies and independent thinking." } ], "psychometrics": { "structure": "Four factors; EFA (N=547, 74.41% variance) then CFA (N=687; χ²/df 4.39, CFI .92, TLI .91, RMSEA .07, SRMR .06)", "reliability": "α .86 (total), .88–.94 (subscales); split-half .81 (total), .77–.90 (subscales); CR .86–.91", "validity": [ "Convergent: loadings > .50, AVE .55–.72", "Discriminant: Fornell–Larcker criterion met (inter-factor r .50–.77 < √AVE)", "Criterion: correlations with social media addiction (BSMAS, r=.51), loneliness (r=.35) and insecure attachment (r=.39)", "Predictive/nomological (Study 3): predicted sleep problems, functional difficulties, depression, anxiety and stress, and lower subjective wellbeing" ], "samples": [ { "n": 31, "country": "China", "population": "high-dependence college students (qualitative interviews; 64 screened)" }, { "n": 547, "country": "China", "population": "college students (EFA)" }, { "n": 687, "country": "China", "population": "college students (CFA, criterion validity)" }, { "n": 1081, "country": "China", "population": "college students (Study 3, sample 1)" }, { "n": 892, "country": "China", "population": "college students (Study 3, sample 2)" } ] }, "status": "published", "citation": "Chen, Y., Wang, M., Yuan, S., & Zhao, Y. (2025). Development and validation of the conversational AI dependence scale for Chinese college students. Frontiers in Psychology, 16, 1621540. https://doi.org/10.3389/fpsyg.2025.1621540", "authors": "Yuanyuan Chen; Mengyun Wang; Shujuan Yuan; Yan Zhao", "year": 2025, "venue": "Frontiers in Psychology", "doi": "10.3389/fpsyg.2025.1621540", "url": "https://doi.org/10.3389/fpsyg.2025.1621540", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "creative-displacement-anxiety-scale", "name": "Creative Displacement Anxiety Scale", "acronym": "CDA", "summary": "A multidimensional scale of creative professionals' anxiety about being displaced by generative AI: replacement fears, skills becoming obsolete and loss of professional identity. Use it in studies of creative workers facing GenAI; the authors caution that discriminant validity needs more work.", "target": "Generative AI in the creative industries", "constructs": [ "anxiety", "workplace", "creativity" ], "populations": [ "employees & professionals" ], "items": null, "response": null, "subscales": [ { "name": "Seven dimensions (names not fully seen)", "items": null, "description": "EFA found seven dimensions of creative displacement anxiety; the abstract names skills atrophy and job anxiety among them" } ], "psychometrics": { "structure": "Two-phase design: EFA (7 dimensions), then CFA with adequate fit (CFI .948, RMSEA .039)", "reliability": "CR .723–.859", "validity": [ "Discriminant validity problems reported (e.g., overlap between skills atrophy and job anxiety)" ], "samples": [ { "n": null, "country": "not stated in abstract (authors in Hong Kong)", "population": "Creative industry professionals" } ] }, "status": "published", "citation": "Chung, K. Y., Ma, H., & Chan, Y. K. (2025). Measuring creative displacement anxiety in the age of generative AI: Scale development and validation. In IASDR 2025: Design Next. Design Research Society. https://doi.org/10.21606/iasdr.2025.46", "authors": "Ka Yan Chung; Henry Ma; Yuet Kai Chan", "year": 2025, "venue": "IASDR 2025: Design Next (Design Research Society conference proceedings)", "doi": "10.21606/iasdr.2025.46", "url": "https://dl.designresearchsociety.org/iasdr/iasdr2025/fullpapers/16/", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "evidence": "partial", "flag_reason": "The only validity test (discriminant) partly failed, and the authors say more validation is needed.", "verified": "2026-09-29" }, { "id": "ceai-critical-engagement-ai", "name": "Critical Engagement with AI Scale", "acronym": "CEAI", "summary": "A single-score scale of how much students think critically when using generative AI for study, for example questioning and checking AI outputs instead of accepting them. The author notes it needs further refinement.", "target": "generative AI in higher education", "constructs": [ "learning", "literacy" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "(unidimensional)", "items": null, "description": "Critical-thinking behaviours when engaging with GenAI" } ], "psychometrics": { "structure": "Unidimensional (method not specified in abstract)", "reliability": "Strong reliability (values not in abstract)", "validity": [ "Convergent validity", "Discriminant validity" ], "samples": [ { "n": 249, "country": "Cambodia", "population": "higher education students" } ] }, "status": "published", "citation": "Cornet, A. (2025). An exploration and scale validation on critical thinking engagement in context of generative AI. In Applied psychology readings: SCAP 2024 (pp. 139–154). Springer Nature Singapore. https://doi.org/10.1007/978-981-95-1936-1_10", "authors": "Adriaan Cornet", "year": 2025, "venue": "Applied Psychology Readings (SCAP 2024), Springer Proceedings in Behavioral & Health Sciences", "doi": "10.1007/978-981-95-1936-1_10", "url": "https://doi.org/10.1007/978-981-95-1936-1_10", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "crtai-critical-thinking-genai-learning", "name": "Critical Thinking in GenAI-Assisted Learning Scale", "acronym": "CrTAI", "summary": "A 9-item pilot scale of how students think critically while learning with generative AI: reasoning about AI output, checking its sources, and monitoring their own reliance on AI. Good for exploratory research; the pilot sample was small.", "target": "generative AI in learning", "constructs": [ "learning", "literacy" ], "populations": [ "university students" ], "items": 9, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Analytical Reasoning", "items": 3, "description": "Analysing and reasoning about AI-generated responses" }, { "name": "Source Verification and Evaluation", "items": 3, "description": "Checking the sources and credibility of AI-provided information" }, { "name": "Metacognitive Self-Regulation", "items": 3, "description": "Reflecting on and regulating one's reliance on AI" } ], "psychometrics": { "structure": "EFA on 12 items (PAF, oblimin; KMO .90): 3 factors, 63.8% variance; pruned to 9 items; CFA (MLR) χ²(24) = 36.97, CFI .97, TLI .95–.96, RMSEA .07, SRMR .04 (same n=100 sample)", "reliability": "12-item α .93, ω .94; subscales α .86–.91; final 9-item CR .86–.90", "validity": [ "Convergent validity: AVE .67–.75, loadings .74–.90", "Discriminant validity argued from inter-factor r .81–.83 (< .90)", "Content validity via expert review" ], "samples": [ { "n": 100, "country": "Vietnam", "population": "English-major undergraduates (mostly first-year)" } ] }, "status": "published", "citation": "Cong-Lem, N. (2025). Exploring human–AI distributed critical thinking (HADCT): Pilot validation of a critical thinking scale with Vietnamese EFL learners. TEFLIN Journal, 36(2), 279–302. https://doi.org/10.15639/teflinjournal.v36i2/279-302", "authors": "Ngo Cong-Lem", "year": 2025, "venue": "TEFLIN Journal", "doi": "10.15639/teflinjournal.v36i2/279-302", "url": "https://doi.org/10.15639/teflinjournal.v36i2/279-302", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "etgai-he", "name": "Epistemic Trust in Generative AI for Higher Education Scale", "acronym": "ETGAI-HE", "summary": "A six-factor scale of what drives epistemic trust in generative AI among students, researchers and teachers in Indian higher education, such as reasoned appraisal, social influences, safety, transparency, expected performance and user control. Use it to profile trust in GenAI in academic settings.", "target": "Generative AI in higher education", "constructs": [ "trust", "learning", "credibility" ], "populations": [ "university students", "teachers & academics" ], "items": null, "response": null, "subscales": [ { "name": "Cognitive Evaluation of Trustworthiness", "items": null, "description": "Reasoned appraisal of whether GenAI output is trustworthy." }, { "name": "Interpersonal and Contextual Influences", "items": null, "description": "Social and contextual cues that shape trust in GenAI." }, { "name": "Dependability and Safety", "items": null, "description": "Perceived dependability and safety of GenAI systems." }, { "name": "System Predictability & Transparency", "items": null, "description": "Whether GenAI behaves predictably and transparently." }, { "name": "Performance Expectation", "items": null, "description": "Expectations that GenAI will perform well." }, { "name": "User Control and Autonomy", "items": null, "description": "Perceived control and autonomy when using GenAI." } ], "psychometrics": { "structure": "EFA (ML, Promax) then CFA with fit indices (values not seen); six-factor model explained 70.8% of variance", "reliability": "Overall α .823", "validity": [ "Convergent validity via AVE", "Discriminant validity via HTMT" ], "samples": [ { "n": null, "country": "India", "population": "Higher-education students, researchers and teachers" } ] }, "status": "published", "citation": "Pandey, C. S., Mishra, P., Pandey, S. R., & Pandey, S. (2025). Epistemic trust in generative AI for higher education scale (ETGAI-HE scale). AI & Society, 41(2), 1387–1400. https://doi.org/10.1007/s00146-025-02566-6", "authors": "Chandra Shekhar Pandey, Patanjali Mishra, Shri Ram Pandey, Shweta Pandey", "year": 2025, "venue": "AI & Society", "doi": "10.1007/s00146-025-02566-6", "url": "https://link.springer.com/article/10.1007/s00146-025-02566-6", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "llm-ethical-awareness-hcp", "name": "Ethical Awareness in the Use of Large Language Models Scale for Healthcare Professionals", "acronym": null, "summary": "A 21-item scale of healthcare professionals' ethical awareness when using LLMs in clinical practice, covering privacy, consent, transparency, bias, safety and education. Use it to assess readiness for responsible LLM use in hospitals or to evaluate ethics training.", "target": "Large language models in clinical practice", "constructs": [ "ethics-concerns", "privacy", "health" ], "populations": [ "health professionals", "employees & professionals" ], "items": 21, "response": null, "subscales": [ { "name": "Data privacy and confidentiality", "items": null, "description": "Awareness of protecting patient data when using LLMs" }, { "name": "Consent and autonomy", "items": null, "description": "Awareness of informed consent and patient autonomy around LLM use" }, { "name": "Transparency and accountability", "items": null, "description": "Awareness of disclosure and responsibility for LLM-assisted decisions" }, { "name": "Bias and equity", "items": null, "description": "Awareness of biased or inequitable LLM outputs" }, { "name": "Safety and professional integrity", "items": null, "description": "Awareness of patient safety and professional standards when relying on LLMs" }, { "name": "Education and sustainability", "items": null, "description": "Awareness of training needs and sustainable integration of LLMs" } ], "psychometrics": { "structure": "Literature review and interviews produced 36 items, reduced to 21. EFA gave 6 factors (71.5% variance). CFA first-order: CMIN/DF 1.798, CFI .967, RMSEA .050. Second-order: CMIN/DF 2.862, CFI .927, RMSEA .058.", "reliability": "α .90 overall (dimensions .780–.964); composite reliability satisfactory", "validity": [ "Convergent validity and discriminant validity reported as satisfactory", "Moderate significant inter-factor correlations" ], "samples": [ { "n": 658, "country": "Egypt and Saudi Arabia", "population": "Healthcare professionals in nine institutions (five in Egypt, four in Saudi Arabia), 2024" } ] }, "status": "published", "citation": "Asal, M. G. R., Alsenany, S. A., Badoman, T. E. A., & El-Sayed, A. A. I. (2025). Ethical awareness in the use of large language models: Development and validation of a scale for healthcare professionals. Journal of Evaluation in Clinical Practice, 31(5), e70241. https://doi.org/10.1111/jep.70241", "authors": "Maha Gamal Ramadan Asal; Samira Ahmed Alsenany; Talal Emad Ahmed Badoman; Ahmed Abdelwahab Ibrahim El-Sayed", "year": 2025, "venue": "Journal of Evaluation in Clinical Practice", "doi": "10.1111/jep.70241", "url": "https://doi.org/10.1111/jep.70241", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "ehars", "name": "Experiences in Human-AI Relationships Scale", "acronym": "EHARS", "summary": "Applies adult attachment theory to relationships with generative AI such as ChatGPT. It measures attachment anxiety (needing reassurance and affection from AI) and attachment avoidance (discomfort with emotional closeness to AI).", "target": "Generative AI (ChatGPT chosen as the reference system); items refer to 'AI'", "constructs": [ "relationships", "anthropomorphism" ], "populations": [ "general adults" ], "items": 7, "response": "7-point Likert (1 = strongly disagree to 7 = strongly agree)", "subscales": [ { "name": "Attachment anxiety toward AI", "items": 4, "description": "Needing shows of affection, intimacy and reassurance from AI" }, { "name": "Attachment avoidance toward AI", "items": 3, "description": "Preferring not to open up to, or be too close to, AI" } ], "psychometrics": { "structure": "20-item pool; EFA (principal axis, varimax) in pilot study 2 (N = 63) gave 2 factors and 7 items (61.66% variance); CFA in the main study (N = 242): χ²(13) = 15.47, CFI .992, RMSEA .028", "reliability": "α .69 (anxiety), .79 (avoidance) in main study (pilot anxiety α .90); 1-month test–retest r = .69 for both subscales (n = 108)", "validity": [ "Convergent/nomological: AI attachment anxiety correlates with human attachment anxiety (r = .17) and lower self-esteem (r = −.24); AI avoidance correlates with less positive attitudes toward AI (ATAI, r = −.19) and lower use frequency (r = −.18)" ], "samples": [ { "n": 56, "country": "China", "population": "adults (pilot 1, attachment functions)" }, { "n": 63, "country": "China", "population": "adults (pilot 2, EFA)" }, { "n": 242, "country": "China", "population": "adults (CFA and validity; 108 retested after 1 month)" } ] }, "status": "published", "citation": "Yang, F., & Oshio, A. (2025). Using attachment theory to conceptualize and measure the experiences in human-AI relationships. Current Psychology, 44(11), 10658–10669. https://doi.org/10.1007/s12144-025-07917-6", "authors": "Fan Yang; Atsushi Oshio", "year": 2025, "venue": "Current Psychology", "doi": "10.1007/s12144-025-07917-6", "url": "https://doi.org/10.1007/s12144-025-07917-6", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "fame-scale", "name": "FAME Scale (Fear, Anxiety, Mistrust, Ethics) toward generative AI", "acronym": "FAME", "summary": "A 12-item scale of health-sciences students' fear, anxiety, mistrust and ethical concerns about generative AI (e.g., ChatGPT) in healthcare and their future careers. Use it to profile apprehension about GenAI in medical and health education.", "target": "Generative AI models (e.g., ChatGPT) in healthcare", "constructs": [ "anxiety", "ethics-concerns", "trust" ], "populations": [ "university students" ], "items": 12, "response": "5-point agreement scale (agree, somewhat agree, neutral, somewhat disagree, disagree; scored 5–1); construct scores are sums of 3 items", "subscales": [ { "name": "Fear", "items": 3, "description": "Fear about GenAI, e.g., being displaced in one's future healthcare job" }, { "name": "Anxiety", "items": 3, "description": "Anxiety about GenAI's role in one's career and training" }, { "name": "Mistrust", "items": 3, "description": "Distrust of GenAI's ability to replace human-centred aspects of care" }, { "name": "Ethics", "items": 3, "description": "Ethical concerns about using GenAI in healthcare" } ], "psychometrics": { "structure": "Pilot (Jordan, n = 164) reported EFA and CFA. In the multinational validation (n = 587), EFA (ML, oblimin; KMO .872) and CFA supported 4 factors: CFI .966, TLI .953, RMSEA .072, SRMR .047, GFI .991.", "reliability": "Multinational sample: α Fear .879, Anxiety .881, Mistrust .657, Ethics .749; total .877", "validity": [ "Convergent: Spearman correlations with a 3-item STAI-adapted GenAI apprehension scale (α .850): Fear ρ = .653, Anxiety ρ = .638, Ethics ρ = .440, Mistrust ρ = .100", "Pilot: higher general anxiety about genAI was associated with higher Fear, Anxiety and Ethics scores" ], "samples": [ { "n": 164, "country": "Jordan", "population": "Medical students (pilot development study)" }, { "n": 587, "country": "Jordan, Egypt, Iraq, Kuwait, Saudi Arabia (plus 5.5% other)", "population": "Health sciences students (medicine, pharmacy, nursing, dentistry, medical laboratory, rehabilitation)" } ] }, "status": "published", "citation": "Sallam, M., Al-Mahzoum, K., Alaraji, H., Albayati, N., Alenzei, S., AlFarhan, F., Alkandari, A., Alkhaldi, S., Alhaider, N., Al-Zubaidi, D., Shammari, F., Salahaldeen, M., Slehat, A. S., Mijwil, M. M., Abdelaziz, D. H., & Al-Adwan, A. S. (2025). Apprehension toward generative artificial intelligence in healthcare: A multinational study among health sciences students. Frontiers in Education, 10, 1542769. https://doi.org/10.3389/feduc.2025.1542769", "authors": "Malik Sallam; Kholoud Al-Mahzoum; Haya Alaraji; Noor Albayati; Shahad Alenzei; Fai AlFarhan; Aisha Alkandari; Sarah Alkhaldi; Noor Alhaider; Dimah Al-Zubaidi; Fatma Shammari; Mohammad Salahaldeen; Aya Saleh Slehat; Maad M. Mijwil; Doaa H. Abdelaziz; Ahmad Samed Al-Adwan", "year": 2025, "venue": "Frontiers in Education", "doi": "10.3389/feduc.2025.1542769", "url": "https://doi.org/10.3389/feduc.2025.1542769", "preprint_url": "https://doi.org/10.20944/preprints202412.0340.v1", "items_available": true, "language": "Arabic & English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gen-aihe-tpack", "name": "Gen AIHE-TPACK Scale", "acronym": "Gen AIHE-TPACK", "summary": "A 20-item scale, based on the TPACK framework (technology, pedagogy and content knowledge), on how competent higher-education teachers are at teaching with generative AI. Developed with college teachers in India.", "target": "generative AI in higher education teaching", "constructs": [ "literacy", "workplace" ], "populations": [ "teachers & academics" ], "items": 20, "response": null, "subscales": [ { "name": "(TPACK components)", "items": null, "description": "TPACK-based dimensions; names not given in abstract" } ], "psychometrics": { "structure": "Not reported in abstract ('six-fold methodology of scale development and validation')", "reliability": "Not reported in abstract", "validity": [ "Not specified in abstract" ], "samples": [ { "n": 181, "country": "India", "population": "college teachers" } ] }, "status": "published", "citation": "Mandal, S., Bakshi, A., & Sareen, S. (2025). Examining teachers' competencies in generative AI-enabled higher education: Scale development and validation for empirical research. SN Social Sciences, 5(4), Article 36. https://doi.org/10.1007/s43545-025-01068-y", "authors": "Sayantan Mandal; Avantika Bakshi; Sheriya Sareen", "year": 2025, "venue": "SN Social Sciences", "doi": "10.1007/s43545-025-01068-y", "url": "https://doi.org/10.1007/s43545-025-01068-y", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "The abstract reports no factor analysis, reliability or validity statistics, and the full text was inaccessible.", "verified": "2026-09-29" }, { "id": "gaics-l2t", "name": "GenAI Competence Scale for L2 Teachers", "acronym": "GAICS-L2T", "summary": "Measures second/foreign-language teachers' competence with generative AI in four areas: openness to exploring it, knowledge of how it works and its limits, applying it in teaching and assessment, and using it responsibly. It is built on China's teacher digital competence framework and is useful for language-teacher PD research.", "target": "Generative AI in L2 teaching (\"GenAI\" can be replaced with a specific tool name, e.g., ChatGPT)", "constructs": [ "literacy", "workplace", "ethics-concerns" ], "populations": [ "teachers & academics" ], "items": 24, "response": "6-point Likert (1 = strongly disagree to 6 = strongly agree)", "subscales": [ { "name": "Consciousness", "items": 6, "description": "Openness and eagerness to explore GenAI for L2 teaching" }, { "name": "Knowledge", "items": 4, "description": "Knowing GenAI platforms, principles, benefits and limitations" }, { "name": "Application", "items": 10, "description": "Using GenAI for resources, personalization, collaboration, assessment, feedback and student data" }, { "name": "Responsibility", "items": 4, "description": "Legal compliance, intellectual property, avoiding over-reliance, protecting student privacy" } ], "psychometrics": { "structure": "Four factors. EFA (n = 525) explained 78.78% of variance after removing the Professional Development factor and the Skills items; CFA (n = 408) showed excellent fit", "reliability": "Described as strong; coefficients not seen (paywalled)", "validity": [ "Described as having strong validity (details not seen)", "Measurement invariance held across gender but not across school levels" ], "samples": [ { "n": 525, "country": "China", "population": "L2 teachers (EFA phase)" }, { "n": 408, "country": "China", "population": "L2 teachers (CFA phase)" } ] }, "status": "published", "citation": "Wu, H., Zeng, Y., Chen, Z., & Liu, F. (2025). GenAI competence is different from digital competence: Developing and validating the GenAI competence scale for second language teachers. Education and Information Technologies, 30(16), 22567–22591. https://doi.org/10.1007/s10639-025-13672-9", "authors": "Hanwei Wu; Yonghong Zeng; Zhongping Chen; Fen Liu", "year": 2025, "venue": "Education and Information Technologies", "doi": "10.1007/s10639-025-13672-9", "url": "https://link.springer.com/article/10.1007/s10639-025-13672-9", "preprint_url": null, "items_available": true, "language": "Chinese & English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gaius", "name": "GenAI Usage Scale", "acronym": "GAIUS", "summary": "Measures how university students value and use generative AI: its perceived educational value, their current usage patterns, and its perceived usefulness. It was built to link GenAI use to academic dishonesty, so it suits academic-integrity research.", "target": "Generative AI tools (student use)", "constructs": [ "acceptance-use", "academic-integrity" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Perceived Educational Value", "items": null, "description": "How valuable students think GenAI is for learning" }, { "name": "Current Usage Patterns", "items": null, "description": "How and how often students currently use GenAI" }, { "name": "Perceived Utility", "items": null, "description": "How practically useful students find GenAI for their work" } ], "psychometrics": { "structure": "Three dimensions; the abstract reports good model fit (method and indices not seen)", "reliability": "Described as strong in the abstract (values not seen)", "validity": [ "Predictive/nomological: Perceived Utility was the strongest predictor of academic dishonesty; frequent users were less likely to misuse AI" ], "samples": [ { "n": 186, "country": "United Arab Emirates", "population": "undergraduates at a public university" } ] }, "status": "published", "citation": "Shomotova, A., Husain, S. H., & ElSayary, A. (2025). Validation of the GenAI Usage Scale (GAIUS) and the role of demographics in academic dishonesty in higher education. Innovations in Education and Teaching International. Advance online publication. https://doi.org/10.1080/14703297.2025.2603622", "authors": "Aizhan Shomotova; Salwa Habib Husain; Areej ElSayary", "year": 2025, "venue": "Innovations in Education and Teaching International", "doi": "10.1080/14703297.2025.2603622", "url": "https://doi.org/10.1080/14703297.2025.2603622", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "The abstract reports \"strong reliability and model fit\" without methods or values; the scale predicts academic dishonesty.", "verified": "2026-09-29" }, { "id": "genai-attitude-scale-students", "name": "Generative AI Attitude Scale for Students", "acronym": "GAAS", "summary": "A 13-item scale of university students' positive and negative attitudes toward using generative AI tools in education. It is short and quick to use in course or program surveys.", "target": "generative AI tools (e.g., ChatGPT) in education", "constructs": [ "attitudes" ], "populations": [ "university students" ], "items": 13, "response": "5-point Likert (1 = strongly disagree, 5 = strongly agree); total score 13–65", "subscales": [ { "name": "Positive attitude", "items": 8, "description": "Favourable views of GenAI for learning" }, { "name": "Negative attitude", "items": 5, "description": "Concerns and unfavourable views of GenAI in education" } ], "psychometrics": { "structure": "EFA (n=400): 2 factors, 78.44% of variance, loadings .768–.925. CFA (n=264): χ²/df 2.126, CFI .98, TLI .98, IFI .98, GFI .93, RMSEA .065, SRMR .037.", "reliability": "α .84 total (.97 positive, .94 negative); test-retest .90 total (.98 / .96), n=25", "validity": [ "Face/content validity via expert and linguistic review", "Item discrimination (upper vs lower 27%, item-total r)", "Factorial validity via CFA; positive and negative subscales correlate r = -.172" ], "samples": [ { "n": 400, "country": "Türkiye", "population": "undergraduates from 9 faculties and 3 vocational schools at one state university (EFA)" }, { "n": 264, "country": "Türkiye", "population": "undergraduates, same university (CFA)" }, { "n": 25, "country": "Türkiye", "population": "undergraduates (test-retest)" } ] }, "status": "published", "citation": "Marengo, A., Karaoglan-Yilmaz, F. G., Yılmaz, R., & Ceylan, M. (2025). Development and validation of generative artificial intelligence attitude scale for students. Frontiers in Computer Science, 7, 1528455. https://doi.org/10.3389/fcomp.2025.1528455", "authors": "Agostino Marengo; Fatma Gizem Karaoglan-Yilmaz; Ramazan Yılmaz; Mehmet Ceylan", "year": 2025, "venue": "Frontiers in Computer Science", "doi": "10.3389/fcomp.2025.1528455", "url": "https://doi.org/10.3389/fcomp.2025.1528455", "preprint_url": "https://doi.org/10.2139/ssrn.4791135", "items_available": true, "language": "English & Turkish", "adaptations": [], "evidence": "partial", "flag_reason": "The full text confirms there are no convergent, discriminant, criterion, invariance or known-groups analyses.", "verified": "2026-09-29" }, { "id": "generative-ai-dependency-scale", "name": "Generative AI Dependency Scale", "acronym": "GAIDS", "summary": "An 11-item measure of dependency on generative AI tools (e.g., ChatGPT, Replika, DALL-E) with three facets: cognitive preoccupation, negative consequences and withdrawal. Good for adult or student samples when you want a short, well-validated multidimensional dependency score.", "target": "Generative AI tools in general (ChatGPT, Replika, DALL-E named in preamble)", "constructs": [ "dependence" ], "populations": [ "university students", "general adults" ], "items": 11, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Cognitive preoccupation", "items": 3, "description": "Salience and compulsive urges to use generative AI, even when unnecessary." }, { "name": "Negative consequences", "items": 4, "description": "Perceived harms: worry, trouble working without GenAI, lower confidence and worse problem-solving." }, { "name": "Withdrawal", "items": 4, "description": "Restlessness or feeling unsettled when GenAI cannot be used." } ], "psychometrics": { "structure": "Three correlated factors with a higher-order dependency factor; EFA (Study 2) then CFA (Study 3, replicated in Study 5)", "reliability": "α .92–.93; one-week test-retest ICC .87", "validity": [ "Convergent: r = .85 with the 3-item Generative AI Addiction scale", "Discriminant: weak associations with Big Five traits", "Measurement invariance: scalar across sex and across Singapore vs US samples", "Nomological: related to lower need satisfaction, higher FoMO, procrastination, cognitive failures, loneliness, lower self-concept clarity, critical thinking and task performance" ], "samples": [ { "n": 160, "country": "Singapore", "population": "university students (Studies 1a–1c item development)" }, { "n": 203, "country": "Singapore", "population": "university students (EFA)" }, { "n": 410, "country": "United States", "population": "adults via CloudResearch Connect (CFA, invariance)" }, { "n": 168, "country": "Singapore", "population": "university students (test-retest)" }, { "n": 131, "country": "Singapore", "population": "university students (convergent/discriminant validity)" }, { "n": 261, "country": "Not confirmed", "population": "adults (correlates study)" } ] }, "status": "published", "citation": "Goh, A. Y. H., Hartanto, A., & Majeed, N. M. (2025). Generative artificial intelligence dependency: Scale development, validation, and its motivational, behavioral, and psychological correlates. Computers in Human Behavior Reports, 20, 100845. https://doi.org/10.1016/j.chbr.2025.100845", "authors": "Adalia Y. H. Goh; Andree Hartanto; Nadyanna M. Majeed", "year": 2025, "venue": "Computers in Human Behavior Reports", "doi": "10.1016/j.chbr.2025.100845", "url": "https://doi.org/10.1016/j.chbr.2025.100845", "preprint_url": "https://doi.org/10.31234/osf.io/aphtb_v2", "items_available": true, "language": "English", "adaptations": [ { "language": "Turkish", "country": "Turkey", "citation": "Alkan, D., Boduroğlu, E., & Yıgıter, M. S. (2026). Adaptation and validation of the generative AI dependency scale: Evidence from construct validity, reliability and measurement invariance. Frontiers in Psychology, 17. https://doi.org/10.3389/fpsyg.2026.1908473", "doi": "10.3389/fpsyg.2026.1908473", "url": "https://doi.org/10.3389/fpsyg.2026.1908473", "status": "published", "notes": "N=640. First- and second-order CFA supported the three factors (CFI .996, RMSEA .053). α .78/.79/.92, total .91; ω .79/.84/.94, total .93; CR .805–.919; AVE .60–.84; strict invariance across gender, age, education and AI-use groups. Full text seen (PMC13491653)." }, { "language": "Turkish", "country": "Turkey", "citation": "Seki, T., Şimşir Gökalp, Z., Abdilamitova, Z., Küçükdere, R. T., & Bayat, A. (2026). Turkish version of the Generative AI Dependency Scale: Validity, reliability, and psychometric properties. Research on Education and Psychology, 10. https://doi.org/10.54535/rep.1886848", "doi": "10.54535/rep.1886848", "url": "https://doi.org/10.54535/rep.1886848", "status": "published", "notes": "A second, independent Turkish adaptation. N=411 (341 women); CFA supported the three factors; α .88, ω .89; correlated with internet addiction and AI chatbot addiction measures. Crossref abstract only." }, { "language": "Chinese", "country": "China", "citation": "Deng, Y., Wen, J., Pang, A., Li, Y., & Zhang, Y. (2026). Validation of the generative artificial intelligence dependency scale among Chinese college students. Acta Psychologica, 270, 107760. https://doi.org/10.1016/j.actpsy.2026.107760", "doi": "10.1016/j.actpsy.2026.107760", "url": "https://doi.org/10.1016/j.actpsy.2026.107760", "status": "published", "notes": "N=986 college students split for item analysis/EFA (491) and CFA (495); CFI .97, TLI .96, RMSEA .05, SRMR .03. α .932 (dimensions .804–.948); 4-week ICC .851 (n=324); invariance across gender and grade; correlated with generative AI addiction and Big Five. OpenAlex abstract only." }, { "language": "Korean", "country": "South Korea", "citation": "Kang, H., & Baek, Y. M. (2026). Validating the Generative Artificial Intelligence Dependency Scale in the Korean context: Construct validity, measurement invariance, and criterion validity across South Korea, Singapore, and the United States. Korean Journal of Journalism & Communication Studies, 70(4), 383–415. https://doi.org/10.20879/kjjcs.2026.70.4.011", "doi": "10.20879/kjjcs.2026.70.4.011", "url": "https://doi.org/10.20879/kjjcs.2026.70.4.011", "status": "published", "notes": "n = 895; Three-factor CFA model showed good fit in Korean GenAI users; Reliability indices met conventional criteria (values not seen; abstract only)" } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gaies", "name": "Generative AI Engagement Scale", "acronym": "GAIES", "summary": "Measures how often people use GenAI for personal versus work/study purposes, and how they interact with it (asking probing questions, steering conversations, writing precise prompts). Use it when you need a validated measure of GenAI use frequency and interaction style.", "target": "Generative AI", "constructs": [ "acceptance-use", "other" ], "populations": [ "general adults" ], "items": 20, "response": "Use Frequency: 7-point (never to always), recoded to 5 categories for analysis; Interaction Style: 5-point Likert (strongly disagree to strongly agree)", "subscales": [ { "name": "Use Frequency – Self-interest-oriented", "items": 4, "description": "Past-month use for curiosity, leisure, hobbies and personal enrichment" }, { "name": "Use Frequency – Task-oriented", "items": 4, "description": "Past-month use for work or study tasks" }, { "name": "Interaction Style – Questioningness", "items": 4, "description": "Probing, challenging and debating GenAI's reasoning" }, { "name": "Interaction Style – Expressiveness", "items": 4, "description": "Steering interactions toward one's own interests and personality" }, { "name": "Interaction Style – Preciseness", "items": 4, "description": "Clear, logically organized, carefully revised prompts" } ], "psychometrics": { "structure": "CTT item analysis, EFA and multidimensional IRT (graded response model) on n = 180; CFA on the hold-out n = 180 (WLSMV). Frequency 2-factor: CFI .991, TLI .987, RMSEA .067, SRMR .072. Interaction Style 3-factor with residual covariances: CFI .981, TLI .977, RMSEA .068, SRMR .068.", "reliability": "Item-analysis stage: α .84 (self-interest), .75 (task), .79 (questioningness), .76 (preciseness), .74 (expressiveness)", "validity": [ "Convergent: standardized CFA loadings .54–.81 (frequency) and .42–.88 (interaction style)", "Predictive/nomological: UTAUT predictors differentially explained self-interest vs task use (SEM)", "Latent profile analysis gave 4 interpretable user groups" ], "samples": [ { "n": 360, "country": "Not stated (authors at Monash University Malaysia)", "population": "Adults with basic awareness of GenAI (414 recorded, 360 valid; split 180/180)" } ] }, "status": "published", "citation": "Zhang, D.-W., Tan, J. Y., Chew, Y. Y., Hew, L., & Choo, J. Y. (2025). Development and validation of the generative AI engagement scale. Computers in Human Behavior: Artificial Humans, 6, 100221. https://doi.org/10.1016/j.chbah.2025.100221", "authors": "Zhang, D.-W., Tan, J. Y., Chew, Y. Y., Hew, L., & Choo, J. Y.", "year": 2025, "venue": "Computers in Human Behavior: Artificial Humans", "doi": "10.1016/j.chbah.2025.100221", "url": "https://doi.org/10.1016/j.chbah.2025.100221", "preprint_url": "https://doi.org/10.31234/osf.io/3kxag_v1", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "glat", "name": "Generative AI Literacy Assessment Test", "acronym": "GLAT", "summary": "A 20-question multiple-choice test that objectively measures what higher-education learners know about GenAI and how to use it. Use it when you want performance-based rather than self-reported literacy. Its scores predicted performance on AI-assisted tasks better than a self-report scale did.", "target": "Generative AI", "constructs": [ "literacy" ], "populations": [ "university students" ], "items": 20, "response": "Multiple choice (4 options, scored correct/incorrect)", "subscales": [ { "name": "Know & understand", "items": null, "description": "Conceptual knowledge of how GenAI works" }, { "name": "Use & apply", "items": null, "description": "Applying GenAI tools to tasks" }, { "name": "Evaluate & create", "items": null, "description": "Evaluating outputs and creating with GenAI" }, { "name": "Ethics", "items": null, "description": "Ethical issues in GenAI use" } ], "psychometrics": { "structure": "Essentially unidimensional; CTT plus IRT, with a 2PL model retained: RMSEA .03, CFI .97", "reliability": "α .80; ω total .81", "validity": [ "Content validity pilot with 200 students (relevance, comprehensiveness, comprehensibility)", "Criterion: GLAT predicted performance on GenAI-supported tasks, while self-reported literacy (CLS) did not" ], "samples": [ { "n": 355, "country": "Not stated (recruited via Prolific)", "population": "higher education students" } ] }, "status": "published", "citation": "Jin, Y., Martinez-Maldonado, R., Gašević, D., & Yan, L. (2025). GLAT: The generative AI literacy assessment test. Computers and Education: Artificial Intelligence, 9, 100436. https://doi.org/10.1016/j.caeai.2025.100436", "authors": "Yueqiao Jin; Roberto Martinez-Maldonado; Dragan Gašević; Lixiang Yan", "year": 2025, "venue": "Computers and Education: Artificial Intelligence", "doi": "10.1016/j.caeai.2025.100436", "url": "https://www.sciencedirect.com/science/article/pii/S2666920X25000761", "preprint_url": "https://arxiv.org/abs/2411.00283", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gail-workplace", "name": "Generative AI Literacy Scale (workplace)", "acronym": "GAIL", "summary": "A 17-item measure of employees' GenAI literacy. It covers basic operation, prompt engineering, judging output quality, innovative use, and ethics/compliance. Use it in organizational or workplace research; the authors also suggest a 10-item short form.", "target": "Generative AI tools at work", "constructs": [ "literacy", "workplace", "creativity" ], "populations": [ "employees & professionals" ], "items": 17, "response": "7-point Likert (1 = strongly disagree to 7 = strongly agree)", "subscales": [ { "name": "Basic operational skills", "items": 3, "description": "Understanding GenAI principles and limits and using its core functions" }, { "name": "Prompt engineering ability", "items": 3, "description": "Designing and refining effective prompts" }, { "name": "Quality evaluation ability", "items": 3, "description": "Judging the accuracy, coherence and logic of outputs" }, { "name": "Innovative application ability", "items": 3, "description": "Spotting opportunities and turning GenAI ideas into results" }, { "name": "Ethical and compliance awareness", "items": 5, "description": "Privacy, regulations, organizational rules and professional ethics" } ], "psychometrics": { "structure": "Five factors from EFA (n = 278), then CFA (n = 306): χ² = 136.94, df = 109, CFI .995, TLI .994, RMSEA .029, SRMR .020", "reliability": "Total α .943; CR .912–.952 by dimension", "validity": [ "Convergent: AVE .776–.825", "Criterion/nomological: GAIL predicted job performance (β = .68), partly mediated by creative self-efficacy, in a separate public-sector sample (n = 344)", "Discriminant validity (HTMT or Fornell-Larcker) not reported" ], "samples": [ { "n": 278, "country": "China", "population": "private-sector employees (EFA)" }, { "n": 306, "country": "China", "population": "private-sector employees (CFA)" }, { "n": 344, "country": "China", "population": "public-sector employees (validation)" } ] }, "status": "published", "citation": "Liu, X., Zhang, L., & Wei, X. (2025). Generative artificial intelligence literacy: Scale development and its effect on job performance. Behavioral Sciences, 15(6), 811. https://doi.org/10.3390/bs15060811", "authors": "Xin Liu; Longxin Zhang; Xiaochong Wei", "year": 2025, "venue": "Behavioral Sciences", "doi": "10.3390/bs15060811", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12189696/", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gai-acs", "name": "Generative AI–Academic Cheating Scale", "acronym": "GAI-ACS", "summary": "A Korean scale of academic cheating with generative AI among undergraduates: accepting AI-generated output as one's own work and not disclosing AI use. Useful for academic-integrity research and policy in AI-integrated courses.", "target": "generative AI in learning", "constructs": [ "academic-integrity", "ethics-concerns" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Acceptance of AI-generated outputs", "items": null, "description": "Submitting or accepting AI-generated output as one's own work" }, { "name": "Non-disclosure of AI use", "items": null, "description": "Not acknowledging or disclosing the use of AI" } ], "psychometrics": { "structure": "Literature review, expert consultation and Delphi; EFA (preliminary survey) and CFA (main survey): 2 factors, final model 'good fit' (indices not in abstract)", "reliability": "Acceptable internal consistency (values not in abstract)", "validity": [ "Delphi expert validation", "Construct validity (type and numbers not given in abstract)" ], "samples": [ { "n": null, "country": "South Korea", "population": "undergraduate students" } ] }, "status": "published", "citation": "장세진, & 한광현. (2025). Development and validation of a scale to measure academic cheating through generative AI (GAI-ACS). 교육혁신연구, 35(2), 175–199. https://doi.org/10.21024/pnuedi.35.2.202506.175", "authors": "장세진; 한광현", "year": 2025, "venue": "교육혁신연구 (Pusan National University Institute of Educational Development)", "doi": "10.21024/pnuedi.35.2.202506.175", "url": "https://doi.org/10.21024/pnuedi.35.2.202506.175", "preprint_url": null, "items_available": false, "language": "Korean", "adaptations": [], "evidence": "partial", "flag_reason": "Delphi, EFA and CFA with \"acceptable\" reliability, but no numbers and no validity evidence beyond construct validity.", "verified": "2026-09-29" }, { "id": "genaias-efl-teachers", "name": "Generative Artificial Intelligence Attitude Scale – EFL Teachers Form", "acronym": "GenAIAS-TF", "summary": "A 25-item teacher version of the GenAIAS covering EFL teachers' attitudes toward GenAI (learning value, enjoyment, usefulness, interest). Use it with language teachers.", "target": "Generative AI", "constructs": [ "attitudes", "workplace" ], "populations": [ "teachers & academics" ], "items": 25, "response": null, "subscales": [ { "name": "Learning/utility", "items": null, "description": "Value of GenAI for learning and teaching" }, { "name": "Enjoyment", "items": null, "description": "Enjoyment of using GenAI" }, { "name": "Usefulness", "items": null, "description": "Perceived usefulness" }, { "name": "Interest", "items": null, "description": "Interest in GenAI" } ], "psychometrics": { "structure": "EFA and CFA across three independent teacher samples; 4 factors", "reliability": "Good internal consistency (values not in abstract)", "validity": [ "Convergent and discriminant validity", "Measurement invariance across gender", "Correlations with daily internet hours and perceived AI knowledge (+) and age (−)" ], "samples": [ { "n": null, "country": "Türkiye", "population": "three samples of EFL teachers" } ] }, "status": "published", "citation": "Aydın Yıldız, T., Çınar Yağcı, Ş., & Orhan, A. (2025). Development and validation of generative artificial intelligence attitude scale for EFL teachers (GenAIAS-EFL teachers form). Interactive Learning Environments, 34(6), 3907–3928. https://doi.org/10.1080/10494820.2025.2583196", "authors": "Tuğba Aydın Yıldız, Şule Çınar Yağcı, Ali Orhan", "year": 2025, "venue": "Interactive Learning Environments", "doi": "10.1080/10494820.2025.2583196", "url": "https://doi.org/10.1080/10494820.2025.2583196", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "gaias", "name": "Generative Artificial Intelligence Awareness Scale (secondary students)", "acronym": "GAIAS", "summary": "A Turkish scale of secondary school students' (ages 14–18) basic knowledge of generative AI, their positive attitudes toward it, and their concerns about its impacts. Use it to gauge adolescents' awareness of GenAI in school settings.", "target": "generative AI", "constructs": [ "literacy", "attitudes", "ethics-concerns" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Basic Knowledge of Generative Artificial Intelligence", "items": null, "description": "Understanding what GenAI is and what it does" }, { "name": "Positive Attitudes Toward Generative Artificial Intelligence", "items": null, "description": "Favourable views of GenAI" }, { "name": "Concerns About the Impacts of Generative Artificial Intelligence", "items": null, "description": "Worries about GenAI's ethical and social impacts" } ], "psychometrics": { "structure": "EFA gave 3 factors (54.29% of variance); CFA gave χ²/df 3.792, RMSEA .056, GFI .905, CFI .927, TLI .919", "reliability": "α .946 total; subscales .822–.925; corrected item-total r .512–.733; split-half for factor 1: Spearman-Brown .891, Guttman .887", "validity": [ "Content validity (expert evaluation)", "Construct validity (EFA/CFA)" ], "samples": [ { "n": 444, "country": "Türkiye", "population": "secondary school students aged 14–18" } ] }, "status": "published", "citation": "Semerci Şahin, R., Özbay, Ö., Çınar Özbay, S., & Durmuş Sarıkahya, S. (2025). Development of the generative artificial intelligence awareness scale for secondary school students in Türkiye. European Journal of Pediatrics, 184(9), Article 585. https://doi.org/10.1007/s00431-025-06435-8", "authors": "Remziye Semerci Şahin; Özkan Özbay; Sevil Çınar Özbay; Selma Durmuş Sarıkahya", "year": 2025, "venue": "European Journal of Pediatrics", "doi": "10.1007/s00431-025-06435-8", "url": "https://doi.org/10.1007/s00431-025-06435-8", "preprint_url": null, "items_available": false, "language": "Turkish", "adaptations": [], "evidence": "partial", "flag_reason": "Only content and factorial validity are reported; no convergent, criterion or other external validity evidence.", "verified": "2026-09-29" }, { "id": "hai-trust-genai-chatbots-russian", "name": "Human-AI Trust Measurement Instrument for Generative AI Chatbots (Russian adaptation)", "acronym": null, "summary": "A Russian-language, multidimensional scale of trust in generative AI chatbots, adapted from an established human–AI trust instrument and re-validated for GenAI. It covers understandability, technical competence, reliability, helpfulness, personal attachment, user autonomy, faith and institutional credibility. Use it with Russian-speaking chatbot users.", "target": "Generative AI chatbots", "constructs": [ "trust", "credibility" ], "populations": [ "general adults" ], "items": null, "response": null, "subscales": [ { "name": "Understandability", "items": null, "description": "Ability to understand how the chatbot works." }, { "name": "Technical competence", "items": null, "description": "Perceived technical capability of the chatbot." }, { "name": "Reliability", "items": null, "description": "Consistency and dependability of the chatbot." }, { "name": "Helpfulness", "items": null, "description": "Perceived helpfulness of the chatbot." }, { "name": "Personal attachment", "items": null, "description": "Emotional attachment to the chatbot." }, { "name": "User autonomy", "items": null, "description": "Sense of retaining control and autonomy when using it." }, { "name": "Faith", "items": null, "description": "Trust beyond available evidence." }, { "name": "Institutional credibility", "items": null, "description": "Credibility of the organization behind the chatbot." } ], "psychometrics": { "structure": "Original multidimensional (8-dimension) factor structure replicated in the GenAI chatbot context (method details not seen)", "reliability": "Reported as 'excellent' (coefficients not seen)", "validity": [ "Convergent: strong correlations with technology acceptance measures", "Divergent: weak associations with personality traits and generalized trust" ], "samples": [ { "n": null, "country": "Russia", "population": "Russian-speaking GenAI chatbot users" } ] }, "status": "published", "citation": "Rafikova, A., & Voronin, A. (2025). Measuring trust in generative AI chatbots: Russian adaptation and validation of the Human-AI Trust Measurement Instrument. International Journal of Human–Computer Interaction, 42(13), 9951–9970. https://doi.org/10.1080/10447318.2025.2580550", "authors": "Antonina Rafikova, Anatoly Voronin", "year": 2025, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2025.2580550", "url": "https://doi.org/10.1080/10447318.2025.2580550", "preprint_url": null, "items_available": false, "language": "Russian", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "haits", "name": "Human-AI Trust Scale", "acronym": "HAITS", "summary": "A 22-item scale of trust in generative AI. It covers the rational side (competence), the relational side (affective trust, benevolence and integrity) and distrust (perceived risk). Use it when you want a multidimensional trust profile of GenAI users, especially for comparing China and the US.", "target": "Generative AI systems (conversational GenAI tools such as ChatGPT)", "constructs": [ "trust", "relationships" ], "populations": [ "general adults" ], "items": 22, "response": null, "subscales": [ { "name": "Affective Trust", "items": 6, "description": "Emotional closeness to the AI, e.g., seeing it as a friend, warmth, emotional support." }, { "name": "Competence Trust", "items": 6, "description": "Belief that the AI is reliable, efficient and competent, and that its information can be trusted." }, { "name": "Benevolence & Integrity", "items": 5, "description": "Belief that one can confide in the AI and that it would not criticize or harm the user." }, { "name": "Perceived Risk", "items": 5, "description": "Suspicion that the AI is deceptive, underhanded or harmful, and that interacting with it is risky." } ], "psychometrics": { "structure": "72-item pool. EFA (PAF, oblimin) on Wave 1 (n = 1,546) gave 4 factors and 22 items, explaining 56.74% of variance. CFA on Wave 2 (n = 1,426): CFI = .956, TLI = .950, SRMR = .045, RMSEA = .055. Replicated with Exploratory Graph Analysis", "reliability": "ω > .90 for all factors (Table 3 values ≈ .94-.97); Cronbach's α also reported (Table 4 diagonal)", "validity": [ "Discriminant validity: all HTMT < .70", "Convergent validity: standardized loadings > .50, ω > .90", "Measurement invariance across nationality (configural, metric, partial scalar) and gender (full)", "Criterion validity: r with GenAI importance .59, use frequency .44, addiction .38, collaboration .37, GenAI literacy .59, technology readiness .40, technology acceptance .57, loneliness -.31", "Latent profile analysis identified six trust profiles" ], "samples": [ { "n": 1546, "country": "China and United States", "population": "Adults (Credamo, China n = 759; Prolific, US n = 787), exploratory wave" }, { "n": 1426, "country": "China and United States", "population": "Adults (China n = 700; US n = 726), validation wave" } ] }, "status": "preprint", "citation": "Sun, H., Liu, W., Wu, D., Yu, G., & Yao, M. (2025). Revisiting trust in the era of generative AI: Factorial structure and latent profiles [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2510.10199", "authors": "Haocan Sun; Weizi Liu; Di Wu; Guoming Yu; Mike Yao", "year": 2025, "venue": "arXiv (cs.HC)", "doi": "10.48550/arXiv.2510.10199", "url": "https://arxiv.org/abs/2510.10199", "preprint_url": "https://arxiv.org/abs/2510.10199", "items_available": true, "language": "Chinese & English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "human-genai-trust-scale-chinese", "name": "Human-Generative Artificial Intelligence Trust Scale (Chinese version)", "acronym": null, "summary": "A 9-item Chinese scale of trust in generative AI, adapted from the Human–Computer Trust Scale and re-validated. It covers benevolence, competence and reciprocity. Use it when you need a brief trust measure for Chinese GenAI users.", "target": "Generative AI (human–GenAI interaction)", "constructs": [ "trust" ], "populations": [ "general adults" ], "items": 9, "response": null, "subscales": [ { "name": "Benevolence", "items": null, "description": "Belief that the GenAI acts in the user's interest." }, { "name": "Competence", "items": null, "description": "Belief that the GenAI can do its tasks well." }, { "name": "Reciprocity", "items": null, "description": "Sense of a mutual, give-and-take interaction with the GenAI." } ], "psychometrics": { "structure": "3-factor, 9-item structure; CFA χ²/df=2.32, TLI=.92, CFI=.95, RMSEA=.09, SRMR=.05; all items showed good discrimination", "reliability": "Overall α .891; subscales α .806–.810", "validity": [ "Criterion-related validity supported (measures not named in abstract)", "Convergent validity supported" ], "samples": [ { "n": 310, "country": "China", "population": "Online survey of GenAI users (valid responses)" } ] }, "status": "published", "citation": "Wang, P., Yin, K., Tian, M., Zheng, Y., Wu, H., Zhou, C., Zhang, M., Ma, J., & Yuan, X. (2025). A validation of the Human-Generative Artificial Intelligence Trust Scale. International Journal of Human–Computer Interaction, 42(7), 4808–4821. https://doi.org/10.1080/10447318.2025.2542881", "authors": "Peng Wang, Kexin Yin, Mei Tian, Yuanxin Zheng, Heyu Wu, Chen Zhou, Mingzhu Zhang, Junchi Ma, Xiqing Yuan", "year": 2025, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2025.2542881", "url": "https://www.tandfonline.com/doi/full/10.1080/10447318.2025.2542881", "preprint_url": null, "items_available": false, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "icap-genai-scale", "name": "ICAP GenAI Scale", "acronym": null, "summary": "A 27-item scale of how graduate students use generative AI in their research, from passively taking its output to actively co-constructing ideas with it, based on the ICAP (Interactive-Constructive-Active-Passive) framework. Use it to profile the depth of students' cognitive engagement with GenAI in research work.", "target": "generative AI in academic research", "constructs": [ "learning", "acceptance-use" ], "populations": [ "graduate students", "university students" ], "items": 27, "response": null, "subscales": [ { "name": "Passive", "items": null, "description": "Receiving GenAI output without further processing" }, { "name": "Active", "items": null, "description": "Selecting or manipulating GenAI output" }, { "name": "Constructive", "items": null, "description": "Generating new ideas beyond what GenAI provides" }, { "name": "Interactive", "items": null, "description": "Co-constructing knowledge with GenAI through dialogue" } ], "psychometrics": { "structure": "Item-total analysis and EFA (Sample 1) gave 4 factors; CFA (Sample 2) showed excellent fit (indices not in abstract)", "reliability": "Reported as high (values not in abstract)", "validity": [ "Construct validity (Sample 2)", "Demographic measurement invariance" ], "samples": [ { "n": 1216, "country": "China", "population": "graduate students across five disciplines, split into two samples" } ] }, "status": "published", "citation": "Zhang, J., Pan, W., Liang, X., & Ge, J. (2025). Development and validation of the ICAP GenAI Scale to measure how graduate students integrate generative AI into academic research. European Journal of Education, 60(3), Article e70209. https://doi.org/10.1111/ejed.70209", "authors": "Jianzhen Zhang; Weihao Pan; Xiaoyu Liang; Jiahao Ge", "year": 2025, "venue": "European Journal of Education", "doi": "10.1111/ejed.70209", "url": "https://doi.org/10.1111/ejed.70209", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "instructors-chatgpt-usage-acceptance-confidence", "name": "Instructors' Usage, Acceptance and Confidence towards ChatGPT Instrument", "acronym": null, "summary": "A questionnaire of university instructors' use of, acceptance of and confidence with ChatGPT for teaching. Useful for gauging staff readiness before rolling out GenAI in teaching.", "target": "ChatGPT in teaching", "constructs": [ "acceptance-use", "self-efficacy", "workplace" ], "populations": [ "teachers & academics" ], "items": null, "response": null, "subscales": [ { "name": "Usage", "items": null, "description": "Instructors' use of ChatGPT (construct name inferred from title)" }, { "name": "Acceptance", "items": null, "description": "Acceptance of ChatGPT in teaching (inferred from title)" }, { "name": "Confidence", "items": null, "description": "Confidence using ChatGPT (inferred from title)" } ], "psychometrics": { "structure": "Measurement-model assessment (factor loadings, AVE); no EFA/CFA named in abstract", "reliability": "Cronbach's α and CR reported as high (values not in abstract)", "validity": [ "Content review by a 3-member expert team", "Convergent validity (loadings, AVE)", "Discriminant validity (Fornell–Larcker, cross-loadings, HTMT)" ], "samples": [ { "n": null, "country": "not reported", "population": "instructors" } ] }, "status": "published", "citation": "Singh, P., & Anthonysamy, L. (2025). Development and validation of an instrument to assess instructors' usage, acceptance, and confidence towards ChatGPT: A reliability and discriminant validity analysis. Education and Information Technologies, 30(17), 25251–25271. https://doi.org/10.1007/s10639-025-13773-5", "authors": "Parmjit Singh; L. Anthonysamy", "year": 2025, "venue": "Education and Information Technologies", "doi": "10.1007/s10639-025-13773-5", "url": "https://doi.org/10.1007/s10639-025-13773-5", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "Reports reliability and discriminant validity from a PLS measurement model; no named EFA/CFA, and item details were not accessible.", "verified": "2026-09-29" }, { "id": "etcdl", "name": "K-12 English Teachers' ChatGPT-Based Digital Literacy Scale", "acronym": "ETCDL", "summary": "A 19-item scale of how digitally literate primary and secondary English teachers are when using ChatGPT for teaching. Use it to assess pre-service or in-service English teachers' readiness for ChatGPT-assisted instruction.", "target": "ChatGPT in K-12 English teaching", "constructs": [ "literacy", "workplace" ], "populations": [ "teachers & academics" ], "items": 19, "response": null, "subscales": [ { "name": "(four dimensions; names not given in abstract)", "items": null, "description": "Four dimensions grounded in TPACK, the Theory of Planned Behaviour and a teacher digital-literacy framework" } ], "psychometrics": { "structure": "CFA and SEM supported a 4-dimension, 19-item structure", "reliability": "Reported as reliable (values not in abstract)", "validity": [ "Construct validity via CFA/SEM (details not in abstract)" ], "samples": [ { "n": 288, "country": "China", "population": "prospective and in-service English teachers" } ] }, "status": "published", "citation": "Luo, S., & Zou, D. (2025). K-12 English teachers' ChatGPT-based digital literacy: Scale development and validation. European Journal of Education, 60(4), Article e70273. https://doi.org/10.1111/ejed.70273", "authors": "Shuqiong Luo; Di Zou", "year": 2025, "venue": "European Journal of Education", "doi": "10.1111/ejed.70273", "url": "https://doi.org/10.1111/ejed.70273", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "The abstract reports CFA and calls the scale reliable, but the full text was inaccessible, so the validity evidence could not be checked.", "verified": "2026-09-29" }, { "id": "kab-academic-ai-tools-scale", "name": "KAB-Based Scale for University Students' Academic Use of AI Tools", "acronym": null, "summary": "A 14-item scale of whether students know the academic-integrity rules for using AI tools (e.g., ghostwriting, uncited AI content) and whether they have broken them in coursework or research. Use it to screen for AI-related academic misconduct risk and to target integrity training.", "target": "generative AI tools in academic work (items say 'AI tools')", "constructs": [ "academic-integrity", "literacy" ], "populations": [ "university students" ], "items": 14, "response": null, "subscales": [ { "name": "Usage Norms Knowledge", "items": 6, "description": "Knowing which AI uses (ghostwriting, falsifying data, exam help) count as academic misconduct" }, { "name": "Academic Tasks Misconduct", "items": 3, "description": "Self-reported AI misuse in assignments (ghostwriting, heavy modification, uncited AI content)" }, { "name": "Academic Achievements Misconduct", "items": 5, "description": "Self-reported AI misuse in papers, patents, research data and exams" } ], "psychometrics": { "structure": "EFA (PCA, varimax; n = 128) gave 3 factors, 70.88% of variance; CFA (n = 200) gave χ²/df 2.978, CFI .920, TLI .902, IFI .921, GFI .859, RMSEA .098, RMR .048; the 3-factor model fit better than 1- and 2-factor models", "reliability": "Formal test: α .811 total; knowledge subscale .930, behaviour subscale .865; dimension α .930, .775, .873; CR .932, .783, .889; split-half .891 and .860 (subscales)", "validity": [ "Content validity via two-round Delphi (12 experts)", "Convergent validity (AVE .55–.70)", "Discriminant validity (Fornell-Larcker)", "Competing-model comparison" ], "samples": [ { "n": 128, "country": "China", "population": "nursing undergraduates, pilot/EFA (140 collected)" }, { "n": 200, "country": "China", "population": "second- and third-year nursing undergraduates with ≥3 months of GenAI use, Hunan, formal test/CFA (220 collected)" } ] }, "status": "published", "citation": "Ou, Y., Aguila, N. A., Xiao, L., Zhang, Z., & Ouyang, F. (2025). Development and reliability-validity testing of a knowledge, attitude, and behavior (KAB)-based scale for university students' academic use of AI tools. International Journal of Public Health and Medical Research, 3(3), 12–22. https://doi.org/10.62051/ijphmr.v3n3.02", "authors": "Yangli Ou; Nancy A. Aguila; Lulu Xiao; Zhiyuan Zhang; Fangzhu Ouyang", "year": 2025, "venue": "International Journal of Public Health and Medical Research", "doi": "10.62051/ijphmr.v3n3.02", "url": "https://doi.org/10.62051/ijphmr.v3n3.02", "preprint_url": null, "items_available": true, "language": "Chinese", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "lds", "name": "Large Language Model Dependence Scale", "acronym": "LDS", "summary": "An 18-item scale of dependence on LLMs, grounded in human–computer trust and addiction theory. It has two parts: functional dependence (trust in and reliance on what LLMs produce) and existential dependence (addiction-like difficulty working without them). Use it where trust-driven reliance shades into dependence.", "target": "Large language models / generative AI", "constructs": [ "dependence", "reliance", "trust" ], "populations": [ "general adults" ], "items": 18, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree), per studies using the scale", "subscales": [ { "name": "Functional dependence", "items": null, "description": "Reliance driven by trust in LLM-generated content and capability (e.g., 'I believe [LLMs] are honest')." }, { "name": "Existential dependence", "items": null, "description": "Addiction-like symptoms such as difficulty concentrating or working without LLMs." } ], "psychometrics": { "structure": "Two samples: item analysis, EFA and network analysis (reported n=421), then CFA (reported n=1,030) supporting a bifactor/two-dimension structure (per search snippets; not re-verified)", "reliability": "Original α .87 and .89 (two samples), subscales > .85 (as reported by a later study citing Li et al.)", "validity": [ "Criterion-related validity reported (per snippets; details not seen)" ], "samples": [ { "n": 421, "country": "not confirmed", "population": "LLM users (exploratory sample)" }, { "n": 1030, "country": "not confirmed", "population": "LLM users (confirmatory sample)" } ] }, "status": "published", "citation": "Li, Z., Zhang, Z., Wang, M., & Wu, Q. (2025). From assistance to reliance: Development and validation of the large language model dependence scale. International Journal of Information Management, 83, 102888. https://doi.org/10.1016/j.ijinfomgt.2025.102888", "authors": "Zewei Li, Zheng Zhang, Mingwei Wang, Qi Wu", "year": 2025, "venue": "International Journal of Information Management", "doi": "10.1016/j.ijinfomgt.2025.102888", "url": "https://www.sciencedirect.com/science/article/abs/pii/S0268401225000209", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "llm-d12", "name": "Large Language Models Dependency 12-item Scale", "acronym": "LLM-D12", "summary": "A 12-item scale of dependency on LLMs with two parts: instrumental dependency (relying on LLMs for decisions and thinking tasks) and relationship dependency (treating LLMs as companion-like). It frames dependency as reliance rather than addiction, so it suits studies of how much people lean on LLMs.", "target": "Large language models (e.g., ChatGPT)", "constructs": [ "dependence", "reliance", "relationships" ], "populations": [ "general adults" ], "items": 12, "response": "6-point Likert (1 = strongly disagree to 6 = strongly agree)", "subscales": [ { "name": "Instrumental Dependency", "items": 6, "description": "Relying on LLMs to support or collaborate in decision-making and cognitive tasks." }, { "name": "Relationship Dependency", "items": 6, "description": "Perceiving LLMs as socially meaningful, sentient, or companion-like." } ], "psychometrics": { "structure": "Split sample: EFA and CFA on separate halves supported 2 factors; network psychometrics (qgraph/bootnet) cross-validated the structure", "reliability": "α .84 (Instrumental), .91 (Relationship)", "validity": [ "Convergent validity: AVE and CR", "Discriminant validity: HTMT", "External validation with internet addiction, attitudes toward AI (ATAI), need for cognition, and perceived trustworthiness of primary LLM" ], "samples": [ { "n": 526, "country": "United Kingdom", "population": "Adults" } ] }, "status": "published", "citation": "Yankouskaya, A., Babiker, A., Rizvi, S., Alshakhsi, S., Liebherr, M., & Ali, R. (2025). LLM-D12: A dual-dimensional scale of instrumental and relational dependencies on large language models. ACM Transactions on the Web. https://doi.org/10.1145/3765895", "authors": "Ala Yankouskaya, Areej Babiker, Syeda Rizvi, Sameha Alshakhsi, Magnus Liebherr, Raian Ali", "year": 2025, "venue": "ACM Transactions on the Web", "doi": "10.1145/3765895", "url": "https://dl.acm.org/doi/10.1145/3765895", "preprint_url": "https://arxiv.org/abs/2506.06874", "items_available": true, "language": "English", "adaptations": [ { "language": "Arabic", "country": "Arab countries", "citation": "Alshakhsi, S., Yankouskaya, A., Liebherr, M., & Ali, R. (2026). Measuring large language models dependency: Validating the Arabic version of the LLM-D12 scale. Arabian Journal for Science and Engineering, 51(15), 18621–18644. https://doi.org/10.1007/s13369-026-11386-9", "doi": "10.1007/s13369-026-11386-9", "url": "https://link.springer.com/article/10.1007/s13369-026-11386-9", "status": "published", "notes": "Crossref abstract: N=250 Arab participants; α total .90, Instrumental .85, Relationship .90. Instrumental dependency correlated with AI acceptance and internet addiction; relationship dependency with lower need for cognition and higher LLM trust." }, { "language": "Spanish", "country": "Spanish-speaking", "citation": "Bao, T. G., El-Haj, M., Al-Shakhsi, S., Garcia-Cabot, A., Ali, R., & Yankouskaya, A. (2026). Developing and validating the Spanish version of the large language models dependency scale (LLM-D12-SP). Discover Psychology. https://doi.org/10.1007/s44202-026-00829-x", "doi": "10.1007/s44202-026-00829-x", "url": "https://doi.org/10.1007/s44202-026-00829-x", "status": "published", "notes": "Crossref and arXiv (2607.22041) abstracts: N=386 Spanish-speaking adults (mean age 28); CFA two-factor; α total .89, subscales .86/.85. Both dimensions associated with internet addiction and perceived LLM trustworthiness; minimal link with need for cognition." }, { "language": "German", "country": "Germany", "citation": "Liebherr, M., Yankouskaya, A., AlShakhsi, S., Montag, C., & Ali, R. (2026). Development and validation of the German version of the large language model dependency scale (LLM-D12). Discover Artificial Intelligence, 6, 778. https://doi.org/10.1007/s44163-026-01781-4", "doi": "10.1007/s44163-026-01781-4", "url": "https://doi.org/10.1007/s44163-026-01781-4", "status": "published", "notes": "Crossref abstract: N=402 German-speaking active LLM users. Excellent internal consistency, strong composite reliability, satisfactory convergent validity; both dimensions correlated with AI acceptance and trustworthiness." }, { "language": "Japanese", "country": "Japan", "citation": "Tanaka, M., Furutani, K., AlShakhsi, S., Yankouskaya, A., & Ali, R. (2026). Development and validation of the Japanese version of the Large Language Model Dependency Scale (LLM-D12) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-9832900/v1", "doi": "10.21203/rs.3.rs-9832900/v1", "url": "https://doi.org/10.21203/rs.3.rs-9832900/v1", "status": "preprint", "notes": "Crossref abstract: N=358 Japanese university students who had used LLMs; two-factor model fitted better than one-factor; instrumental dependency showed broader links with internet use and attitudes toward AI. Reliability values not seen." }, { "language": "Turkish", "country": "Turkey", "citation": "Coskun Aslan, T., Uncular, G., Durmus, H., Kavla, Y., Borlu, A., Alshakhsi, S., Yankouskaya, A., & Ali, R. (2026). Adaptation and validation of the Turkish version of the Large Language Model Dependency Scale (LLM-D12) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2603.26296", "doi": "10.48550/arXiv.2603.26296", "url": "https://arxiv.org/abs/2603.26296", "status": "preprint", "notes": "arXiv abstract: N=387 regular LLM users; forward–backward translation; 11 items after removing one; CFI .993, RMSEA .073; high internal consistency; no correlation with need for cognition." } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "lrcel", "name": "Learner Readiness for ChatGPT-Assisted English Learning scale", "acronym": "LRCEL", "summary": "An 18-item scale of how ready university EFL (English as a foreign language) learners are to use ChatGPT for learning English. It draws on the theory of planned behaviour and on achievement emotions. Use it before running ChatGPT-based language-learning activities, or to compare learners' readiness.", "target": "ChatGPT in English (EFL) learning", "constructs": [ "acceptance-use", "learning", "attitudes" ], "populations": [ "university students" ], "items": 18, "response": null, "subscales": [ { "name": "Seven readiness dimensions (names not seen)", "items": null, "description": "Dimensions based on the theory of planned behaviour and control-value theory of achievement emotions; exact labels and item counts not seen" } ], "psychometrics": { "structure": "Seven dimensions tested with CFA and SEM (abstract). A secondary source also reports ESEM", "reliability": "Reported as reliable (values not seen)", "validity": [ "Validity testing reported in the abstract; a secondary source describes convergent and discriminant validity" ], "samples": [ { "n": 347, "country": "China", "population": "university learners" } ] }, "status": "published", "citation": "Luo, S., & Zou, D. (2025). University learners' readiness for ChatGPT-assisted English learning: Scale development and validation. European Journal of Education, 60(1), e12886. https://doi.org/10.1111/ejed.12886", "authors": "Shuqiong Luo; Di Zou", "year": 2025, "venue": "European Journal of Education", "doi": "10.1111/ejed.12886", "url": "https://doi.org/10.1111/ejed.12886", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "lair-learner-genai-relationship", "name": "Learner-Generative AI Relationship Scale", "acronym": "LAIR", "summary": "Measures the relationship students feel they have with a generative AI tutor or assistant such as ChatGPT: emotional closeness, how competent they judge the AI to be, and how smoothly the interaction flows. Use it to study how that relationship relates to engagement and learning in AI-assisted tasks.", "target": "generative AI (ChatGPT) in learning", "constructs": [ "relationships", "social-presence", "learning" ], "populations": [ "university students" ], "items": null, "response": null, "subscales": [ { "name": "Affective Intimacy", "items": null, "description": "Emotional closeness with the AI (3 sub-factors)" }, { "name": "Cognitive Competence", "items": null, "description": "Perceived intellectual competence of the AI (3 sub-factors)" }, { "name": "Social Flow", "items": null, "description": "Smooth, natural social interaction with the AI (3 sub-factors)" } ], "psychometrics": { "structure": "EFA (n=95): 3 factors, each with 3 sub-factors; no CFA mentioned in abstract", "reliability": "Good internal consistency (values not in abstract)", "validity": [ "Concurrent validity: correlations with attitude toward AI and AI self-efficacy", "Predictive validity: predicted learning engagement and perceived cognitive and motivational effects in a ChatGPT-assisted argumentative writing task (n=75)", "Expert review and cognitive pre-testing" ], "samples": [ { "n": 95, "country": "not reported in abstract (author affiliation: South Korea)", "population": "undergraduate students (EFA)" }, { "n": 75, "country": "not reported in abstract (author affiliation: South Korea)", "population": "participants completing a ChatGPT-assisted argumentative essay task" } ] }, "status": "published", "citation": "Jin, S.-H. (2025). Measures of learner-generative AI relationships. Computers and Education Open, 8, Article 100258. https://doi.org/10.1016/j.caeo.2025.100258", "authors": "Sung-Hee Jin", "year": 2025, "venue": "Computers and Education Open", "doi": "10.1016/j.caeo.2025.100258", "url": "https://doi.org/10.1016/j.caeo.2025.100258", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "psu-ai", "name": "Perceived Shared Understanding with AI Scale", "acronym": "PSU-AI", "summary": "An 8-item measure of how much users feel an LLM chatbot shares meaning with them, as if it were 'someone' who understands them and their situation. Useful in research on anthropomorphism, social presence, trust and the quality of conversations with LLMs.", "target": "LLM chatbots (ChatGPT); participants rated a specific past ChatGPT conversation", "constructs": [ "anthropomorphism", "social-presence" ], "populations": [ "general adults" ], "items": 8, "response": "7-point Likert-style; stem 'This AI ...'", "subscales": [ { "name": "(unidimensional)", "items": 8, "description": "Social-semantic sense that the AI understands one's feelings, is connected to one, shares one's perspective and grasps the meaning and context of the interaction" } ], "psychometrics": { "structure": "EFA (PAF, oblimin; 33-item pool): an 8-item social-semantic factor retained; a 2-item contextual factor dropped. CFA (Study 2): χ²(20) = 68.52, SRMR .048, RMSEA .128 [.095, .161], CFI .933; all loadings > .70", "reliability": "ω .92 (Study 1); ω .92 (Study 2)", "validity": [ "Convergent: correlations with IOS overlap (r = .44 / .39), response satisfaction (r = .49 / .45) and trust in the AI (r = .53 / .51)", "Known-groups: higher scores among those who accepted the AI's answer and for successful vs unsuccessful interactions, replicated in both studies" ], "samples": [ { "n": 580, "country": "multinational (US, UK, Portugal, South Africa, Poland, Italy and others)", "population": "ChatGPT users (Prolific)" }, { "n": 150, "country": "multinational (Poland, US, UK, Portugal, South Africa and others)", "population": "ChatGPT users (Prolific; validation sample)" } ] }, "status": "published", "citation": "Liang, Q., & Banks, J. (2025). Perceived shared understanding between humans and artificial intelligence: Development and validation of a self-report scale. Technology, Mind, and Behavior, 6(1), 17–27. https://doi.org/10.1037/tmb0000161", "authors": "Qingyu Liang; Jaime Banks", "year": 2025, "venue": "Technology, Mind, and Behavior", "doi": "10.1037/tmb0000161", "url": "https://doi.org/10.1037/tmb0000161", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "pt-llm-8", "name": "Perceived Trustworthiness of LLMs Scale", "acronym": "PT-LLM-8", "summary": "An 8-item measure of how trustworthy people judge their main LLM (e.g., ChatGPT) to be, with one item for each TrustLLM dimension: truthfulness, privacy, fairness, safety, robustness, transparency, accountability and legal compliance. Use the total score to compare groups or test interventions; single items can be read for specific dimensions.", "target": "The respondent's primary large language model (e.g., ChatGPT, Gemini, Claude)", "constructs": [ "trust", "credibility" ], "populations": [ "general adults" ], "items": 8, "response": "11-point scale, 0 (not at all) to 10 (completely); stem 'I trust my primary LLM will...'", "subscales": [ { "name": "(unidimensional)", "items": 8, "description": "Overall perceived trustworthiness of the LLM; each item maps to one TrustLLM dimension and can be read individually." } ], "psychometrics": { "structure": "Split sample. EFA (ML, n=376): single factor explaining 64.3% of variance. CFA (robust ML, n=376): one-factor model χ²(17)=63.59, robust CFI=.96, TLI=.93, RMSEA=.09, SRMR=.03", "reliability": "α .90 (bootstrap 95% CI .889–.914); CR .91; item-total r .62–.75; mean inter-item r .55", "validity": [ "Convergent validity: AVE = .552", "Measurement invariance across gender (configural, metric, scalar, strict tested)", "External correlates in regression: self-efficacy (strongest), self-esteem and Big Five traits; LLM competence and use for personal or professional tasks predicted scores (usage model R² = .24)" ], "samples": [ { "n": 752, "country": "United Kingdom", "population": "Adult LLM users (M age 28.6, SD 6.1; 50.3% male)" } ] }, "status": "preprint", "citation": "Yankouskaya, A., Barajeeh, B., Babiker, A., AlShakhsi, S., Ma, Y. T., Ho, C. S. M., & Ali, R. (2025). Development and validation of a scale assessing perceived trustworthiness in large language models [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7738215/v1", "authors": "Ala Yankouskaya, Basad Barajeeh, Areej Babiker, Sameha AlShakhsi, Yunsi Tina Ma, Chun Sing Maxwell Ho, Raian Ali", "year": 2025, "venue": "Research Square (preprint)", "doi": "10.21203/rs.3.rs-7738215/v1", "url": "https://www.researchsquare.com/article/rs-7738215/v1", "preprint_url": "https://www.researchsquare.com/article/rs-7738215/v1", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "teacher-chatgpt-math-attitude", "name": "Primary School Teacher Attitude Scale for the Use of ChatGPT in Mathematics Education", "acronym": null, "summary": "A 30-item scale of primary school teachers' attitudes toward using ChatGPT to teach mathematics. Use it in teacher-education or maths-education studies of ChatGPT adoption.", "target": "ChatGPT", "constructs": [ "attitudes", "learning", "workplace" ], "populations": [ "teachers & academics" ], "items": 30, "response": null, "subscales": [ { "name": "Dimension 1 (name not reported in abstract)", "items": null, "description": "One of two attitude dimensions; name and content not verified" }, { "name": "Dimension 2 (name not reported in abstract)", "items": null, "description": "One of two attitude dimensions; name and content not verified" } ], "psychometrics": { "structure": "EFA (pilot n = 250), then CFA (main n = 300); final 30 items on 2 dimensions", "reliability": "CR .96", "validity": [ "Discriminant validity tests", "AVE .47 (below the usual .50 benchmark)" ], "samples": [ { "n": 250, "country": "Türkiye", "population": "Primary school teachers, Ankara (pilot)" }, { "n": 300, "country": "Türkiye", "population": "Primary school teachers, Ankara (main study)" } ] }, "status": "published", "citation": "Mazı, A. (2025). Developing a primary school teacher attitude scale for the use of ChatGPT in mathematics education. Acta Psychologica, 261, 105729. https://doi.org/10.1016/j.actpsy.2025.105729", "authors": "Mazı, A.", "year": 2025, "venue": "Acta Psychologica", "doi": "10.1016/j.actpsy.2025.105729", "url": "https://doi.org/10.1016/j.actpsy.2025.105729", "preprint_url": null, "items_available": false, "language": "Turkish", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-reliance-behaviors-scale", "name": "Reliance Behaviors Scale (GenAI reliance during problem-solving)", "acronym": null, "summary": "A self-report scale of how often undergraduates show reflective, cautious, thoughtless and collaborative behaviours when using generative AI during problem-solving tasks. Use it after GenAI-supported (group) tasks to spot thoughtless over-reliance, without needing a right/wrong outcome.", "target": "Generative AI chatbots used during problem-solving tasks", "constructs": [ "reliance", "learning" ], "populations": [ "university students" ], "items": null, "response": "5-point frequency scale (1 = almost never to 5 = almost always); from a secondary summary, not verified in the full text", "subscales": [ { "name": "Reflective use", "items": null, "description": "Revising prompts, reading AI responses critically, and using AI to improve one's own work." }, { "name": "Cautious use", "items": null, "description": "Spotting AI errors, acknowledging its limits, and discarding outputs when needed." }, { "name": "Thoughtless use", "items": null, "description": "Pasting task descriptions into AI and copying outputs with little change." }, { "name": "Collaborative use", "items": null, "description": "Discussing and correcting AI outputs together with peers." } ], "psychometrics": { "structure": "EFA on 800 responses (first problem-solving activity) gave 4 factors. Two CFAs (remaining 730 responses from the same activity; 1,173 responses from a second activity) showed adequate fit", "reliability": "Overall α .84; subscale α reportedly down to about .65 (per secondary review table)", "validity": [ "Structural generalisation across two different problem-solving tasks (CFA)", "Task sensitivity: reliance behaviours differed between the coding and policy-drafting tasks (from NTU summary; not verified in the full text)", "Partial response-process check: self-reports compared with high scorers' GenAI chat histories (secondary sources)" ], "samples": [ { "n": 800, "country": "Singapore", "population": "Undergraduate responses, EFA (single university)" }, { "n": 1903, "country": "Singapore", "population": "CFA responses across two activities (730 + 1,173)" } ] }, "status": "published", "citation": "Hou, C., Zhu, G., Sudarshan, V., Lim, F. S., & Ong, Y. S. (2025). Measuring undergraduate students' reliance on generative AI during problem-solving: Scale development and validation. Computers & Education, 234, 105329. https://doi.org/10.1016/j.compedu.2025.105329", "authors": "Chenyu Hou, Gaoxia Zhu, Vidya Sudarshan, Fun Siong Lim, Yew Soon Ong", "year": 2025, "venue": "Computers & Education", "doi": "10.1016/j.compedu.2025.105329", "url": "https://doi.org/10.1016/j.compedu.2025.105329", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "partial", "flag_reason": "EFA, two CFAs and alpha .84 are reported, but validity evidence beyond internal structure is limited.", "verified": "2026-09-29" }, { "id": "negative-attitudes-genai-academic-writing", "name": "Scale of College Students' Negative Attitudes Toward Generative AI-Assisted Academic Writing", "acronym": null, "summary": "A 16-item scale of students' worries that GenAI writing help makes their language generic, outsources their thinking, and blurs who the author is. Use it in research on GenAI in academic writing and student authorship.", "target": "Generative AI writing tools (e.g., GPT-based assistants) in academic writing", "constructs": [ "attitudes", "academic-integrity", "learning" ], "populations": [ "university students" ], "items": 16, "response": null, "subscales": [ { "name": "Language homogenization", "items": 4, "description": "Concern that AI makes one's writing style generic and uniform" }, { "name": "Thought outsourcing", "items": 6, "description": "Concern about handing idea generation and critical reasoning over to AI" }, { "name": "Identity ambiguity", "items": 6, "description": "Uncertainty about authorship and ownership of AI-assisted writing" } ], "psychometrics": { "structure": "Three random subsamples of 673: item analysis and EFA (parallel analysis, 3 factors); CFA χ²/df 3.78, CFI .981, TLI .978, RMSEA .064, SRMR .016; MG-CFA invariance", "reliability": "α .975 overall; subscale α .937 / .969 / .977; CR .938–.975; AVE .79–.87", "validity": [ "Content validity (expert I-CVI)", "Convergent validity (AVE > .50, CR > .70)", "Discriminant validity (Fornell–Larcker; narrow margin between thought outsourcing and identity ambiguity, r = .887)", "Configural to strict invariance across gender, birthplace and education level" ], "samples": [ { "n": 2019, "country": "China (mostly; some respondents in Australia)", "population": "College students, ~95% undergraduates, split into three subsamples of 673" } ] }, "status": "preprint", "citation": "Yang, Y., Shi, Z., Cui, T., Yang, X., & Tan, Z. (2025). Development and validation of a scale assessing college students' negative attitudes toward generative AI-assisted academic writing [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-6974623/v1", "authors": "Yang, Y., Shi, Z., Cui, T., Yang, X., & Tan, Z.", "year": 2025, "venue": "Research Square", "doi": "10.21203/rs.3.rs-6974623/v1", "url": "https://doi.org/10.21203/rs.3.rs-6974623/v1", "preprint_url": "https://doi.org/10.21203/rs.3.rs-6974623/v1", "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "strategic-ai-use-srl-inventory", "name": "Strategic Use of AI in Self-Regulated Learning Inventory", "acronym": null, "summary": "A 35-item inventory of how often university students use AI tools strategically in their own learning: planning, refining and checking work with AI, and regulating their AI use through prompting, tool choice, effort and ethical limits. Use it to diagnose students' GenAI learning strategies, especially in language courses.", "target": "generative AI tools (e.g., ChatGPT, DeepSeek) in self-regulated learning; items say 'AI tools'", "constructs": [ "learning", "ethics-concerns" ], "populations": [ "university students" ], "items": 35, "response": "5-point frequency (never, rarely, sometimes, often, always)", "subscales": [ { "name": "Planning & performing", "items": 9, "description": "Using AI to plan, find and process sources, and generate ideas" }, { "name": "Product refinement", "items": 3, "description": "Using AI to polish the language and content of one's own work" }, { "name": "Evaluation & assessment", "items": 3, "description": "Using AI to evaluate one's work against task criteria" }, { "name": "Language compensation", "items": 3, "description": "Using AI to translate or look up meaning" }, { "name": "Output-focused metacognitive regulation", "items": 5, "description": "Designing prompts, monitoring and evaluating AI output, adjusting prompts" }, { "name": "Tool-focused metacognitive regulation", "items": 5, "description": "Purposefully selecting and appraising AI tools and adapting to tool failures" }, { "name": "Behavioral regulation", "items": 4, "description": "Putting effort into learning how to use AI (self-exploration, peers, online resources)" }, { "name": "Ethical regulation", "items": 3, "description": "Restricting AI use for ethical and academic-integrity reasons" } ], "psychometrics": { "structure": "EFA (principal axis, oblimin; KMO .918) 8 factors, 64.9% variance, plus Rasch (unidimensionality and item fit per factor) in Survey 1; CFA in Survey 2 favouring the 8-factor correlated model over alternatives; second-order model with 2 regulation levels also acceptable (CFI .91, RMSEA .05, SRMR .06)", "reliability": "α .73–.87 (Survey 1); α .77–.90 (Survey 2)", "validity": [ "Convergent validity: AVE > .50 except behavioral regulation (.49)", "Discriminant validity: HTMT < .85", "Measurement invariance (configural to residual) across gender and academic discipline", "Correlations with degree of AI exposure; gender and discipline differences" ], "samples": [ { "n": 488, "country": "China", "population": "university students (Survey 1: EFA/Rasch)" }, { "n": 707, "country": "China", "population": "university students (Survey 2: CFA/invariance)" } ] }, "status": "published", "citation": "Liu, X., Xiao, Y., & Li, D. (2025). Assessing strategic use of artificial intelligence in self-regulated learning: Instrument development and evidence from Chinese university students. International Journal of Educational Technology in Higher Education, 22, Article 69. https://doi.org/10.1186/s41239-025-00567-5", "authors": "Xiaohua Liu; Yangyu Xiao; Danling Li", "year": 2025, "venue": "International Journal of Educational Technology in Higher Education", "doi": "10.1186/s41239-025-00567-5", "url": "https://doi.org/10.1186/s41239-025-00567-5", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-attitudes-acceptability-he-tang", "name": "Student Attitudes and Acceptability of GenAI Tools in Higher Education Scale", "acronym": null, "summary": "A survey of undergraduates' attitudes toward generative AI covering four areas (concern about its effects on society, how clear institutional policy is, fairness and trust, and impact on careers), plus how acceptable they find GenAI for specific writing and coursework tasks. Useful for quick campus-level surveys of student views on GenAI policy and use.", "target": "generative AI tools (e.g., ChatGPT)", "constructs": [ "attitudes", "academic-integrity", "ethics-concerns" ], "populations": [ "university students" ], "items": 24, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree) for attitude items; 5-point acceptability scale (1 = always unacceptable to 5 = always acceptable) for task items", "subscales": [ { "name": "Societal concern", "items": 5, "description": "Concerns about GenAI's broader effects on society, e.g., bias, privacy, environmental impact" }, { "name": "Policy clarity", "items": 3, "description": "Perceived clarity of institutional and instructor GenAI policies" }, { "name": "Fairness and trust", "items": 3, "description": "Perceived fairness of GenAI policy and trust in instructor judgment" }, { "name": "Career impact", "items": 2, "description": "Beliefs about GenAI's relevance to educational and career paths" }, { "name": "Writing task acceptability", "items": 6, "description": "Acceptability of GenAI for writing tasks such as grammar correction, idea generation, paraphrasing (not factor-analysed)" }, { "name": "Coursework task acceptability", "items": 5, "description": "Acceptability of GenAI for coursework and study support such as study guides, coding, translation (not factor-analysed)" } ], "psychometrics": { "structure": "EFA (minres, oblimin) on 13 attitudinal items: 4 factors, 47.3% of variance; RMSR .03, TLI .934, RMSEA .053. Acceptability groups not factor-analysed. No CFA.", "reliability": "α .59–.82 (policy .71; fairness/trust .59; career .81; societal .76; writing acceptability .78; coursework acceptability .82)", "validity": [ "Content validity via expert panel review", "Exploratory demographic group differences (gender, home language, year of study, first-generation status)" ], "samples": [ { "n": 297, "country": "United States", "population": "undergraduates at one university (University of Notre Dame authors)" } ] }, "status": "preprint", "citation": "Tang, X., Chen, S., Cheng, Y., Chawla, N. V., Metoyer, R., & Ambrose, G. A. (2025). Understanding student attitudes and acceptability of GenAI tools in higher ed: Scale development and evaluation [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2508.01926", "authors": "Xiuxiu Tang; Si Chen; Ying Cheng; Nitesh V. Chawla; Ronald Metoyer; G. Alex Ambrose", "year": 2025, "venue": "arXiv", "doi": "10.48550/arXiv.2508.01926", "url": "https://doi.org/10.48550/arXiv.2508.01926", "preprint_url": "https://arxiv.org/abs/2508.01926", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "sla-gai", "name": "Student Learning Agency Scale in Generative AI-Supported Contexts", "acronym": "SLA-GAI", "summary": "A 34-item, 10-factor scale of how much agency university students keep when learning with generative AI: self-regulation abilities (e.g., goal setting, reflection), active and responsible actions, and self-efficacy and volition. Use it to see whether GenAI supports or undermines students' ownership of their learning.", "target": "generative AI-supported learning", "constructs": [ "learning", "self-efficacy" ], "populations": [ "university students" ], "items": 34, "response": null, "subscales": [ { "name": "Self-cognition", "items": null, "description": "Awareness of oneself as a learner (ability dimension)" }, { "name": "Goal setting", "items": null, "description": "Setting learning goals (ability dimension)" }, { "name": "Self-adjustment", "items": null, "description": "Adjusting learning strategies (ability dimension)" }, { "name": "Self-reflection", "items": null, "description": "Reflecting on one's learning (ability dimension)" }, { "name": "Selective action", "items": null, "description": "Choosing when and how to use GenAI (action dimension)" }, { "name": "Responsible action", "items": null, "description": "Using GenAI responsibly (action dimension)" }, { "name": "Participative action", "items": null, "description": "Actively participating in learning (action dimension)" }, { "name": "Self-efficacy", "items": null, "description": "Confidence in learning with GenAI (mental-characteristics dimension)" }, { "name": "Volition", "items": null, "description": "Persistence and will to learn (mental-characteristics dimension)" }, { "name": "(10th factor not named in abstract)", "items": null, "description": "The abstract reports ten factors but names only nine" } ], "psychometrics": { "structure": "Stage 1: EFA (n = 268) to refine items. Stage 2: 425 responses randomly split into n1 = 268 (EFA) and n2 = 245 (CFA) as reported, giving 10 factors and 34 items", "reliability": "Not reported in abstract", "validity": [ "Content via literature review and expert group interview", "Factorial validity via CFA" ], "samples": [ { "n": 268, "country": "China", "population": "university students (stage 1)" }, { "n": 425, "country": "China", "population": "university students (stage 2, split for EFA/CFA)" } ] }, "status": "published", "citation": "Xia, L., Shen, K., Sun, H., An, X., & Dong, Y. (2025). Developing and validating the student learning agency scale in generative artificial intelligence (AI)-supported contexts. Education and Information Technologies, 30(10), 13999–14021. https://doi.org/10.1007/s10639-024-13137-5", "authors": "Liangliang Xia; Kexin Shen; Herui Sun; Xin An; Yan Dong", "year": 2025, "venue": "Education and Information Technologies", "doi": "10.1007/s10639-024-13137-5", "url": "https://doi.org/10.1007/s10639-024-13137-5", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [], "evidence": "unconfirmed", "flag_reason": "The abstract reports EFA and CFA only; reliability and validity could not be checked because the full text was inaccessible.", "verified": "2026-09-29" }, { "id": "students-gai-tools-use-scale", "name": "Students' Use of Generative AI Tools Scale", "acronym": null, "summary": "A 26-item scale of how higher-education students use generative AI tools such as ChatGPT in academic work: for research, for writing and communication, how much they rely on it, and whether they use it ethically. Use it to describe students' GenAI practices or to evaluate responsible-use initiatives.", "target": "Generative AI tools in academic work (e.g., ChatGPT, Gemini, Copilot)", "constructs": [ "acceptance-use", "reliance", "academic-integrity" ], "populations": [ "university students" ], "items": 26, "response": "Likert-type statements (number of points not stated)", "subscales": [ { "name": "Research tool", "items": 7, "description": "Using GenAI to find answers, sources, outlines and ideas for schoolwork" }, { "name": "Communication tool", "items": 6, "description": "Using GenAI to improve clarity, wording, grammar and tone, or to write essays and reports" }, { "name": "Reliance", "items": 8, "description": "Depending on and preferring GenAI for learning and completing tasks, including in tests" }, { "name": "Ethical use", "items": 5, "description": "Transparent, accountable, reflective and responsible GenAI use" } ], "psychometrics": { "structure": "40 statements drafted from 20 student interviews and a scoping review. EFA (ML, varimax) retained 26 items on 4 factors (59.8% variance). CFA: χ²/df = 3.51, CFI = .932, TLI = .916, RMSEA = .031, SRMR = .044; loadings .723-.930", "reliability": "α .822 (research), .836 (communication), .913 (reliance), .840 (ethical use)", "validity": [ "Convergent validity: AVE .664-.794 for all factors" ], "samples": [ { "n": 506, "country": "Philippines", "population": "higher education students from three Manila universities (pilot 1)" }, { "n": 287, "country": "Philippines", "population": "higher education students (pilot 2, online)" } ] }, "status": "published", "citation": "Barcelona, A., & Dela Cruz, S. R. (2025). Development and validation of a scale measuring students' use of generative artificial intelligence tools. International Journal of Evaluation and Research in Education, 14(5), 3612-3621. https://doi.org/10.11591/ijere.v14i5.34809", "authors": "Alvin Barcelona; Sam Rhoy Dela Cruz", "year": 2025, "venue": "International Journal of Evaluation and Research in Education (IJERE)", "doi": "10.11591/ijere.v14i5.34809", "url": "https://ijere.iaescore.com/index.php/IJERE/article/view/34809", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "t-gaic", "name": "Teachers' GenAI Competencies instrument", "acronym": "T-GAIC", "summary": "Measures pre-service and in-service teachers' competencies for bringing generative AI into teaching: technical skill with GenAI tools, fitting GenAI to pedagogy, preparing students to use it well, ongoing professional learning, and awareness of risks and ethics. Use it in teacher-education research or to plan professional development.", "target": "Generative AI in teaching", "constructs": [ "literacy", "workplace", "ethics-concerns" ], "populations": [ "teachers & academics" ], "items": 36, "response": "5-point Likert (strongly disagree to strongly agree)", "subscales": [ { "name": "Technological proficiency of GenAI (TP)", "items": 5, "description": "Operating GenAI tools competently" }, { "name": "Pedagogical compatibility of GenAI in teaching (PC)", "items": 7, "description": "Aligning GenAI use with pedagogy and teaching goals" }, { "name": "Preparing students with effective practices of GenAI (PS)", "items": 8, "description": "Guiding students to use GenAI effectively" }, { "name": "GenAI-related professional development and communication (GPD)", "items": 8, "description": "Ongoing learning and professional communication about GenAI" }, { "name": "Risk and ethical awareness of GenAI in education (REA)", "items": 8, "description": "Recognising misuse, over-reliance, misinformation and ethical issues" } ], "psychometrics": { "structure": "Five factors. EFA on 70% of the sample (72% of variance explained), then CFA on the remaining 30%: RMSEA = .078, CFI = .946, TLI = .934", "reliability": "Total α .98; subscale α .90–.96; CR .898–.953", "validity": [ "Content and face validity from expert review and think-aloud with pre-service teachers", "Item discrimination (extreme groups) and item–total correlations", "Convergent: AVE .524–.739; CR > .70", "Discriminant: HTMT .38–.66 and Fornell–Larcker criterion" ], "samples": [ { "n": 478, "country": "China", "population": "pre-service and in-service teachers from two regions" } ] }, "status": "published", "citation": "Shi, L. (2025). Assessing teachers' generative artificial intelligence competencies: Instrument development and validation. Education and Information Technologies, 30(16), 23365–23384. https://doi.org/10.1007/s10639-025-13684-5", "authors": "Lehong Shi", "year": 2025, "venue": "Education and Information Technologies", "doi": "10.1007/s10639-025-13684-5", "url": "https://link.springer.com/article/10.1007/s10639-025-13684-5", "preprint_url": null, "items_available": false, "language": "Chinese", "adaptations": [ { "language": "Turkish", "country": "Türkiye", "citation": "Erol, A., Temur, M., & Erol, M. (2026). Turkish adaptation of the artificial intelligence literacy and generative artificial intelligence competency scales. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2026.2668027", "doi": "10.1080/10494820.2026.2668027", "url": "https://www.tandfonline.com/doi/full/10.1080/10494820.2026.2668027", "status": "published", "notes": "N = 451 primary and preschool teachers. CFA confirmed the five-factor T-GAIC structure (alongside the four-factor AILST). Internal consistency was acceptable for all dimensions. No other validity evidence is mentioned in the abstract. Seen: abstract (OpenAlex) and Crossref (published 1 May 2026, pp. 1–15)." } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "tillmi", "name": "Trust-In-LLMs Index", "acronym": "TILLMI", "summary": "A short 6-item index of how much people trust large language models. It splits into affective 'closeness' with LLMs and cognitive 'reliance' on them. Use it as a brief trust measure in surveys of LLM users.", "target": "Large language models (e.g., ChatGPT, GPT-4)", "constructs": [ "trust", "reliance", "relationships" ], "populations": [ "general adults" ], "items": 6, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Closeness with LLMs", "items": 3, "description": "Affective trust: dismay if LLM interactions stopped, expecting caring responses to wellbeing concerns, investing time in prompts (Q2-Q4)." }, { "name": "Reliance on LLMs", "items": 3, "description": "Cognitive trust: relying on LLMs not to make one's job harder, keeping the last word, trusting LLMs more than people (Q6-Q8)." } ], "psychometrics": { "structure": "8 initial items, pre-tested with GPT-4-simulated responses. EGA and EFA (PAF, oblimin; n = 260) gave 2 factors; Q1 and Q5 dropped. CFA (n = 261): scaled χ²(8) = 13.012, p = .111, robust RMSEA = .046, CFI = .995, TLI = .991, SRMR = .022; factor covariance .94", "reliability": "α .893 (Closeness), .781 (Reliance)", "validity": [ "Convergent: Kendall τ with openness (.10/.14), extraversion (.16), and cognitive flexibility (.07, Reliance only)", "Negative associations with text-derived (DASentimental) depression (-.12), stress (-.13/-.15) and anxiety (-.11); the abstract also reports a negative link with neuroticism" ], "samples": [ { "n": 1000, "country": "United States", "population": "Adults (Bilendi online panel); 521 LLM users analysed (EFA 260, CFA 261), 479 non-users excluded" } ] }, "status": "preprint", "citation": "De Duro, E. S., Veltri, G. A., Golino, H., & Stella, M. (2025). Measuring and identifying factors of individuals' trust in large language models [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2502.21028", "authors": "Edoardo Sebastiano De Duro; Giuseppe Alessandro Veltri; Hudson Golino; Massimo Stella", "year": 2025, "venue": "arXiv (cs.HC)", "doi": "10.48550/arXiv.2502.21028", "url": "https://arxiv.org/abs/2502.21028", "preprint_url": "https://arxiv.org/abs/2502.21028", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "chatgpt-attitude-scale-cas", "name": "ChatGPT Attitude Scale (5-T model)", "acronym": "CAS", "summary": "A 21-item scale of college students' attitudes toward ChatGPT across five roles: Tool, Tutor, Talk (emotional or social chat), Trend and Threat. Use it for a broad profile of how students see and use ChatGPT.", "target": "ChatGPT", "constructs": [ "attitudes", "acceptance-use", "ethics-concerns" ], "populations": [ "university students" ], "items": 21, "response": "4-point scale (anchors not verified)", "subscales": [ { "name": "Tool", "items": 4, "description": "Everyday information use (news, travel, trends)" }, { "name": "Tutor", "items": 5, "description": "Learning skills, subject knowledge, languages, translation" }, { "name": "Threat", "items": 6, "description": "Worries about privacy, job loss, plagiarism, unreliability" }, { "name": "Talk", "items": 3, "description": "Emotional, personal-growth or social chatting" }, { "name": "Trend", "items": 3, "description": "Seeing ChatGPT as the future and a driver of change" } ], "psychometrics": { "structure": "EFA gave a 5-factor solution on one half-sample (n≈256); CFA on the other half: χ²(184)=357.40, CFI .97, RMSEA .061, SRMR .081", "reliability": "α .87 (total)", "validity": [ "Known-groups: men scored higher on the total and on Tool, Tutor and Trend; no gender difference on Talk or Threat" ], "samples": [ { "n": 516, "country": "Taiwan", "population": "college students" } ] }, "status": "published", "citation": "Yu, S.-C., Huang, Y.-M., & Wu, T.-T. (2024). Tool, threat, tutor, talk, and trend: College students' attitudes toward ChatGPT. Behavioral Sciences, 14(9), 755. https://doi.org/10.3390/bs14090755", "authors": "Sen-Chi Yu, Yueh-Min Huang, Ting-Ting Wu", "year": 2024, "venue": "Behavioral Sciences", "doi": "10.3390/bs14090755", "url": "https://doi.org/10.3390/bs14090755", "preprint_url": null, "items_available": true, "language": "Not reported", "adaptations": [ { "language": "Persian", "country": "Iran", "citation": "Mastour, H., Moghadasin, M., Caliskan, S. A., Keshavarz, F., Shadravan, M. M., & Sohrabi, S. (2026). Decoding medical students' attitudes toward ChatGPT: Psychometric evaluation of the Persian version of the attitudes toward ChatGPT questionnaire. Computers in Human Behavior Reports, 22, 101069. https://doi.org/10.1016/j.chbr.2026.101069", "doi": "10.1016/j.chbr.2026.101069", "url": "https://doi.org/10.1016/j.chbr.2026.101069", "status": "published", "notes": "421 medical students; Beaton framework. EFA kept the 5 factors (44% variance) and CFA fit acceptably (CFI .922, TLI .905, RMSEA .051); α .63–.82, ω .67–.84. Discriminant validity was mostly supported, with Tool/Tutor overlap and an item-21 Heywood case; the authors call it preliminary. Abstract only (OpenAlex)." } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "kohler-hartig-chatgpt-scales", "name": "ChatGPT Knowledge, Usage/Use-Value and Attitude Scales", "acronym": null, "summary": "Three short scales covering what students know about ChatGPT, which academic tasks they have used it for (and whether it helped), and their attitude toward it. Use them together or separately in higher-education surveys.", "target": "ChatGPT", "constructs": [ "acceptance-use", "attitudes", "literacy" ], "populations": [ "university students" ], "items": 24, "response": "Knowledge: correct/incorrect (plus 'I don't know', scored incorrect); Usage: 4 options (no / no but can imagine / yes not helpful / yes helpful); Attitude: 4-point Likert (absolutely disagree to absolutely agree)", "subscales": [ { "name": "Knowledge about ChatGPT", "items": 6, "description": "True/false knowledge of ChatGPT's abilities and limits" }, { "name": "Actual usage / Use value", "items": 10, "description": "Task-specific use (e.g., summarising, feedback), scored for actual use and perceived value" }, { "name": "Attitude towards ChatGPT", "items": 8, "description": "Evaluative attitude toward ChatGPT" } ], "psychometrics": { "structure": "Separate unidimensional CFAs: knowledge CFI .95, RMSEA .06; actual usage CFI .95, RMSEA .08; use value CFI .93, RMSEA .10; attitude CFI .96, RMSEA .08. Joint 4-factor CFA for latent correlations", "reliability": "Knowledge α .57/ω .84; Actual usage α .78/ω .91; Use value α .78/ω .92; Attitude α .70/ω .82", "validity": [ "Latent intercorrelations among scales (all positive except knowledge–attitude, which is negative)", "Known-groups: differences by academic field and semester (MANOVA with Bonferroni-adjusted ANOVAs)" ], "samples": [ { "n": 693, "country": "Germany", "population": "students at various universities" } ] }, "status": "published", "citation": "Köhler, C., & Hartig, J. (2024). ChatGPT in higher education: Measurement instruments to assess student knowledge, usage, and attitude. Contemporary Educational Technology, 16(4), ep528. https://doi.org/10.30935/cedtech/15144", "authors": "Carmen Köhler, Johannes Hartig", "year": 2024, "venue": "Contemporary Educational Technology", "doi": "10.30935/cedtech/15144", "url": "https://doi.org/10.30935/cedtech/15144", "preprint_url": null, "items_available": true, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "chatgpt-literacy-scale", "name": "ChatGPT Literacy Scale", "acronym": "CLS", "summary": "A 25-item self-report questionnaire on how well university students can use ChatGPT. It covers the technical side, judging its answers critically, prompting it, using it creatively, and using it ethically. Use it when you need a multi-dimensional, ChatGPT-specific literacy score.", "target": "ChatGPT", "constructs": [ "literacy", "ethics-concerns" ], "populations": [ "university students" ], "items": 25, "response": null, "subscales": [ { "name": "Technical proficiency", "items": null, "description": "Operating ChatGPT and understanding what it can and cannot do" }, { "name": "Critical evaluation", "items": null, "description": "Judging the accuracy and reliability of ChatGPT outputs" }, { "name": "Communication proficiency", "items": null, "description": "Communicating with and prompting ChatGPT effectively" }, { "name": "Creative application", "items": null, "description": "Using ChatGPT for novel and creative purposes" }, { "name": "Ethical competence", "items": null, "description": "Using ChatGPT responsibly and ethically" } ], "psychometrics": { "structure": "Five factors from EFA, then CFA (25 final items)", "reliability": "Original values not seen (paywalled). Chilean adaptation: α .963, CR > .70. Turkish adaptation: high internal consistency reported.", "validity": [ "Content validity: a Delphi panel of 10 experts with content validity ratios, plus a student pilot focus group", "Construct validity: EFA/CFA with 822 students", "Turkish adaptation: convergent validity with AI literacy and attitudes, discriminant validity, and configural-to-strict invariance", "Chilean adaptation: AVE > .50 and discriminant validity" ], "samples": [ { "n": 822, "country": "South Korea", "population": "college students" } ] }, "status": "published", "citation": "Lee, S., & Park, G. (2024). Development and validation of ChatGPT literacy scale. Current Psychology, 43(21), 18992–19004. https://doi.org/10.1007/s12144-024-05723-0", "authors": "Seyoung Lee; Gain Park", "year": 2024, "venue": "Current Psychology", "doi": "10.1007/s12144-024-05723-0", "url": "https://link.springer.com/article/10.1007/s12144-024-05723-0", "preprint_url": null, "items_available": false, "language": "Korean", "adaptations": [ { "language": "Turkish", "country": "Türkiye", "citation": "Bulut, A., Aba, G., Mutlu, C., Güven Uslu, P., & Çalışkan, R. E. (2026). Validation of the Turkish version of the ChatGPT Literacy Scale. Measurement: Interdisciplinary Research and Perspectives. Advance online publication. https://doi.org/10.1080/15366367.2026.2665784", "doi": "10.1080/15366367.2026.2665784", "url": "https://www.tandfonline.com/doi/full/10.1080/15366367.2026.2665784", "status": "published", "notes": "N = 572 university students (mean age 21.3). CFA confirmed the 5-factor structure. Evidence from CTT and IRT. Convergent validity with AI literacy measures and discriminant validity from broader AI constructs. Verified via OpenAlex metadata and abstract (online 11 May 2026, pp. 1–19)." }, { "language": "Spanish", "country": "Chile", "citation": "Del Río, C. S., Monge-Rogel, R., Fuentes-Lama, R., & Fernández-Ochoa, H. (2026). Cultural adaptation and validation of a ChatGPT literacy scale for university students in Chile. Current Psychology, 45(7), Article 778. https://doi.org/10.1007/s12144-026-09314-z", "doi": "10.1007/s12144-026-09314-z", "url": "https://link.springer.com/article/10.1007/s12144-026-09314-z", "status": "published", "notes": "Back-translation, 12 expert judges, and N = 620 students at 3 Chilean universities. EFA/CFA reproduced 5 factors (73.5% of variance). α = .963 and CR > .70. AVE > .50 and low inter-dimension correlations. Spanish items in Appendix Table 5. Verified from the Springer page (abstract, metadata, appendix headings)." } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "chatgpt-usage-scale", "name": "ChatGPT Usage Scale", "acronym": null, "summary": "A 15-item scale of how postgraduate students use ChatGPT: as a writing aid, for academic task support, and how much they rely on and trust it. Use it to profile ChatGPT use in graduate education; the Reliance and Trust subscale fits trust/reliance research.", "target": "ChatGPT", "constructs": [ "acceptance-use", "reliance", "trust" ], "populations": [ "graduate students", "university students" ], "items": 15, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Academic Writing Aid", "items": 8, "description": "Using ChatGPT for paraphrasing, idea generation, drafting, and counterarguments." }, { "name": "Academic Task Support", "items": 4, "description": "Using ChatGPT for writer's block, organizing thoughts, study materials, and information retrieval." }, { "name": "Reliance and Trust", "items": 3, "description": "Trusting ChatGPT outputs, seeking its feedback, and recognizing its limitations." } ], "psychometrics": { "structure": "EFA (PCA, varimax) reduced 39 items to 3 factors, 49.19% variance; CFA χ²(87)=223.60, CMIN/DF=2.57, CFI=.917, TLI=.900, RMSEA=.060", "reliability": "α .848; ω .849; CR .855", "validity": [ "Convergent validity: AVE = .664; standardized loadings .434–.728" ], "samples": [ { "n": 443, "country": "Egypt", "population": "Postgraduate students at two universities (Kafr el-Sheikh, Al-Azhar)" } ] }, "status": "published", "citation": "Nemt-allah, M., Khalifa, W., Badawy, M., Elbably, Y., & Ibrahim, A. (2024). Validating the ChatGPT Usage Scale: Psychometric properties and factor structures among postgraduate students. BMC Psychology, 12, 497. https://doi.org/10.1186/s40359-024-01983-4", "authors": "Mohamed Nemt-allah, Waleed Khalifa, Mahmoud Badawy, Yasser Elbably, Ashraf Ibrahim", "year": 2024, "venue": "BMC Psychology", "doi": "10.1186/s40359-024-01983-4", "url": "https://bmcpsychology.biomedcentral.com/articles/10.1186/s40359-024-01983-4", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [ { "language": "Indonesian", "country": "Indonesia", "citation": "Hafsari, N. L., & Mahanani, F. K. (2025). Adaptation and validation of the ChatGPT Usage Scale in Indonesia: Exploring challenges of high usage among students. Jurnal Kependidikan, 11(4), 1399–1409. https://doi.org/10.33394/jk.v11i4.18058", "doi": "10.33394/jk.v11i4.18058", "url": "https://doi.org/10.33394/jk.v11i4.18058", "status": "published", "notes": "542 university students; ITC guidelines; Aiken's V .894. Initial CFA fit poorly; after 8 items were removed, a 7-item model fit (CFI .949, RMSEA .056); α .703. The Reliance and Trust factor was not retained. Abstract only (OpenAlex)." }, { "language": "Turkish", "country": "Türkiye", "citation": "Batuk, B., Türk, N., & Özmen, M. (2025). Psychometric properties of the Turkish version of the ChatGPT Usage Scale. Siirt Sosyal Araştırmalar Dergisi, 4(2), 47–60.", "doi": null, "url": "https://dergipark.org.tr/en/pub/ssad/article/1832126", "status": "published", "notes": "332 undergraduate and graduate students; CFA showed good fit for the 3 subscales; α/ω .87–.96; convergent validity. Abstract and citation metadata read on DergiPark; no DOI listed." }, { "language": "Turkish", "country": "Türkiye", "citation": "Batuk, B., Türk, N., & Özmen, M. (2025). Psychometric properties of the Turkish version of the ChatGPT Usage Scale. Siirt Journal of Social Research, 4(2), 47–60. https://doi.org/10.5281/zenodo.18095268", "doi": "10.5281/zenodo.18095268", "url": "https://doi.org/10.5281/zenodo.18095268", "status": "published", "notes": "n = 332; CFA of the 15-item, three-factor model: χ²/df = 3.61, CFI = .95, IFI = .95, TLI = .94, NFI = .94, GFI = .89, AGFI = .85, RMSEA = .08; loadings .41–.94 (a second-order model is also mentioned); α/ω: Academic Writing Aid .95, Academic Task Support .87, Reliance and Trust .90, total .96; CR .80–.96" } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "genai-literacy-tool-k12-teachers-korea", "name": "Generative AI Literacy Measurement Tool for Elementary and Secondary Teachers", "acronym": null, "summary": "A 31-item Korean self-report tool of K-12 teachers' generative AI literacy: how well they understand GenAI, use it in teaching, and judge its value. Use it to find teachers' GenAI training needs.", "target": "generative AI in K-12 teaching", "constructs": [ "literacy", "workplace" ], "populations": [ "teachers & academics" ], "items": 31, "response": null, "subscales": [ { "name": "Understanding Generative AI", "items": null, "description": "Knowledge of what GenAI is and how it works (two sub-factors)" }, { "name": "Educational utilization of Generative AI", "items": null, "description": "Using GenAI in teaching (two sub-factors)" }, { "name": "Value of Generative AI", "items": null, "description": "Values and ethics around GenAI (two sub-factors)" } ], "psychometrics": { "structure": "EFA (n = 107) then CFA (n = 137) supported 3 factors and 6 sub-factors; CFI and TLI > .90", "reliability": "α > .90", "validity": [ "Expert review (content)", "Factorial validity (CFA); sub-factor intercorrelations all ≥ .70" ], "samples": [ { "n": 107, "country": "South Korea", "population": "teachers enrolled in an AI-convergence education graduate program (pilot/EFA)" }, { "n": 137, "country": "South Korea", "population": "elementary and secondary teachers (CFA)" } ] }, "status": "published", "citation": "Kim, I., Yi, S., & Kim, K. (2024). A study on the development and validation of generative AI literacy measurement tool for elementary and secondary teachers. Journal of Learner-Centered Curriculum and Instruction, 24(22), 697–708. https://doi.org/10.22251/jlcci.2024.24.22.697", "authors": "Injoo Kim; Soyul Yi; Kwihoon Kim", "year": 2024, "venue": "Journal of Learner-Centered Curriculum and Instruction", "doi": "10.22251/jlcci.2024.24.22.697", "url": "https://doi.org/10.22251/jlcci.2024.24.22.697", "preprint_url": null, "items_available": false, "language": "Korean", "adaptations": [], "evidence": "partial", "flag_reason": "Validity rests on CFA fit and expert review; very high correlations between factors raise discriminant-validity concerns.", "verified": "2026-09-29" }, { "id": "genai-acceptance-scale-gaias", "name": "Generative Artificial Intelligence Acceptance Scale", "acronym": "GAIAS", "summary": "A 20-item scale based on UTAUT (a standard technology-acceptance model) that measures how far university students accept generative AI tools such as ChatGPT. It covers perceived usefulness for performance, ease of use, supporting conditions and social influence. Use it to measure GenAI adoption or readiness among students.", "target": "generative AI applications (e.g., ChatGPT)", "constructs": [ "acceptance-use" ], "populations": [ "university students" ], "items": 20, "response": "5-point Likert (1 = strongly disagree, 5 = strongly agree)", "subscales": [ { "name": "Performance expectancy", "items": 7, "description": "Belief that GenAI improves academic performance" }, { "name": "Effort expectancy", "items": 5, "description": "Ease of using GenAI tools" }, { "name": "Facilitating conditions", "items": 3, "description": "Resources and support for using GenAI" }, { "name": "Social influence", "items": 5, "description": "Perceived social pressure/endorsement to use GenAI" } ], "psychometrics": { "structure": "EFA (n=338): 4 factors, 20 items, 78.35% of variance; CFA (n=250) confirmed the 4-factor structure", "reliability": "α .97 (total); test-retest .95", "validity": [ "Face/content validity via expert review", "Item discrimination (upper vs lower 27%)", "Factorial validity via CFA" ], "samples": [ { "n": 627, "country": "Türkiye", "population": "university students who had used GenAI tools such as ChatGPT (2022-2023)" } ] }, "status": "published", "citation": "Karaoglan Yilmaz, F. G., Yilmaz, R., & Ceylan, M. (2024). Generative artificial intelligence acceptance scale: A validity and reliability study. International Journal of Human–Computer Interaction, 40(24), 8703–8715. https://doi.org/10.1080/10447318.2023.2288730", "authors": "Fatma Gizem Karaoglan Yilmaz; Ramazan Yilmaz; Mehmet Ceylan", "year": 2024, "venue": "International Journal of Human–Computer Interaction", "doi": "10.1080/10447318.2023.2288730", "url": "https://doi.org/10.1080/10447318.2023.2288730", "preprint_url": null, "items_available": false, "language": "English", "adaptations": [ { "language": "Chinese", "country": "China", "citation": "Tian, X., & Zhang, Q. (2026). Psychometric validation of the Chinese generative artificial intelligence acceptance scale among university students. Frontiers in Psychology, 17, 1966312. https://doi.org/10.3389/fpsyg.2026.1966312", "doi": "10.3389/fpsyg.2026.1966312", "url": "https://doi.org/10.3389/fpsyg.2026.1966312", "status": "published", "notes": "Full text read on frontiersin.org. n=604 students at six universities (Yunnan, Anhui, Zhejiang). Four-factor, 20-item model: robust CFI .993, TLI .992, RMSEA .020. α .757–.873; ω .810–.905; CR .773–.888. AVE >.50 except PE (.494); HTMT .542–.775 (Fornell–Larcker not fully met). Gender invariance supported. No external/criterion measures. Chinese items in Supplementary Table S1." } ], "evidence": "partial", "flag_reason": "The original reports only content, factorial and item-discrimination validity; a Chinese adaptation later added convergent, discriminant and invariance evidence.", "verified": "2026-09-29" }, { "id": "genaias-efl-learners", "name": "Generative Artificial Intelligence Attitude Scale for EFL Learners", "acronym": "GenAIAS", "summary": "A 33-item scale of English-as-a-foreign-language learners' attitudes toward GenAI: learning value, enjoyment, usefulness and interest. Use it in language-learning research.", "target": "Generative AI", "constructs": [ "attitudes", "learning" ], "populations": [ "university students" ], "items": 33, "response": null, "subscales": [ { "name": "Learning/Utility", "items": null, "description": "Value of GenAI for learning" }, { "name": "Enjoyment", "items": null, "description": "Enjoyment of using GenAI" }, { "name": "Usefulness", "items": null, "description": "Perceived usefulness of GenAI" }, { "name": "Interest", "items": null, "description": "Interest in GenAI" } ], "psychometrics": { "structure": "EFA and CFA in two independent samples; 4 factors", "reliability": "Satisfactory internal consistency (values not in abstract)", "validity": [ "Convergent and discriminant validity", "Measurement invariance across gender", "Positive correlation with daily internet use" ], "samples": [ { "n": null, "country": "Türkiye", "population": "two independent samples of university EFL students" } ] }, "status": "published", "citation": "Orhan, A., Aydın Yıldız, T., & Çınar Yağcı, Ş. (2024). Assessing EFL learners' attitudes on generative artificial intelligence: Development and validation of generative artificial intelligence attitude scale for EFL learners (GenAIAS). Journal of Research on Technology in Education, 58(3), 494–514. https://doi.org/10.1080/15391523.2024.2437744", "authors": "Ali Orhan, Tuğba Aydın Yıldız, Şule Çınar Yağcı", "year": 2024, "venue": "Journal of Research on Technology in Education", "doi": "10.1080/15391523.2024.2437744", "url": "https://doi.org/10.1080/15391523.2024.2437744", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "pcus", "name": "Problematic ChatGPT Use Scale", "acronym": "PCUS", "summary": "An 11-item screener for addiction-like, problematic use of ChatGPT (preoccupation, withdrawal, tolerance, loss of control, conflict, escape). Use it when you want a single score for how dysregulated someone's ChatGPT use is.", "target": "ChatGPT", "constructs": [ "dependence", "health" ], "populations": [ "general adults", "university students", "employees & professionals" ], "items": 11, "response": "4-point Likert (1 = strongly disagree to 4 = strongly agree)", "subscales": [ { "name": "(unidimensional)", "items": 11, "description": "Items map onto IGD criteria (preoccupation, withdrawal, tolerance, loss of control, conflict, mood modification/escape) and are summed into one score." } ], "psychometrics": { "structure": "Unidimensional; EFA then CFA (revised model acceptable fit)", "reliability": "α .936; 4-week test-retest .904", "validity": [ "Correlates: positive correlations with usage time and depression", "Group differences: men scored higher than women; no differences by age, user category or satisfaction" ], "samples": [ { "n": 1040, "country": "Taiwan", "population": "adults (undergraduates, graduate students, working professionals; mean age 25.5)" } ] }, "status": "published", "citation": "Yu, S.-C., Chen, H.-R., & Yang, Y.-W. (2024). Development and validation the Problematic ChatGPT Use Scale: A preliminary report. Current Psychology, 43(31), 26080–26092. https://doi.org/10.1007/s12144-024-06259-z", "authors": "Sen-Chi Yu; Hong-Ren Chen; Yu-Wen Yang", "year": 2024, "venue": "Current Psychology", "doi": "10.1007/s12144-024-06259-z", "url": "https://link.springer.com/article/10.1007/s12144-024-06259-z", "preprint_url": null, "items_available": false, "language": "Not reported", "adaptations": [ { "language": "Turkish", "country": "Turkey", "citation": "Maral, S., Naycı, N., Bilmez, H., Erdemir, E. İ., & Satici, S. A. (2025). Problematic ChatGPT Use Scale: AI-human collaboration or unraveling the dark side of ChatGPT. International Journal of Mental Health and Addiction, 24(3), 2369–2395. https://doi.org/10.1007/s11469-025-01509-y", "doi": "10.1007/s11469-025-01509-y", "url": "https://link.springer.com/article/10.1007/s11469-025-01509-y", "status": "published", "notes": "PCGUS, a 9-item Turkish version (items 1 and 4 dropped for low loadings or conceptual ambiguity). Study I N=391, Study II N=473; CFA unidimensional; configural, metric and scalar invariance across gender; IRT discriminations > 1.0. α .903, ω .896, λ6 .928 (Study I); α .877, ω .875 (Study II). Correlated with AI addiction (r=.425), internet gaming disorder and internet addiction, and negatively with conscientiousness. Publisher full text seen." }, { "language": "Turkish", "country": "Turkey", "citation": "Tiring, O., & İslamoğlu, E. (2026). Adaptation study of the Problematic ChatGPT Use Scale into Turkish. Hacettepe University Journal of Education, 41(2), 360–370. https://doi.org/10.16986/hunefd.1747472", "doi": "10.16986/hunefd.1747472", "url": "https://dergipark.org.tr/en/pub/hunefd/article/1747472", "status": "published", "notes": "A second, independent Turkish adaptation keeping all 11 items on a 5-point format; N=301 adults aged 18–60. EFA/CFA single factor; α .91; criterion correlation with an AI Dependence Scale. Crossref abstract only (CFA fit values reported by a previous agent not re-verified)." } ], "verified": "2026-09-28", "evidence": "verified" }, { "id": "semantic-differential-ai-trust-scale", "name": "Semantic Differential Scale for Affective and Cognitive AI Trust", "acronym": null, "summary": "A 27-item set of adjective-pair (semantic differential) scales measuring cognitive trust (18 items) and affective trust (9 items) in AI agents. It was built with LLM-powered conversational agents in mind. Use it when you need to separate emotional trust in a chatbot from competence-based trust.", "target": "AI agents / LLM-powered conversational agents (validation used ChatGPT-generated chatbot conversations)", "constructs": [ "trust" ], "populations": [ "general adults" ], "items": 27, "response": "Semantic differential (bipolar adjective pairs); 5-step (−2 to +2) in development, 7-point (−3 to +3) in validation studies", "subscales": [ { "name": "Cognitive trust", "items": 18, "description": "Perceived reliability, competence, rationality, transparency and integrity of the AI agent (e.g., unreliable–reliable, incompetent–competent)." }, { "name": "Affective trust", "items": 9, "description": "Emotional warmth and care shown by the agent (e.g., apathetic–empathetic, rude–cordial, impatient–patient)." } ], "psychometrics": { "structure": "Development: EFA on 33 adjective pairs (n=151, 302 responses) gave 2 factors (43% and 23% variance) after removing 6 cross-loading pairs. Study A: CFA (DWLS) excellent fit (CFI 1.000, TLI 1.003, RMSEA .000, SRMR .038). Study B: EFA with the MDMT moral trust items retained 3 factors (70% variance).", "reliability": "α .98 (cognitive), .96 (affective)", "validity": [ "Concurrent validity with a single-item general trust measure: development r = .881 (cognitive), .253 (affective); Study A r = .546 and .486", "Construct validity: scales were sensitive to manipulated cognitive and affective trustworthiness in ChatGPT-generated conversations", "Discriminant validity from MDMT moral trust (Study B): separate factor, and moral trust did not predict general trust" ], "samples": [ { "n": 151, "country": "United States", "population": "MTurk adults, scenario-based development survey (302 responses)" }, { "n": 44, "country": "United States", "population": "MTurk adults, within-subjects experiment with ChatGPT-generated conversations (88 responses; CFA)" }, { "n": 168, "country": "Not stated (Prolific)", "population": "Prolific adults, 2x2 between-subjects experiment" } ] }, "status": "published", "citation": "Shang, R., Hsieh, G., & Shah, C. (2024). Trusting your AI agent emotionally and cognitively: Development and validation of a semantic differential scale for AI trust. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7, 1343–1356. https://doi.org/10.1609/aies.v7i1.31728", "authors": "Ruoxi Shang, Gary Hsieh, Chirag Shah", "year": 2024, "venue": "Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES)", "doi": "10.1609/aies.v7i1.31728", "url": "https://ojs.aaai.org/index.php/AIES/article/view/31728", "preprint_url": "https://arxiv.org/abs/2408.05354", "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "evt-genai-perceptions", "name": "Expectancy-Value Theory (EVT) instrument for student perceptions of generative AI", "acronym": null, "summary": "Measures students' knowledge of GenAI, its perceived value (attainment, intrinsic, utility) and its perceived cost, and links these to intention to use. Use it to study why students do or don't adopt GenAI.", "target": "Generative AI (e.g., ChatGPT)", "constructs": [ "acceptance-use", "attitudes", "literacy" ], "populations": [ "graduate students", "university students" ], "items": 20, "response": "5-point Likert (1 = strongly disagree to 5 = strongly agree)", "subscales": [ { "name": "Knowledge", "items": 5, "description": "Self-rated knowledge of GenAI" }, { "name": "Perceived value", "items": 11, "description": "Attainment, intrinsic and utility value of using GenAI" }, { "name": "Perceived cost", "items": 4, "description": "Costs and concerns of using GenAI" } ], "psychometrics": { "structure": "CFA only (theory-driven, no EFA) for knowledge, perceived value (3 sub-scales) and cost; χ²/df < 3, RMSEA < .07, SRMR < .08; perceived value TLI .948", "reliability": "α .812/.876/.746; CR .822/.763/.873", "validity": [ "Convergent validity (AVE/CR; some AVE < .50)", "Discriminant validity: HTMT .002–.660 (< .85)", "Correlations with intention to use GenAI (value strongly +, cost weakly −)" ], "samples": [ { "n": 405, "country": "Hong Kong", "population": "university students (undergraduate and postgraduate)" } ] }, "status": "published", "citation": "Chan, C. K. Y., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10, 64. https://doi.org/10.1186/s40561-023-00284-4", "authors": "Cecilia Ka Yuk Chan, Wenxin Zhou", "year": 2023, "venue": "Smart Learning Environments", "doi": "10.1186/s40561-023-00284-4", "url": "https://doi.org/10.1186/s40561-023-00284-4", "preprint_url": null, "items_available": true, "language": "English", "adaptations": [], "verified": "2026-09-28", "evidence": "verified" }, { "id": "tame-chatgpt", "name": "Technology Acceptance Model Edited to Assess ChatGPT Adoption", "acronym": "TAME-ChatGPT", "summary": "A TAM-based questionnaire with an attitude scale (for anyone who has heard of ChatGPT) and a usage scale (for people who have used it). Use it to measure students' perceived usefulness, ease of use, risks and anxiety about ChatGPT, especially in health-care or university education.", "target": "ChatGPT", "constructs": [ "acceptance-use", "attitudes", "anxiety" ], "populations": [ "university students" ], "items": 27, "response": "5-point Likert (strongly disagree to strongly agree)", "subscales": [ { "name": "Perceived risks (attitude scale)", "items": 5, "description": "General risks seen in ChatGPT (reverse-coded)" }, { "name": "Attitude to technology / social influence (attitude scale)", "items": 5, "description": "Positive attitude to technology and influence of others" }, { "name": "Anxiety (attitude scale)", "items": 3, "description": "Anxiety about ChatGPT as a technology (reverse-coded)" }, { "name": "Perceived usefulness (usage scale)", "items": 6, "description": "How useful ChatGPT is for study tasks" }, { "name": "Perceived risk of use (usage scale)", "items": 3, "description": "Risks tied to one's own use (reverse-coded)" }, { "name": "Perceived ease of use (usage scale)", "items": 2, "description": "How easy ChatGPT is to use" }, { "name": "Behavioral/cognitive factors (usage scale)", "items": 3, "description": "Behavioural and cognitive drivers of use" } ], "psychometrics": { "structure": "EFA: 3-factor attitude scale (13 items; KMO .823) and 4-factor usage scale (14 items; KMO .702). CFA in a multinational follow-up: attitude RMSEA .060, SRMR .032, CFI .965, TLI .954 (after added error correlations); usage RMSEA .077, SRMR .050, CFI .923, TLI .901", "reliability": "Attitude α .876/.858/.827; usage α .885/.718/.824/.781", "validity": [ "Known-groups: prior ChatGPT users scored higher on technology/social influence than non-users (M 21 vs 17.6, p<.001); no difference on risk or anxiety", "CFA confirmation in a 5-country Arab sample (Abdaljaleel et al., 2024, Scientific Reports 14, 1983)" ], "samples": [ { "n": 458, "country": "Jordan", "population": "health-care university students (only 55 had used ChatGPT and answered the usage scale)" }, { "n": 2240, "country": "Iraq, Kuwait, Egypt, Lebanon, Jordan", "population": "university students (multinational CFA; attitude n=1048, usage n=551)" } ] }, "status": "published", "citation": "Sallam, M., Salim, N. A., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D., Hallit, R., & Hallit, S. (2023). Assessing health students' attitudes and usage of ChatGPT in Jordan: Validation study. JMIR Medical Education, 9, e48254. https://doi.org/10.2196/48254", "authors": "Sallam, M., Salim, N. A., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D., Hallit, R., Hallit, S.", "year": 2023, "venue": "JMIR Medical Education", "doi": "10.2196/48254", "url": "https://mededu.jmir.org/2023/1/e48254", "preprint_url": "https://doi.org/10.2196/preprints.48254", "items_available": true, "language": "English", "adaptations": [ { "language": "Arabic & English", "country": "Iraq, Kuwait, Egypt, Lebanon, Jordan", "citation": "Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., ... Sallam, M. (2024). A multinational study on the factors influencing university students' attitudes and usage of ChatGPT. Scientific Reports, 14, 1983. https://doi.org/10.1038/s41598-024-52549-8", "doi": "10.1038/s41598-024-52549-8", "url": "https://doi.org/10.1038/s41598-024-52549-8", "status": "published", "notes": "CFA of the attitude scale (n=1048; RMSEA .060, CFI .965 after modification) and the usage scale (n=551; RMSEA .077, CFI .923) in N = 2240 students; survey given in Arabic and English. Preprint: 10.21203/rs.3.rs-3400248/v1 (resolves to Research Square). Full text read via PMC10806219." }, { "language": "Turkish", "country": "Türkiye", "citation": "Küçük, E., Meral, B., Yeşilçiçek Çalık, K., & Çapık, C. (2025). The Turkish version of the Technology Acceptance Model-based scale TAME-ChatGPT: A validity and reliability study. International Journal of Human–Computer Interaction, 41(23), 14734–14745. https://doi.org/10.1080/10447318.2025.2487876", "doi": "10.1080/10447318.2025.2487876", "url": "https://doi.org/10.1080/10447318.2025.2487876", "status": "published", "notes": "541 nursing students; the attitude scale explained 72.8% and the usage scale 73.9% of variance; subscale α > .72. Metadata and abstract confirmed via OpenAlex; abstract only." } ], "verified": "2026-09-28", "evidence": "verified" } ] }