AI-Enhanced Problem-Based Learning: Differential Effects of AI Usage Patterns on Core Competency Development Through Psychological Mechanisms
DOI:
https://doi.org/10.46328/ijtes.5725Keywords:
artificial intelligence, problem-based learning, self-determination theory, psychological mechanisms, core competenciesAbstract
This study investigates how different levels of AI usage influence PBL core competencies through psychological mechanisms, establishing a theoretical framework that integrates Self-Determination Theory, Social Cognitive Theory, and Cognitive Load Theory. A cross-sectional survey design examined 412 university students across diverse academic disciplines. Participants completed validated instruments measuring AI usage patterns (assistive, dependent, substitutive), basic psychological need satisfaction, intrinsic motivation, self-efficacy, and five PBL core competencies (collaborative learning, autonomous learning, critical thinking, knowledge integration, communication skills). Structural equation modeling tested direct and mediated relationships. AI usage patterns demonstrated significant differential effects on PBL competencies. Assistive AI usage positively influenced all five core competencies, while substitutive AI usage negatively affected critical thinking abilities. Psychological mechanisms accounted for 64-73% of total effects through a sequential mediation pathway: basic need satisfaction → intrinsic motivation → self-efficacy → competency development. Competence need satisfaction emerged as the most critical mediator. The findings reveal that AI's educational impact depends critically on usage patterns and psychological mechanisms rather than technology presence alone. The study establishes theoretical foundations for AI-PBL implementation that prioritizes learner agency while leveraging technological affordances.
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.
Anggraeni, D. M., Prahani, B. K., Suprapto, N., Shofiyah, N., & Jatmiko, B. (2023). Systematic review of problem based learning research in fostering critical thinking skills. Thinking Skills and Creativity, 49, 101334.
Autin, K. L., Herdt, M. E., Garcia, R. G., & Ezema, G. N. (2022). Basic psychological need satisfaction, autonomous motivation, and meaningful work: A self-determination theory perspective. Journal of Career Assessment, 30(1), 78-93.
Bauer, E., Greiff, S., Graesser, A. C., Scheiter, K., & Sailer, M. (2025). Looking beyond the hype: Understanding the effects of AI on learning. Educational Psychology Review, 37(2), 45.
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., . . . Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4.
Chen, C.-C., & Tu, H.-Y. (2021). The effect of digital game-based learning on learning motivation and performance under social cognitive theory and entrepreneurial thinking. Frontiers in psychology, 12, 750711.
Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118.
Fan, J., & Zhang, Q. (2024). From literacy to learning: The sequential mediation of attitudes and enjoyment in AI-assisted EFL education. Heliyon, 10(17).
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sciences, 15(2), 203.
Guay, F. (2022). Applying self-determination theory to education: Regulations types, psychological needs, and autonomy supporting behaviors. Canadian Journal of School Psychology, 37(1), 75-92.
Hidayat-ur-Rehman, I. (2024). Examining AI competence, chatbot use and perceived autonomy as drivers of students' engagement in informal digital learning. Journal of Research in Innovative Teaching & Learning, 17(2), 196-212.
Jia, L., Jalaludin, N. A., & Rasul, M. S. (2023). Design thinking and project-based learning (DT-PBL): A review of the literature. International Journal of Learning, Teaching and Educational Research, 22(8), 376-390.
Konradt, L., Boote, D. N., & Taub, M. N. (2025). Making AI Annoying on Purpose: When Helpful Tools Don't Always Help. Available at SSRN 5477706.
Krulj, J. R., Marković, E., Simijonović, I., & Lazović, N. R. (2024). Intrinsic and extrinsic motivation within the context of creating a stimulating learning environment. Društvene i humanističke studije, 9(2 (26)), 1329-1344.
Li, J., King, R. B., Chai, C. S., Zhai, X., & Lee, V. W. (2025). The AI Motivation Scale (AIMS): a self-determination theory perspective. Journal of Research on Technology in Education, 1-22.
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, 1285392.
Liu, W. C. (2021). Implicit theories of intelligence and achievement goals: A look at students’ intrinsic motivation and achievement in mathematics. Frontiers in psychology, 12, 593715.
Lu, Y.-C. A., Lee, S.-H., Hsu, M.-Y., Shih, F.-F., Yen, W.-J., Huang, C.-Y., . . . Kuo, C.-P. (2022). Effects of problem-based learning strategies on undergraduate nursing students’ self-evaluation of their core competencies: a longitudinal cohort study. International Journal of Environmental Research and Public Health, 19(23), 15825.
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Paper presented at the Frontiers in Education.
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082-1104.
Patac, L. P., & Patac Jr, A. V. (2025). Using ChatGPT for academic support: Managing cognitive load and enhancing learning efficiency–A phenomenological approach. Social Sciences & Humanities Open, 11, 101301.
Ryan, R. M., Deci, E. L., Vansteenkiste, M., & Soenens, B. (2021). Building a science of motivated persons: Self-determination theory’s empirical approach to human experience and the regulation of behavior. Motivation science, 7(2), 97.
Schwarzer, R., & Jerusalem, M. (1995). Optimistic self-beliefs as a resource factor in coping with stress. In Extreme stress and communities: Impact and intervention (pp. 159-177): Springer.
Su, T., Liu, J., Meng, L., Luo, Y., Ke, Q., & Xie, L. (2025). The effectiveness of problem-based learning (PBL) in enhancing critical thinking skills in medical education: a systematic review and meta-analysis. Paper presented at the Frontiers in Education.
Viorennita, A., Dewi, L., & Riyana, C. (2023). The role of ChatGPT AI in student learning experience. Indonesian Journal of Multidiciplinary Research, 3(2), 445-452.
Wang, Y.-M., Wei, C.-L., Lin, H.-H., Wang, S.-C., & Wang, Y.-S. (2024). What drives students’ AI learning behavior: A perspective of AI anxiety. Interactive learning environments, 32(6), 2584-2600.
Wang, Y.-Y., & Chuang, Y.-W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29(4), 4785-4808.
Yang, Y., Chen, J., & Zhuang, X. (2025). Self-determination theory and the influence of social support, self-regulated learning, and flow experience on student learning engagement in self-directed e-learning. Frontiers in psychology, 16, 1545980.
Zdravkova, K. (2022). The potential of artificial intelligence for assistive technology in education. In Handbook on intelligent techniques in the educational process: vol 1 recent advances and case studies (pp. 61-85): Springer.
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.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Technology in Education and Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
