AI-Driven LMS: Enhancing Mathematics Education through Generative AI
DOI:
https://doi.org/10.59175/pijed.v4i2.727Keywords:
Artificial Intelligence in Education, Learning Management System, Mathematics Education, Self-Regulated LearningAbstract
This research project is expected to develop and test an AI-based Learning Management System (LMS) contents which can promote Self-regulated learning (SRL) with secondary school mathematics students. Considering that conventional LMS have shortcomings in relation to adaptive learning, and given the increasing significance of SRL, this study bridges that gap by proposing new AI-based LMS for mathematics at high school level. Utilizing a Research and Development model combined with an embedding quasi-experiment design of 94 students, the project developed AI-enhanced LMS-based content that included; personalization, adaptive difficulty, AI tutor intervention, and asynchronous administration. The findings reveal that the AI-based LMS content was effective on math students N-Gain scores compared to traditional instruction learning but with small effect sizes. In addition, students employed the AI LMS at moderate-high SRL levels in most of its components (i.e., goal setting, self-evaluation and help seeking). What is new in this work is the development per se (using AI) of LMS content for mathematics to help support SRL, while also taking into account multi-device and user-friendly considerations. Operationally, this AI-LMS system provides a student-centric and flexible learning environment that can help students develop self-regulation of learning to promote engagement and achievement in mathematics. The unique contribution of this study is a validated AI-oriented LMS model that closes the distance between AI promise and SRL requirements in secondary mathematics education.
References
Bayly-Castaneda, K., Ramirez-Montoya, M. S., & Morita-Alexander, A. (2024). Crafting personalized learning paths with AI for lifelong learning: a systematic literature review. Frontiers in Education, 9(August), 1–12. https://doi.org/10.3389/feduc.2024.1424386
Bruner, J. S. (1966). Toward a Theory of Instruction. The Belknap Press of Harvard University Press.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
Correia, A., Agua, P., & Lobo, V. (2024). Adaptive Learning Design: Integrating AI to Personalize Critical Thinking Education. EDULEARN24 Proceedings, 1(May), 7733–7741. https://doi.org/10.21125/edulearn.2024.1816
Dijkstra, J., Galbraith, R., Hodges, B. D., McAvoy, P. A., McCrorie, P., Southgate, L. J., Van Der Vleuten, C. P., Wass, V., & Schuwirth, L. W. (2012). Expert validation of fit-for-purpose guidelines for designing programmes of assessment. BMC Medical Education, 12(1). https://doi.org/10.1186/1472-6920-12-20
Dong, N., & Chen, Z. (2020). The Fourth Education Revolution: Will Artificial Intelligence Liberate or Infantilise Humanity. Higher Education, 80(4), 797–799. https://doi.org/10.1007/s10734-020-00506-5
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/https://doi.org/10.1108/EBR-11-2018-0203
Hatta Setiabudhi, S.E, M. A., Suwono, S.E, M. S., Yudi Agus Setiawan, S.S, M. ., & Syahrul Karim, M. S. (2024). Quantitative with smart pls. In Ebooks.Borneonovelty.Com. Borneo Novelty Publishing. https://ebooks.borneonovelty.com/media/publications/588838-analisis-data-kuantitatif-dengan-smartpl-29069ce4.pdf?
Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2023). Supporting self-regulated learning with learning analytics interventions – a systematic literature review. Education and Information Technologies, 28(3), 3059–3088. https://doi.org/10.1007/s10639-022-11281-4
Henseler, J., Hubona, G., & Ray, P. A. (2015). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management and Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education. The Center for Curriculum Redesign.
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: a systematic review. Educational Technology Research and Development, 68(4), 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
Jesionkowska, J., Wild, F., & Deval, Y. (2020). Active Learning Augmented Reality for Steam Education—a Case Study. Education Sciences, 10(8), 1–15. https://doi.org/10.3390/educsci10080198
Jin, S. H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00406-5
Joe Khan Omar Jian, M. (2023). Personalized learning through AI. Advances in Engineering Innovation, 5(1). https://doi.org/10.54254/2977-3903/5/2023039
Johnson, H. L., Tzur, R., Gardner, A., Hodkowski, N. M., Lewis, A., & McClintock, E. (2022). A new angle: a teacher’s transformation of mathematics teaching practice and engagement in quantitative reasoning. Research in Mathematics Education, 24(1), 88–108. https://doi.org/10.1080/14794802.2021.1988688
Kemendikbud. (2021). Guide to Implementing the Independent Campus Policy (MBKM). Direktorat Pendidikan Tinggi Vokasi dan Profesi. https://lldikti13.kemdikbud.go.id/wp-content/uploads/2022/07/Panduan-Implementasi-Kebijakan-Merdeka-Belajar-Kampus-Merdeka-MBKM.pdf
Laohajaratsang, T. (2018). Designing Effective Pedagogical Approaches with Next-Generation LMS for Students in Higher Education Thanomporn Laohajaratsang Information Technology Service Center and Faculty of Education. THAITESOL Journal, 31(1), 1–20.
Lefortier, D., Swaminathan, A., Gu, X., Joachims, T., & de Rijke, M. (2016). Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. 1–10. http://arxiv.org/abs/1612.00367
Merino-Campos, C. (2025). The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review. Trends in Higher Education, 4(2), 17. https://doi.org/10.3390/higheredu4020017
Murray, M., Pérez, J., Geist, D., & Hedrick, A. (2013). Student Interaction with Content in Online and Hybrid Courses. Informing Science, 16(1), 99–115. https://doi.org/10.28945/1779
Nindiasari, H., Pranata, M. F., Sukirwan, Sugiman, Fathurrohman, M., Ruhimat, A., & Yuhana, Y. (2024). the Use of Augmented Reality To Improve Students’ Geometry Concept Problem-Solving Skills Through the Steam Approach. Infinity Journal, 13(1), 119–138. https://doi.org/10.22460/infinity.v13i1.p119-138
Nuangchalerm, P. (2023). AI-Driven Learning Analytics in STEM Education. International Journal of Research in STEM Education, 5(2), 77–84. https://doi.org/10.33830/ijrse.v5i2.1596
Pellas, N. (2023). The Effects of Generative AI Platforms on Undergraduates’ Narrative Intelligence and Writing Self-Efficacy. Education Sciences, 13(11). https://doi.org/10.3390/educsci13111155
Pintrich, P. R., Zeidner, M., & Boekaerts, M. (2000). The Role of Goal Orientation in Self-Regulated Learning. In Handbook of Self-Regulation (pp. 451–502). Academic Press. http://www.sciencedirect.com/science/article/pii/B9780121098902500433
Prajapati, A. kumar B. bhai. (2024). Artificially Intelligent in Education: “Redefining Learning in the 21st Century.” Educational Resurgence Journal, 7(2). https://coed.dypvp.edu.in/educational-resurgence-journal/
Pressman, R. S., & R.Maxim, B. (2015). Software Engineering A Practitioner’s Approach (Eighth Edi). Mc Graw Hill Education. www.mhhe.com
Schunk, D. H., & Greene, J. A. (2017). Handbook of Self-Regulation of Learning and Performance, Second Edition (Second Edi). Routledge -Taylor & Francis. https://doi.org/10.4324/9781315697048
Stacey, K., & MacGregor, M. (2020). Ideas about Symbolism That Students Bring to Algebra. The Mathematics Teacher, 90(2), 110–113. https://doi.org/10.5951/mt.90.2.0110
Sweller, J. (1988). Cognitive Load during Problem Solving: Effects on Learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7
Tandililing, E. (2011). The Enhancement of Mathematical Communication and Self Regulated Learning of Senior High School Students Through PQ4R Strategy Accompanied by Refutation Text Reading. Building the Nation Character Through Humanistic on Mathematic Education, 918–928.
Thiagarajan, S., S.Semmel, D., & Semmel, M. I. (1974). Instructional development for training teachers of exceptional children: A sourcebook (Vol. 14, Issue 1). Indiana University Bloomington, Indiana.
Vygotsky, L. S. (1978). Mind of Society. Harvard University Press.
Wang, K., Cui, W., & Yuan, X. (2025). Artificial Intelligence in Higher Education: The Impact of Need Satisfaction on Artificial Intelligence Literacy Mediated by Self-Regulated Learning Strategies. Behavioral Sciences, 15(2), 1–24. https://doi.org/10.3390/bs15020165
Winne, P. H., & Hadwin, A. F. (2021). Studying as Self-Regulated Learning. Metacognition in Educational Theory and Practice, January 1998, 291–318. https://doi.org/10.4324/9781410602350-19
Yasar, O., & Adiguzel, T. (2010). A working successor of learning management systems: SLOODLE. Procedia - Social and Behavioral Sciences, 2(2), 5682–5685. https://doi.org/10.1016/j.sbspro.2010.03.928
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
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Agus Arifin, Heni Pujiastuti, Hepsi Nindiasari
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
