Capability Gains and Psychological Risks of AI Users/Consumers: A Dual-Path Model of Digital Well-Being
Main Article Content
Abstract
The rapid diffusion of generative artificial intelligence (AI) has reshaped students’ learning practices, creating both performance-related benefits and psychological risks. This study examines how academic performance, smart learning, and AI-related creepiness influence students’ mental well-being through a dual-path model. Drawing on Social Cognitive Theory and Consumer Vulnerability Theory, this study positions academic performance and smart learning as capability-gain factors, while creepiness is treated as a psychological risk factor. It also examines the moderating roles of self-efficacy and perceived behavioral control as psychological boundary conditions. Data were collected through an online survey of 1,486 AI users in Indonesia and analyzed using Moderated Regression Analysis and Hayes’ PROCESS Macro. The findings show that academic performance and smart learning positively influence mental well-being, while creepiness does not directly reduce mental well-being. Self-efficacy strengthens the positive effects of academic performance and smart learning on mental well-being. In addition, perceived behavioral control significantly moderates the relationship between creepiness and mental well-being, indicating that students’ perceived control shapes how AI-related creepiness is interpreted psychologically. This study contributes to digital well-being literature by showing that generative AI can simultaneously generate capability gains and psychological risks, and that its effect on mental well-being depends on students’ psychological resources and perceived control.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
https://creativecommons.org/licenses/by-sa/4.0/
References
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(1), 10.
Ajzen, I. (2002). Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior1. Journal ofApplied Social Psychology, 32(4), 665-683.
Alqurni, J. S. (2026). Exploring the role of agentic AI in fostering self-efficacy, autonomy support, and self-learning motivation in higher education. Frontiers in Artificial Intelligence, 9, 1738774. https://doi.org/10.3389/frai.2026.1738774
Au, A., Caltabiano, N. J., & Vaksman, O. (2023). The impact of sense of belonging, resilience, time management skills and academic performance on psychological well-being among university students. Cogent Education, 10(1), 2215594.
Baek, T. H., & Kim, M. (2023). Is ChatGPT scary good? How user motivations affect creepiness and trust in generative artificial intelligence. Telematics and Informatics, 83, 102030.
Baker, S. M., LaBarge, M., & Baker, C. N. (2015). Consumer vulnerability: Foundations, phenomena, and future investigations. In Consumer vulnerability (pp. 13-30). Routledge.
Baker, S. M., Gentry, J. W., & Rittenburg, T. L. (2005). Building understanding of the domain of consumer vulnerability. Journal of macromarketing, 25(2), 128-139.
Barbayannis, G., Bandari, M., Zheng, X., Baquerizo, H., Pecor, K. W., & Ming, X. (2022). Academic stress and mental well-being in college students: Correlations, affected groups, and COVID-19. Frontiers in psychology, 13, 886344.
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26). https://doi.org/10.1073/pnas.2422633122
Bhagat, S., & Sveum, E. C. (2025). Navigating AI Teaching Assistants: How Perceptions and Interactions Shape Student Engagement, Cognition, and Learning. Journal of the Association for Information Systems. https://aisel.aisnet.org/icis2025/learn_curricula/learn_curricula/8
Bandura, A. (2006). Guide for constructing self-efficacy scales.
Bandura, A. (2001). Social Cognitive Theory: An Agentic Perspective. Annual Review of Psychology, 52(1), 1. https://doi.org/10.1146/annurev.psych.52.1.1
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28), 2.
Bhat, R., Kowshik, S., Suresh, S., Alamelu, G., Gite, S., & Ahmad, A. (2025, January 1). Digital Companionship or Psychological Risk? The Role of AI Characters in Shaping Youth Mental Health. In Asian Journal of Psychiatry (Vol. 104, p. 104356). Elsevier BV. https://doi.org/10.1016/j.ajp.2024.104356
Brauner, P., Glawe, F., Liehner, G. L., Vervier, L., & Ziefle, M. (2024). Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2411.19356
Brown, S. D., Lent, R. W., & Larkin, K. C. (1989). Self-efficacy as a moderator of scholastic aptitude-academic performance relationships. Journal of vocational behavior, 35(1), 64-75.
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS-Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change-and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014.
Chew, S., Razak, S. S. A., & Lo, Y. T. (2025). The relationship between artificial intelligence usage and academic performance of students at a Malaysian public university. International Journal of Pervasive Computing and Communications, 1. https://doi.org/10.1108/ijpcc-05-2025-0186
Chirayath, G., Premamalini, K., & Joseph, J. (2025). Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping. Frontiers in Psychology, 16, 1699320. https://doi.org/10.3389/fpsyg.2025.1699320
Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal of business and psychology, 29(1), 1-19.
Dang, Q., & Li, G. (2025). Unveiling trust in AI: the interplay of antecedents, consequences, and cultural dynamics. AI & Society. https://doi.org/10.1007/s00146-025-02477-6
Ding, K. (2025). A Review Study on the Formation Factors of AI Anxiety and Its Impact on Employees. In Advances in economics, business and management research/Advances in Economics, Business and Management Research (p. 738). Atlantis Press. https://doi.org/10.2991/978-94-6463-811-0_77
Ding, N., Hu, L., Kim, K.-T., & Chen, M. (2026). When Generative Artificial Intelligence Becomes a Colleague: Dual Pathways of Empowerment and Depletion in University Design Teachers’ Work Behaviors. Sustainability, 18(4), 1775. https://doi.org/10.3390/su18041775
Glickman, M. E., & Zhang, Y. (2024). AI and Generative AI for Research Discovery and Summarization. Harvard Data Science Review, 6(2). https://doi.org/10.1162/99608f92.7f9220ff
Grøtan, K., Sund, E. R., & Bjerkeset, O. (2019). Mental health, academic self-efficacy and study progress among college students–The SHoT study, Norway. Frontiers in psychology, 10, 45.
Hagger, M. S., Cheung, M. W. L., Ajzen, I., & Hamilton, K. (2022). Perceived behavioral control moderating effects in the theory of planned behavior: A meta-analysis. Health Psychology, 41(2), 155.
Hayat, A. A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: a structural equation model. BMC medical education, 20(1), 76.
Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press.
Heydarnejad, T. (2025). Disclosing motivation and educational self-efficacy in AI-enhanced EFL learning via a lenz into reflective thinking, academic mindfulness, and teacher support. Discover Psychology, 5(1). https://doi.org/10.1007/s44202-025-00502-9
Hu, R. J., Gao, Q., Hu, R., Liu, G., & Zhang, L. (2025). The relationship between digital technostress and cyber moral disengagement among college students: a moderated mediation model of psychological resilience, self-efficacy, and online self-control. Frontiers in Psychology, 16, 1706794. https://doi.org/10.3389/fpsyg.2025.1706794
Huang, Y., Yu, T., Chen, Y., Tian, Y., & Yim, J. (2025). Smart Learning with Generative AI Tools in Higher Education: An Integrated SOR–SDT Model of Student Creative Confidence and Engagement. Applied Sciences, 16(1), 63. https://doi.org/10.3390/app16010063
Hwang, G. J. (2014). Definition, framework and research issues of smart learning environments-a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 4.
Iacobucci, D., Schneider, M. J., Popovich, D. L., & Bakamitsos, G. A. (2017). Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux. Behavior research methods, 49(1), 403-404.
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77, 102521.
Kokolakis, S. (2017). Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & security, 64, 122-134.
Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review. Frontiers in Psychology, 16, 1498132.
Kirk, H. R., Davidson, H. A., Saunders, E. R., Luettgau, L., Vidgen, B., Hale, S. A., & Summerfield, C. (2025). Neural steering vectors reveal dose and exposure-dependent impacts of human-AI relationships. ArXiv.Org. https://doi.org/10.48550/arxiv.2512.01991
Kuupole, E., Wemegah, R., Awinzeligo, M. H., Adjei, D. A., Armah, L., & Agale, A. S. (2025). Exploring Factors that Influence Consumers’ Behaviour Toward Second-Hand Clothing Purchase. African Journal Of Applied Research, 11(2), 419. https://doi.org/10.26437/ajar.v11i2.1043
Lemon, N., & Harris, M. (2025). Understanding regional, rural and remote teacher wellbeing narratives through podcasting: a qualitative analysis of multiple stakeholder perspectives. The Australian Educational Researcher. https://doi.org/10.1007/s13384-025-00914-5
Lera, M. J., Leon-Perez, J. M., & Ruiz-Zorrilla, P. (2023). Effective educational practices and students’ well-being: the mediating role of students’ self-efficacy. Current Psychology, 42(26), 22137-22147.
Li, Y. L. (2025). The impact of positive attitude towards artificial intelligence and multiple mediation functions of satisfying basic psychological needs. Scientific Reports, 15(1), 44737. https://doi.org/10.1038/s41598-025-28529-x
Li, Y., Liao, T.-H., Tsai, C.-H., & Wu, T.-J. (2025). From Synergy to Strain: Exploring the Psychological Mechanisms Linking Employee–AI Collaboration and Knowledge Hiding. Behavioral Sciences, 16(1), 13. https://doi.org/10.3390/bs16010013
Li, X., Wong, W., Lamoureux, E. L., & Wong, T. Y. (2012). Are linear regression techniques appropriate for analysis when the dependent (outcome) variable is not normally distributed? Investigative ophthalmology & visual science, 53(6), 3082-3083.
Lizarte Simón, E. J., Gijón Puerta, J., Galván Malagón, M. C., & Khaled Gijón, M. (2024). Influence of self-efficacy, anxiety and psychological well-being on academic engagement during university education. Education Sciences, 14(12), 1367.
Liang, H., & Reiß, M. (2025). The associations between students’ attitudes toward AI and learning engagement: serial mediating roles of perceived autonomy and learning enjoyment. Frontiers in Psychology, 16, 1681635. https://doi.org/10.3389/fpsyg.2025.1681635
Lin, T., & Zhu, X. (2024). Academic self-efficacy, grit, and teacher support as predictors of psychological well-being of Chinese EFL students. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1332909
Liu, J. (2025). When Usefulness Fuels Fear: The Paradox of Generative AI Dependence and the Mitigating Role of AI Literacy. International Journal of Human-Computer Interaction, 1. https://doi.org/10.1080/10447318.2025.2544006
Liu, S., Zaigham, G. H. K., Rashid, R. M., & Bilal, A. (2022). Social media-based collaborative learning effects on student performance/learner performance with moderating role of academic self-efficacy. Frontiers in psychology, 13, 903919.
Low, M. P., Wut, T. M., & Pok, W. F. (2025). Artificial intelligence facilitators in higher education institutions: A student-centric exploration with comparative analysis in Asian countries. Education and Information Technologies, 30(13), 18485. https://doi.org/10.1007/s10639-025-13513-9
Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets. Annual review of public health, 23(1), 151-169.
Markowitz, D. M., Boyd, R. L., & Blackburn, K. (2024). From silicon to solutions: AI’s impending impact on research and discovery. Frontiers in Social Psychology, 2. https://doi.org/10.3389/frsps.2024.1392128
M. F. Shahzad, S. Xu, and I. Javed, “ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone,” Int. J. Educ. Technol. High. Educ., vol. 21, no. 1, p. 46, Jul. 2024.
Mobarak, N. A., Amin, N. A., Abdel-Mohssen, A., A.Sharif, A., Halim, Y. T., & Brickman, K. (2025). Second glance: exploring consumer shifts to thrift shopping and perception of second-hand fashion. Future Business Journal, 11(1). https://doi.org/10.1186/s43093-025-00481-2
Muhammad, A., Ardi, N. A. H., Nitswat, N., Nur, S. D. A., Razak, A. A., Amoretti, E. C., Aquil, A., & Pivetta, D. V. C. (2025). Impact of Artificial Intelligence on Academic Literacy among University Students in Indonesia: A Case Study. Berkala Ilmiah Pendidikan, 5(1), 25. https://doi.org/10.51214/bip.v5i1.1393
Neyazi, T. A., Ee, T. K., Nadaf, A. H., & Schroeder, R. (2023). The effect of information seeking behaviour on trust in AI in Asia: The moderating role of misinformation concern. New Media & Society. https://doi.org/10.1177/14614448231212804
Peng, X., Sun, K., Shan, X., & Zhang, J. (2026). Empowerment or dependency? A systematic review of the impacts of intelligent assessment and generative AI on learners’ self-beliefs and cognitive agency in music education. Frontiers in Psychology, 17. https://doi.org/10.3389/fpsyg.2026.1776445
Raff, S., Rose, S., & Huynh, T. (2024). Perceived creepiness in response to smart home assistants: A multi-method study. International Journal of Information Management, 74, 102720.
Ren, J., Guo, J., & Li, H. (2025). Linking digital competence, self-efficacy, and digital stress to perceived interactivity in AI-supported learning contexts. Scientific Reports, 15(1), 33182. https://doi.org/10.1038/s41598-025-18873-3
Riley, C., Alrefai, O., Reyes, Y. C., & Hammad, E. (2025). Human-AI Interactions: Cognitive, Behavioral, and Emotional Impacts. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2510.17753
Rodríguez-Ruiz, J., Marín-López, I., & Espejo-Siles, R. (2025). Is artificial intelligence use related to self-control, self-esteem and self-efficacy among university students?. Education and Information Technologies, 30(2), 2507-2524.
Salah, M., Alhalbusi, H., Ismail, M. M., & Abdelfattah, F. (2024). Chatting with ChatGPT: decoding the mind of Chatbot users and unveiling the intricate connections between user perception, trust and stereotype perception on self-esteem and psychological well-being. Current Psychology, 43(9), 7843-7858.
Sanci, L., Williams, I., Russell, M., Chondros, P., Duncan, A. M., Tarzia, L., ... & Minas, H. (2022). Towards a health promoting university: descriptive findings on health, wellbeing and academic performance amongst university students in Australia. BMC public health, 22(1), 2430.
Seberger, J. S., Choung, H., Snyder, J., & David, P. (2024). Better living through creepy technology? Exploring tensions between a novel class of well-being apps and affective discomfort in app culture. Proceedings of the ACM on human-computer interaction, 8(CSCW1), 1-39.
Shi, Y., Cui, H., Zhang, Y., Hui, X., Li, G., Ouyang, M., & Pan, L. (2026). The impact of artificial intelligence perception on university students' academic engagement: the mediating role of academic motivation and the moderating role of academic self-efficacy. Frontiers in Psychology, 17, 1735410.
Shubina, I., & Dzido, A. J. (2025). Artificial Intelligence and Mental Health: Bibliometric Analysis. International Journal of Interactive Mobile Technologies (iJIM), 19(19), 4. https://doi.org/10.3991/ijim.v19i19.58041
Soaloon, S. K., & Handayani, P. W. (2025). The influence of continuance intention on users’ well-being and intention to recommend online music streaming applications. Cogent Arts and Humanities, 12(1). https://doi.org/10.1080/23311983.2025.2480445
Soto-Sanfiel, M. T., Angulo-Brunet, A., & Lutz, C. (2025). The scale of artificial intelligence literacy for all (SAIL4ALL): assessing knowledge of artificial intelligence in all adult populations. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-05978-3
Tennant, R., Hiller, L., Fishwick, R., Platt, S., Joseph, S., Weich, S., ... & Stewart-Brown, S. (2007). The Warwick-Edinburgh mental well-being scale (WEMWBS): development and UK validation. Health and Quality of life Outcomes, 5(1), 63.
Yan, L., Greiff, S., Lodge, J. M., & Gašević, D. (2025). Distinguishing performance gains from learning when using generative AI. Nature Reviews Psychology, 4(7), 435. https://doi.org/10.1038/s44159-025-00467-5
Youssef, E., Medhat, M., Abdellatif, S., & Al Malek, M. (2024). Examining the effect of ChatGPT usage on students’ academic learning and achievement: A survey-based study in Ajman, UAE. Computers and Education: Artificial Intelligence, 7, 100316.
Wang, B., Liu, S., & Luo, C. (2026). How does human-AI collaboration task complexity affect employee work engagement? The roles of humble leadership and AI self-efficacy. Frontiers in Psychology, 17. https://doi.org/10.3389/fpsyg.2026.1767967
Wang, X., & Lin, L. (2025). The innovation paradox in human-AI symbiosis: ambidextrous effects of AI technology adoption on innovative behavior. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1635246
Wang, Z., Chen, Z., Li, L., & Sun, J. (2025). Between support and substitution: the impact of artificial intelligence use on anxiety and learning performance among Spanish majors in China. Frontiers in Psychology, 16, 1710445. https://doi.org/10.3389/fpsyg.2025.1710445
Wozniak, P. W., Karolus, J., Lang, F., Eckerth, C., Schöning, J., Rogers, Y., & Niess, J. (2021, May). Creepy technology: what is it and how do you measure it?. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-13).
Xiao-hua, L., 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(1). https://doi.org/10.1186/s41239-025-00567-5
Yang, J., Shi, G., Zhu, W., & Sun, Y. (2025). Intelligent technologies in smart education: a comprehensive review of transformative pillars and their impact on teaching and learning methods. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-05444-0
Yang, Y., Yang, S., Wu, K., Zhang, R., & Wang, C. (2025). Adoption of Generative Artificial Intelligence: The Roles of Perceived Usefulness, Self-Efficacy, and Workload. TEM Journal, 2926. https://doi.org/10.18421/tem144-03
Yang, Y.-Y., & Delgado, M. R. (2025). The integration of self-efficacy and response-efficacy in decision making. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-85577-z
Yao, R., Qi, G., Sheng, D., Sun, H., & Zhang, J. (2025). Connecting self-esteem to problematic AI chatbot use: the multiple mediating roles of positive and negative psychological states. Frontiers in Psychology, 16, 1453072. https://doi.org/10.3389/fpsyg.2025.1453072
Yuan, S., Li, F., Browning, M. H. E. M., Bardhan, M., Zhang, K., McAnirlin, O., Patwary, M. M., & Reuben, A. (2024). Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1295275
Zhang, M. (2025). Optimizing academic engagement and mental health through AI: an experimental study on LLM integration in higher education. Frontiers in Psychology, 16, 1641212. https://doi.org/10.3389/fpsyg.2025.1641212
Zhang, T., & Chen, X. (2025). The mediating role of learning self-efficacy in the relationship between subjective well-being and academic performance in children. Frontiers in Psychology, 16, 1570068. https://doi.org/10.3389/fpsyg.2025.1570068
Zhou, J., & Yu, H. (2021). Contribution of social support to home-quarantined Chinese college students’ well-being during the COVID-19 pandemic: the mediating role of online learning self-efficacy and moderating role of anxiety. Social Psychology of Education, 24(6), 1643-1662.