Influence of Self-Efficacy in the Use of Artificial Intelligence (AI) and Anxiety Toward AI Use on AI Dependence Among Peruvian University Students
DOI:
https://doi.org/10.56294/dm2025210Keywords:
Self-efficacy, technological anxiety, artificial intelligence, dependence, higher educationAbstract
Background: The advancement of artificial intelligence (AI) in education has transformed the way students interact with technological tools, creating new challenges related to self-efficacy, anxiety, and AI dependence. Self-efficacy refers to one's confidence in their ability to use AI, while AI-related anxiety pertains to the fear or concern when interacting with these systems. These variables can influence technological dependence, affecting academic performance and emotional well-being. Objective: This study aims to examine the influence of self-efficacy in AI use and anxiety toward AI on AI dependence among Peruvian university students. Methods: A descriptive cross-sectional study was conducted with 528 Peruvian university students aged 18 to 37 years (M = 19.00, SD = 3.84). Scales were used to measure AI self-efficacy, anxiety toward AI, and AI dependence. Correlation and multiple regression analyses were applied to identify predictors of technological dependence. Results: The results showed that AI self-efficacy was positively correlated with AI anxiety (r = 0.43, p < .01) and AI dependence (r = 0.61, p < .01). Anxiety also significantly correlated with AI dependence (r = 0.71, p < .01). Multiple regression analysis revealed that both AI anxiety (β = 1.131, p < .001) and AI self-efficacy (β = 0.610, p < .001) predicted AI dependence. Additionally, business administration students exhibited greater dependence compared to students from other fields (β = 1.025, p < .05). Conclusions: Students with higher self-efficacy in AI use tend to utilize AI more frequently but also experience greater anxiety and dependence on AI. Educational interventions should focus on reducing AI-related anxiety to prevent excessive dependence, especially among students.
References
Alania-Contreras, R. D., Ruiz-Aquino, M., Alvarez-Risco, A., Condori-Apaza, M., Chanca-Flores, A., Fabián-Arias, E., Rafaele-de-la-Cruz, M., Del-Aguila-Arcentales, S., Davies, N. M., Ortiz de Agui, M. L., & Yáñez, J. A. (2024). Evolving attitudes toward online education in Peruvian university students: A quantitative approach. Heliyon, 10(9), e30566. https://doi.org/10.1016/j.heliyon.2024.e30566
Anderson, J., & Smith, A. (2014). AI, Robotics, and the Future of Jobs. Technology Review, 16(4).
Davenport, T., & Ronanki, R. (2023). Artificial intelligence for the real world. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/IRJMETS42512
Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.
Brown, J. S., & Adler, R. P. (2008). Minds on Fire: Open Education, the Long Tail, and Learning 2.0. Educause Review, 43(1).
Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1), 60. https://doi.org/10.1186/s40561-023-00269-3
Chang, P. C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-Driven Technostress Promote or Hinder Employees’ Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy. Psychology Research and Behavior Management, 17. https://doi.org/10.2147/PRBM.S441444
Chu, C. H., Nyrup, R., Leslie, K., Shi, J., Bianchi, A., Lyn, A., McNicholl, M., Khan, S., Rahimi, S., & Grenier, A. (2022). Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults. In Gerontologist (Vol. 62, Issue 7). https://doi.org/10.1093/geront/gnab167
Czaja, S. J. (2005). The impact of aging on access to technology. ACM SIGACCESS Accessibility and Computing, 83. https://doi.org/10.1145/1102187.1102189
Dávila Cisneros, J. D., Flores Limo, F. A., Barrios Tinoco, L. M., Cavero Aybar, H. N., Medina Romero, M. Á., Romero Flores, R. A., & Calizaya Alarcon, V. G. (2023). ADJUSTMENT OF PERUVIAN UNIVERSITY STUDENTS TO ARTIFICIAL INTELLIGENCE. Artseduca, 36. https://doi.org/10.6035/artseduca.3615
Erdfelder, E., FAul, F., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods 2009 41:4, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Flores-Cueto, J. J., Garay-Argandoña, R., Hernández, R. M., Saavedra-López, M. A., & Ponce-Meza, J. C. (2023). DIGITAL DIVIDE IN PERUVIAN HIGHER EDUCATION: A POST-PANDEMIC REVIEW. Relacoes Internacionais No Mundo Atual, 2(40). https://doi.org/10.21902/Revrima.v6i40.6347
Grassini, S. (2023). Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692
Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4). https://doi.org/10.1016/j.ijis.2020.09.001
Hasani, T., Rezania, D., & Mohammadi, M. (2024). Towards a framework for successful metaverse adoption in Small and Medium-sized Enterprises: An exploratory study. International Journal of Engineering Business Management, 16. https://doi.org/10.1177/18479790241257118
Hoerger, M., & Currell, C. (2011). Ethical issues in Internet research. In S. Knapp, M. Gottlieb, M. Handelsman, & L. VandeCreek (Eds.), APA handbook of ethics in psychology, Vol 2: Practice, teaching, and research. (Vol. 2, pp. 385–400). American Psychological Association. https://doi.org/10.1037/13272-018
Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X., & Wang, Y. (2024). AI Technology panic—is AI Dependence Bad for Mental Health? A Cross-Lagged Panel Model and the Mediating Roles of Motivations for AI Use Among Adolescents. Psychology Research and Behavior Management, 17. https://doi.org/10.2147/PRBM.S440889
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4). https://doi.org/10.1016/j.bushor.2018.03.007
Bancoro, J. C. M. (2024). The Relationship Between Artificial Intelligence (AI) Usage and Academic Performance of Business Administration Students. International Journal of Asian Business and Management, 3(1). https://doi.org/10.55927/ijabm.v3i1.7876
Jia, X. H., & Tu, J. C. (2024). Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness. Systems, 12(3). https://doi.org/10.3390/systems12030074
Jia, Z., Chen, J., Xu, X., Kheir, J., Hu, J., Xiao, H., Peng, S., Hu, X. S., Chen, D., & Shi, Y. (2023). The importance of resource awareness in artificial intelligence for healthcare. Nature Machine Intelligence, 5(7). https://doi.org/10.1038/s42256-023-00670-0
Kim, J., Kadkol, S., Solomon, I., Yeh, H., Soh, J. Y., Nguyen, T. M., Choi, J. Y., Lee, S., Srivatsa, A. V., Nahass, G. R., & Ajilore, O. A. (2023). AI Anxiety: A Comprehensive Analysis of Psychological Factors and Interventions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4573394
Kimmerle, J., Timm, J., Festl-Wietek, T., Cress, U., & Herrmann-Werner, A. (2023). Medical Students’ Attitudes Toward AI in Medicine and their Expectations for Medical Education. Journal of Medical Education and Curricular Development, 10. https://doi.org/10.1177/23821205231219346
Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63. https://doi.org/10.1016/j.techsoc.2020.101410
Liang, J., Wang, L., Luo, J., Yan, Y., & Fan, C. (2023). The relationship between student interaction with generative artificial intelligence and learning achievement: serial mediating roles of self-efficacy and cognitive engagement. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1285392
Marsh, E., Vallejos, E. P., & Spence, A. (2022). The digital workplace and its dark side: An integrative review. In Computers in Human Behavior (Vol. 128). https://doi.org/10.1016/j.chb.2021.107118
McAfee, A., & Brynjolfsson, E. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W.W. Norton & Company.
Mercader-Rubio, I., Gutiérrez Ángel, N., Silva, S., Moisão, A., & Brito-Costa, S. (2023). Relationships between somatic anxiety, cognitive anxiety, self-efficacy, and emotional intelligence levels in university physical education students. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1059432
Mitzner, T. L., Boron, J. B., Fausset, C. B., Adams, A. E., Charness, N., Czaja, S. J., Dijkstra, K., Fisk, A. D., Rogers, W. A., & Sharit, J. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26(6). https://doi.org/10.1016/j.chb.2010.06.020
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024a). Adaptation and psychometric properties of a brief version of the general self-efficacy scale for use with artificial intelligence (GSE-6AI) among university students. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1293437
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024b). Adaptation and Psychometric Properties of an Attitude toward Artificial Intelligence Scale (AIAS-4) among Peruvian Nurses. Behavioral Sciences, 14(6), 437. https://doi.org/10.3390/bs14060437
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024c). Development and validation of a scale for dependence on artificial intelligence in university students. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1323898
Morales-Rodríguez, F. M., & Pérez-Mármol, J. M. (2019). The Role of Anxiety, Coping Strategies, and Emotional Intelligence on General Perceived Self-Efficacy in University Students. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.01689
Novita, R., & Sulastri, S. (2023). The influence of beliefs and anxiety in using artificial intelligence towards readiness to change with high performance work systems as a moderating variable. Human Resource Management Studies, 3(4). https://doi.org/10.24036/hrms.v3i4.308
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464). https://doi.org/10.1126/science.aax2342
Parsakia, K. (2023). The Effect of Chatbots and AI on The Self-Efficacy, Self-Esteem, Problem-Solving and Critical Thinking of Students. Health Nexus, 1(1). https://doi.org/10.61838/hn.1.1.14
Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., Tyskbo, D., & Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research, 22(1). https://doi.org/10.1186/s12913-022-08215-8
Rodrigues, M., Silva, R., Borges, A. P., Franco, M., & Oliveira, C. (2024). Artificial intelligence: threat or asset to academic integrity? A bibliometric analysis. In Kybernetes. https://doi.org/10.1108/K-09-2023-1666
Rodríguez-Ruiz, J., Marín-López, I., & Espejo-Siles, R. (2024). Is artificial intelligence use 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
Sanchez-Franco, M. J. (2009). The Moderating Effects of Involvement on the Relationships Between Satisfaction, Trust and Commitment in e-Banking. Journal of Interactive Marketing, 23(3). https://doi.org/10.1016/j.intmar.2009.04.007
Thomas, B. (2021). Artificial Intelligence: Review of Current and Future Applications in Medicine. Federal Practitioner, 38 (11). https://doi.org/10.12788/fp.0174
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3). https://doi.org/10.2307/30036540
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: Management Information Systems, 36(1). https://doi.org/10.2307/41410412
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(1), 34. https://doi.org/10.1186/s41239-024-00467-0
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Copyright (c) 2025 Wilter C. Morales-García, Liset Z. Sairitupa-Sanchez, Alcides Flores-Paredes, Jai Pascual-Mariño, Mardel Morales-García (Author)
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