RESEARCH PAPER
Development of the GAI Dependence Scale: a validity and reliability study
1
Fujian Polytechnic Normal University, Fuqing, China
2
Fujian Normal University, Fuzhou, China
These authors had equal contribution to this work
Submission date: 2025-09-09
Final revision date: 2026-01-29
Acceptance date: 2026-02-05
Online publication date: 2026-05-15
KEYWORDS
TOPICS
ABSTRACT
Background:
While individuals use generative artificial intelligence (GAI) to enhance their productivity, they may also develop psychological and behavioral over-reliance on these tools. Evaluating the extent of an individual’s dependence on GAI is crucial for conducting related research.
Participants and procedure:
The research involved 627 participants. Exploratory and confirmatory factor analysis was conducted to determine the construct validity of the scale. The results supported a structure consisting of four sub-dimensions and 19 items. The sub-dimensions are Usage Intensity, Content Dependence, Withdrawal Symptoms, and Negative Consequences.
Results:
Confirmatory factor analysis indicated good model fit. Significant relationships were found with the GAI Dependence Scale, AI Dependence Scale (version of six items), and Behavioral Intention Scale during the analysis of the scale’s criterion validity. Cronbach’s α internal consistency, and the test-retest method were used to assess the reliability of the scale. Cronbach’s α internal consistency coefficient for the total score was found to be .92 for Sample 1 and .90 for Sample 2. Additionally, the test-retest reliability over a four-week interval yielded a coefficient of .81. Both coefficients are considered to indicate acceptable levels of reliability.
Conclusions:
The scale is an effective and reliable tool for assessing individual dependence on GAI.
While individuals use generative artificial intelligence (GAI) to enhance their productivity, they may also develop psychological and behavioral over-reliance on these tools. Evaluating the extent of an individual’s dependence on GAI is crucial for conducting related research.
Participants and procedure:
The research involved 627 participants. Exploratory and confirmatory factor analysis was conducted to determine the construct validity of the scale. The results supported a structure consisting of four sub-dimensions and 19 items. The sub-dimensions are Usage Intensity, Content Dependence, Withdrawal Symptoms, and Negative Consequences.
Results:
Confirmatory factor analysis indicated good model fit. Significant relationships were found with the GAI Dependence Scale, AI Dependence Scale (version of six items), and Behavioral Intention Scale during the analysis of the scale’s criterion validity. Cronbach’s α internal consistency, and the test-retest method were used to assess the reliability of the scale. Cronbach’s α internal consistency coefficient for the total score was found to be .92 for Sample 1 and .90 for Sample 2. Additionally, the test-retest reliability over a four-week interval yielded a coefficient of .81. Both coefficients are considered to indicate acceptable levels of reliability.
Conclusions:
The scale is an effective and reliable tool for assessing individual dependence on GAI.
REFERENCES (40)
1.
Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, 10. https://doi.org/10.1186/s41239....
2.
Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M. K., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10, 311. https://doi.org/10.1057/s41599....
3.
Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook Addiction Scale. Psychological Reports, 110, 501–517. https://doi.org/10.2466/02.09.....
4.
Calzada, K. P. D. (2024). Anti-dependency teaching strategy for innovation in the age of AI among technology-based students. Environment and Social Psychology, 9, 1–18. https://doi.org/10.59429/esp.v....
5.
Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being: Development of a theory-based cognitive-behavioral measurement instrument. Computers in Human Behavior, 18, 553–575. https://doi.org/10.1016/S07475....
6.
Chen, S., Weng, L., Su, Y., Wu, H., & Yang, P. (2003). Study on the development and psychometric characteristics of Chinese Internet Addiction Scale. Chinese Journal of Psychology, 45, 279–294.
7.
Chen, Z., Zhou, M., Li, Z., & Wang, H. (2024). Empowerment or disempowerment: The influence of using AI on creative personality. Advances in Psychological Science, 32, 1990–2004. https://doi.org/10.3724/SP.J.1....
8.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijin....
9.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijin....
10.
Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage Publications.
11.
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022). Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. bioRxiv [preprint]. https://doi.org/10.1101/2022.1....
12.
Güler, Ç. (2021). Adaptation of the Chabot Emotional Differentiation Scale: Turkish version validity and reliability study. Current Issues in Personality Psychology, 9, 53-65. https://doi.org/10.5114/cipp.2....
13.
Groth-Marnat, G. (2009). Handbook of psychological assessment. John Wiley & Sons.
14.
Hu, B., Mao, Y., & Kim, K. J. (2023). How social anxiety leads to problematic use of conversational AI: The roles of loneliness, rumination, and mind perception. Computers in Human Behavior, 145, 107760. https://doi.org/10.1016/j.chb.....
15.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lind....
16.
Kasturiratna, K. 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....
17.
King, M. R. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 16, 1–2. https://doi.org/10.1007/s12195....
19.
Krou, M. R., Fong, C. J., & Hoff, M. A. (2021). Achievement motivation and academic dishonesty: a meta-analytic investigation. Educational Psychology Review, 33, 427–458. https://doi.org/10.1007/s10648....
20.
Łaguna, M., Mielniczuk, E., & Razmus, W. (2023). Development and initial validation of the Daily Goal Realization Scale. Current Issues in Personality Psychology, 11, 240–250. https://doi.org/10.5114/cipp/1....
21.
Lei, L., & Li, H. (2003). Definition and measurement of pathological use of the Internet. Advances in Psychological Science, 11, 73–77.
22.
Lei, L., & Yang, Y. (2007). Development and validation of the Adolescent Pathological Internet Use Scale. Acta Psychologica Sinica, 39, 688–696.
23.
Leung, L. (2008). Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in Hong Kong. Journal of Children and Media, 2, 93–113. https://doi.org/10.1080/174827....
24.
Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023). Generating scholarly content with ChatGPT: Ethical challenges for medical publishing. The Lancet Digital Health, 5, e105–e106. https://doi.org/10.1016/S2589-....
25.
Lozano-Blasco, R., Robres, A. Q., & Sánchez, A. S. (2022). Internet addiction in young adults: a meta-analysis and systematic review. Computers in Human Behavior, 130, 107201. https://doi.org/10.1016/j.chb.....
26.
Marsh, H. W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling, 16, 439–476. https://doi.org/10.1080/107055....
27.
Murtagh, F., & Heck, A. (2012). Multivariate data analysis. Springer Science & Business Media.
28.
Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014). Human performance consequences of stages and levels of automation: an integrated meta-analysis. Human Factors, 56, 476-488. https://doi.org/10.1177/001872....
29.
Pan, Y. C., Chiu, Y. C., & Lin, Y. H. (2020). Systematic review and meta-analysis of epidemiology of internet addiction. Neuroscience & Biobehavioral Reviews, 118, 612–622. https://doi.org/10.1016/j.neub....
30.
Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and consumers: Benefits, pitfalls and future research agenda. International Journal of Consumer Studies, 47, 1213–1225. https://doi.org/10.1111/ijcs.1....
31.
Sage, R., & Matteucci, R. (2024). Technology and learning: Issues vital to address. Brill.
32.
Strzelecki, A. (2023). Students’ acceptance of ChatGPT in higher education: an extended unified theory of acceptance and use of technology. Innovative Higher Education, 49, 223–245. https://doi.org/10.1007/s10755....
33.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157–178. https://doi.org/10.2307/414104....
34.
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: a critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11, 7–30. https://doi.org/10.15678/EBER.....
35.
Wu, M. (2010). Questionnaire statistical analysis practice: SPSS operation and application. Chongqing University Press.
36.
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. Research Square [preprint]. https://doi.org/10.21203/rs.3.....
37.
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. Cyberpsychology & Behavior, 1, 237–244. https://doi.org/10.1089/cpb.19....
38.
Yuan, K. H., & Bentler, P. M. (2001). Effect of outliers on estimators and tests in covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 54, 161–175. https://doi.org/10.1348/000711....
39.
Zhang, G., Yang, X., Tu, X., Ding, N., & Lau, J. T. F. (2020). Prospective relationships between mobile phone dependence and mental health status among Chinese undergraduate students with college adjustment as a mediator. Journal of Affective Disorders, 260, 498–505. https://doi.org/10.1016/j.jad.....
40.
Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. International Journal of Educational Technology in Higher Education, 21, 34. https://doi.org/10.1186/s41239....
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