The Role of Self Regulated Learning in Mediating the Influence of Generative AI Usage Intensity on Students' Learning Independence in Mathematics Learning
Keywords:
Generative AI, Self-Regulated Learning, Learning Independence, Mathematics Learning.
Abstract
This study aims to analyze the influence of generative Artificial Intelligence (AI) usage intensity on students’ learning independence through the mediating role of Self Regulated Learning (SRL) in mathematics learning. This research employed a quantitative approach with an ex post facto design involving 157 students of MAN 2 Sinjai selected through total sampling. Data were collected using questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results indicate that generative AI usage intensity has a positive and significant effect on Self Regulated Learning (β = 0.865, p < 0.001), while Self Regulated Learning significantly influences students’ learning independence (β = 0.912, p < 0.001). Furthermore, Self Regulated Learning was found to significantly mediate the relationship between generative AI usage intensity and learning independence. These findings suggest that the effectiveness of generative AI in promoting learning independence depends on students’ ability to regulate their learning processes. Therefore, the integration of generative AI in mathematics education should be accompanied by strategies that strengthen students’ self-regulation skills to maximize learning outcomes.
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Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50 (2), 248–287. https://doi.org/10.1016/0749-5978(91)90022-L
Boekaerts, M., Pintrich, P. R., & Zeidner, M. (2000). Handbook of self-regulation . Academic Press.
Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A.E., & Rodríguez, M.E. (2021). Artificial intelligence and reflections from the educational landscape: A review of AI studies in half a century. Sustainability, 13 (2), 800. https://doi.org/10.3390/su13020800
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
Dwivedi, YK, et.al (2023). 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.ijinfomgt.2023.102642
Hair, J.F., Hult, G.T.M., Ringle, C.M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed .). Sage Publications.
Hidayat, W., & Asyafah, A. (2023). Student learning independence in the digital era. Indonesian Journal of Education, 12 (2), 145–156.
Khalil, M., & Er, E. (2024). Opportunities and challenges from the perspective of higher education. Smart Learning Environments, 11 (1), 1–18.
Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers . Cambridge Adult Education.
Kurniawati, D., & Nita, S. (2022). Self Regulated Learning and Student Learning Independence in the Digital Era. Journal of Education and Learning, 29 (1), 45–56.
Marzuki, M., & Wahyuni, S. (2024). Utilization of artificial intelligence in 21st century mathematics learning. Journal of Mathematics Education, 18 (1), 33–47.
Mulyono, H., & Anwar, K. (2023). Artificial intelligence and students' learning autonomy: A systematic review. Journal of Educational Technology Studies, 15 (3), 201–215.
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31 (2), 199–218. https://doi.org/10.1080/03075070600572090
Panadero, E. (2017). A review of Self Regulated Learning : Six models and four directions for research. Frontiers in Psychology, 8 , 422. https://doi.org/10.3389/fpsyg.2017.00422
Pintrich, P. R. (2000). The role of goal orientation in Self Regulated Learning . In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press.
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and Self Regulated Learning in college students. Educational Psychology Review, 16 (4), 385–407. https://doi.org/10.1007/s10648-004-0006-x
Schunk, DH (2012). Learning theories: An educational perspective (6th ed .). Pearson Education.
Schunk, D.H., & Greene, J.A. (2018). Handbook of self-regulation of learning and performance (2nd ed .). Routledge.
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2 (1), 3–10.
Sugiyono. (2022). Quantitative, qualitative, and R&D research methods (Revised edition). Alphabet.
UNESCO. (2021). Reimagining our futures together: A new social contract for education . UNESCO Publishing.
UNESCO. (2023). Guidance for generative AI in education and research . UNESCO Publishing.
Utami, R., & Rahman, A. (2023). The relationship between Self Regulated Learning and learning independence of high school students. Journal of Educational Psychology, 9 (2), 88–99.
Wong, J., & Yang, M. (2024). Generative artificial intelligence and student engagement in learning environments. Computers & Education: Artificial Intelligence, 6 , 100230.
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81 (3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41 (2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45 (1), 166–183. https://doi.org/10.3102/0002831207312909
Published
2026-07-06
How to Cite
Asri, S. A., Syahrul, & Suryadi Ishak. (2026). The Role of Self Regulated Learning in Mediating the Influence of Generative AI Usage Intensity on Students’ Learning Independence in Mathematics Learning. International Journal of Education, Vocational and Social Science , 5(03), 3921-3935. https://doi.org/10.63922/ijevss.v5i03.4712
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Email : ijevss@citakonsultindo.or.id / editor.ijevss@gmail.com
