An Empirical Investigation on Psychosocial Determinants of AI Dependence

Aim To examine how gender, self-efficacy, attachment styles, and social influence AI dependence among college students.
Background Artificial Intelligence (AI) has become increasingly important in daily life, and researchers have identified psychological factors as significant determinants of AI dependence.
Methods A total of 154 college students aged between 18 and 28 were selected. Data was collected through a questionnaire, and participants’ AI Usage, Self-Efficacy, Attachment style, and Dependency were assessed through scales based on the Technology Acceptance Model (extended TAM), New General Self-Efficacy Scale (NGSES), Experience in Close Relationship Revised Scale (ECR-R), and Scale for Dependence on Artificial Intelligence (DIA). AI-Self Efficacy was measured through the AI-Self Efficacy Scale (AISES).
Results
An independent samples t-test revealed that males (M = 14.06, SD = 3.77, n = 88) had significantly higher AI dependence than females (M = 12.33, SD = 4.36, n = 46) (t = 2.28, p < .05), with no significant difference between general or AI self-efficacy and gender. A moderate positive correlation was observed between DAI and AISES subscales. DAI showed significant positive correlations with AISE-AS.
Conclusion
Males showed higher AI-dependency, while attachment styles were not significantly related. Human-like interaction and perceived trust in AI predicted AI Dependence.

Artificial Intelligence (AI), Psychological factor, Gender differences, College Students

1. Asio, J. M. R., & Sardina, D. P. (2025). Gender differences on the impact of AI self-efficacy on AI anxiety through AI self-competency: A moderated mediation analysis. Journal of Pedagogical Research, 9(2), 55–71. https://doi.org/10.33902/JPR.202533231 [Google Scholar] [Crossref]

2. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191 [Google Scholar] [Crossref]

3. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. [Google Scholar] [Crossref]

4. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman. [Google Scholar] [Crossref]

5. Baitekov, A. (2023). Student’s perception of Chat GPT: A technology acceptance model study. International Education Review, 114, 1–14. https://doi.org/10.58693/ier.114 [Google Scholar] [Crossref]

6. Bewersdorff, A. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students’ AI self-efficacy. Computers and Education: Artificial Intelligence, 100340, 1–12. https://doi.org/10.1016/j.caeai.2024.100340 [Google Scholar] [Crossref]

7. Bowlby, J. (1969). Attachment and loss: Vol. 1. Attachment. Basic Books. [Google Scholar] [Crossref]

8. Chen, G., & Gully, S. M. (2001). Validation of a new general self-efficacy scale. Organizational Research Methods, 4(1), 62–83. https://doi.org/10.1177/109442810141004 [Google Scholar] [Crossref]

9. Cheng, S., Guo, J., Wang, Z., & Luo, Y. (2022). Anthropomorphic chatbots and customer engagement: The mediating role of perceived social presence. Journal of Retailing and Consumer Services, 64, 102756. https://doi.org/10.1016/j.jretconser.2021.102756 [Google Scholar] [Crossref]

10. Dahri, N. A., & Yahaya, N. (2024). Extended extended TAM based acceptance of AI-powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(7), e29317. https://doi.org/10.1016/j.heliyon.2024.e29317 [Google Scholar] [Crossref]

11. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

12. Estrada-Araoz, E. G., Mamani-Roque, M., Quispe-Aquise, J., Manrique-Jaramillo, Y. V., & Cruz-Laricano, E. O. (2025). Academic self-efficacy and dependence on artificial intelligence in a sample of university students. Sapienza: International Journal of Interdisciplinary Studies, 6(1). https://doi.org/10.51798/sijis.v6i1.916 [Google Scholar] [Crossref]

13. Fang, X. (2025). Emotional attachment to AI chatbots: Predicting user dependency patterns. Computers in Human Behavior, 144, 107784. https://doi.org/10.1016/j.chb.2023.107784 [Google Scholar] [Crossref]

14. Fraley, R. C., Waller, N. G., & Brennan, K. A. (2000). An item response theory analysis of self-report measures of adult attachment. Journal of Personality and Social Psychology, 78(2), 350–365. https://doi.org/10.1037/0022-3514.78.2.350 [Google Scholar] [Crossref]

15. Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006 [Google Scholar] [Crossref]

16. Gillath, O., Ai, T., Branicky, M. S., Keshmiri, S., Davison, R. B., & Spaulding, R. (2021). Attachment and trust in artificial intelligence. Computers in Human Behavior, 115, 106607. https://doi.org/10.1016/j.chb.2020.106607 [Google Scholar] [Crossref]

17. Heslin, Peter A. and Klehe, Ute-Christine, Self-Efficacy (September 22, 2006). ENCYCLOPEDIA OF INDUSTRIAL / ORGANIZATIONAL PSYCHOLOGY, S. G. Rogelberg, ed., Vol. 2, pp.705-708, Thousand Oaks: Sage, 2006, Available at SSRN: https://ssrn.com/abstract=1150858 [Google Scholar] [Crossref]

18. Hoffman, D. L., Novak, T. P., & Stein, R. (2018). Digital dependence: Implications of the human–technology interaction. Journal of Consumer Research, 44(3), 381–400. https://doi.org/10.1093/jcr/ucx066 [Google Scholar] [Crossref]

19. Hong, J.-W. (2021). I was born to love AI: The influence of social status on AI self-efficacy and intentions to use AI. International Journal of Communication, 15, 5236–5256. https://www.researchgate.net/publication/357700678_I_Was_Born_to_Love_AI_The_Influence_of_Social_Status_on_AI_Self-Efficacy_and_Intentions_to_Use_AI [Google Scholar] [Crossref]

20. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392 [Google Scholar] [Crossref]

21. JisMakridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. https://doi.org/10.1016/j.futures.2017.03.006 [Google Scholar] [Crossref]

22. Iverson, G., Brooks, B., Ashton, V., Johnson, L., & Gualtieri, C. (2009). Does familiarity with computers affect computerized neuropsychological test performance? Journal of Clinical and Experimental Neuropsychology, 31(5), 594–604. https://doi.org/10.1080/13803390802372125 [Google Scholar] [Crossref]

23. Computers in Human Behavior Reports, 100315, 1–12. https://doi.org/10.1016/j.chbr.2023.100315 [Google Scholar] [Crossref]

24. LianMorales-García, W. C., & Sairitupa-Sanchez, L. Z. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Frontiers in Education, 9, 1323898. https://doi.org/10.3389/feduc.2024.1323898 [Google Scholar] [Crossref]

25. Moriuchi, E. (2021). Anthropomorphism of AI-enabled voice assistants: A persuasion knowledge perspective. Journal of Business Research, 124, 192–205. https://doi.org/10.1016/j.jbusres.2020.11.048 [Google Scholar] [Crossref]

26. Nolemi, S. (2024). Overtrust in AI systems: Cognitive and social implications. Human-Computer Interaction Journal, 40(2), 145–162. [Google Scholar] [Crossref]

27. Octaberlina, L. R., Muslimin, A. I., Chamidah, D., Surur, M., & Mustikawan, A. (2024). Exploring the impact of AI threats on originality and critical thinking in academic writing. Edelweiss Applied Science and Technology, 8(6). https://doi.org/10.55214/25768484.v8i6.3878 [Google Scholar] [Crossref]

28. Otis, N. G., Delecourt, S., Cranney, K., & Koning, R. (2024). Global evidence on gender gaps and generative AI (Working Paper No. 25-023). Harvard Business School Working Paper Series. https://www.hbs.edu/faculty/Pages/item.aspx?num=66548 [Google Scholar] [Crossref]

29. Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886 [Google Scholar] [Crossref]

30. Parsakia, K. (2023). The effect of chatbots and AI on the self-efficacy, self-esteem, problem-solving and critical thinking of students. KMAN Publication Inc. [Google Scholar] [Crossref]

31. Rodríguez-Ruiz, J., & Marín-López, I. (2025). Is artificial intelligence related to self-control, self-esteem and self-efficacy among university students? Education and Information Technologies. https://doi.org/10.1007/s10639-024-12906-6 [Google Scholar] [Crossref]

32. Russo, F. (2025). Gendered perceptions of AI: Implications for user engagement and acceptance. Journal of Artificial Intelligence Research, 52(1), 88–105. https://doi.org/10.3389/fpsyg.2025.1559457 [Google Scholar] [Crossref]

33. Singh, P. D. (2024). Generative AI through the lens of the technology acceptance model. Journal of Education, Management and Social Sciences, 7(1), 1–10. [Google Scholar] [Crossref]

34. Wang, Y.-Y., & Chuang, Y.-W. (2023). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12015-w [Google Scholar] [Crossref]

35. Wu, H., Li, J., & Chen, S. (2025). Attachment styles and the adoption of conversational AI in mental health support. Computers in Human Behavior, 145, 107810. https://doi.org/10.1016/j.chb.2023.107810 [Google Scholar] [Crossref]

36. Wu, X., Liew, K., & Dorahy, M. J. (2025). Trust, Anxious Attachment, and Conversational AI Adoption Intentions in Digital Counseling: A Preliminary Cross-Sectional Questionnaire Study. JMIR AI, 4, e68960. https://doi.org/10.2196/68960 [Google Scholar] [Crossref]

37. Yildiz Durak, H. (2023). Conversational agent-based guidance: Examining the effect of chatbot usage frequency and satisfaction on visual design self-efficacy, engagement, satisfaction, and learner autonomy. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11149-7 [Google Scholar] [Crossref]

38. Zhang, Q., Chen, H., & Luo, S. (2024). AI dependence and its impact on students’ problem-solving capacities. Computers & Education, 192, 104723. [Google Scholar] [Crossref]

39. Zhang, S., & Zhao, X. (2024). Do you have an AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. Humanities and Social Sciences Communications, 11(1), 467. https://doi.org/10.1186/s41239-024-00467-0 [Google Scholar] [Crossref]

• A Comparative Study of Adjustment Level, Parental Income and Academic Achievement of Adolescent Girls
• Increase in STIs among Adolescents in Masvingo District
• A Correlation between SDG 4 (Quality Education) and Emotional Disposition of Teacher Educators in Hyderabad District
• Ear Piercing Behaviour and Self-Esteem among Male University Students in Nigeria
• A Study of Religiosity and Psychological Well-Being