Utilizing AI to Personalize Mathematics Learning

Nur Choiro Siregar* -  Universitas Muhammadiyah Tangerang, Indonesia
Roslinda Rosli -  Universiti Kebangasaan Malaysia, Malaysia
Rahmadani Siregar -  Universitas Islam Negeri Syekh Ali Hasan Ahmad Addary Padangsidimpuan, Indonesia

Abstract


The application of artificial intelligence (AI) in education is a potential solution to deal with the diversity of students' abilities and learning styles, especially in mathematics learning. Conventional learning approaches are often not able to accommodate these differences optimally. This study aims to analyze the influence of AI-based learning on mathematics learning achievement with student involvement as a mediating variable. The research used a quantitative approach involving 100 students of the University of Muhammadiyah Tangerang. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results of the study show that AI-based learning has a positive and significant effect on mathematics learning achievement. In addition, AI-based learning also increases student engagement, which plays an important role in strengthening academic achievement. These findings confirm that the effectiveness of AI in math learning depends not only on material adjustments, but also on its ability to encourage active student engagement. Therefore, AI integration needs to be accompanied by pedagogical strategies oriented towards increasing participation and learning motivation.


Keywords


Artificial intelligence; Transformative; Mathematics performance

Full Text:

PDF

References


Al Mashagbeh, I. A., Al-Fraihat, A. H., Al-Khasawneh, M. A., & Bataineh, E. A. (2025). Artificial intelligence applications in education: Enhancing learning outcomes and engagement. Education and Information Technologies, 30(2), 1457–1475. https://doi.org/10.1007/s10639-024-12897-2 Al-Maroof, R. S., Alfaisal, R., Salloum, S. A., & Aburayya, A. (2024). Examining the role of student engagement in the relationship between AI applications and learning performance. Education and Information Technologies, 29(1), 221–240. https://doi.org/10.1007/s10639-023-11864-9 Asmar, A., Mariën, I., & Van Audenhove, L. (2022). No one-size-fits-all! Eight profiles of digital inequalities for customized inclusion strategies. New Media & Society, 24(2), 279-310. https://doi.org/10.1177/14614448211063182 Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A systematic review of research on personalized learning: Personalized by whom, to what, how, and for what purpose (s)?. Educational Psychology Review, 33(4), 1675-1715. https://par.nsf.gov/servlets/purl/10274018 Bhatt, P., & Muduli, A. (2024). AI learning intention, learning engagement and behavioral outcomes: An empirical study. Journal of Management Development, 43(6), 920-938. https://doi.org/10.1108/JMD-05-2024-0173 Chen, X., Xie, H., Qin, S. J., Wang, F. L., & Hou, Y. (2025). Artificial intelligence‐supported student engagement research: Text mining and systematic analysis. European Journal of Education, 60(1), e70008. https://doi.org/10.1111/ejed.70008 Cho, M. K., & Kim, S. (2025). Analyzing AI-based educational platforms for supporting personalized mathematics learning. International Electronic Journal of Mathematics Education, 20(4), em0847. https://doi.org/10.29333/iejme/16664 Engelbrecht, J., & Borba, M. C. (2024). Recent developments in using digital technology in mathematics education. ZDM–Mathematics Education, 56(2), 281-292. https://doi.org/10.1007/s11858-023-01530-2 Fletscher, L., Mercado, J., Gómez, Á., & Mendoza-Cardenas, C. (2025). Innovating personalized learning in virtual education through AI. Multimodal Technologies and Interaction, 9(7), 69. https://doi.org/10.3390/mti9070069 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 Guettala, M., Bourekkache, S., Kazar, O., & Harous, S. (2024). Generative artificial intelligence in education: Advancing adaptive and personalized learning. Acta Informatica Pragensia, 13(3), 460-489. https://doi.org/10.18267/j.aip.235 Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584 Integrating Artificial Intelligence in Primary Mathematics Education: Investigating Internal and External Influences on Teacher Adoption. (2025). International Journal of Science and Mathematics Education, 23, 1283-1308. https://doi.org/10.1007/s10763-024-10515-w Irshad, R., Ahmad, I., & Malik, M. (2023). Generative AI-based feedback and its impact on student engagement and learning performance. Computers and Education: Artificial Intelligence, 4, 100145. https://doi.org/10.1016/j.caeai.2023.100145 Lin, C. C., Huang, A. Y., & Lu, O. H. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart learning environments, 10(1), 41. https://doi.org/10.1186/s40561-023-00260-y Liu, B., Zhang, J., Lin, F., Jia, X., & Peng, M. (2025). One size doesn’t fit all: A personalized conversational tutoring agent for mathematics instruction. ArXiv. https://arxiv.org/abs/2502.12633 Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill. Oubagine, R., Laaouina, L., Jeghal, A., & Tairi, H. (2025, July). Advancing MOOCs Personalization: The Role of Generative AI in Adaptive Learning Environments. In International Conference on Artificial Intelligence in Education (pp. 242-254). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-99261-2_22 Ramadhani, A. K., & Ramadani, I. (2024). AI in mathematics education: Potential ranging from automation to personalized learning. Linear: Journal of Mathematics Education. https://doi.org/10.32332/46xc9p64 Saraswat, S. (2023). The role of artificial intelligence in enhancing student engagement in higher education. International Journal of Emerging Technologies in Learning, 18(12), 23–36. https://doi.org/10.3991/ijet.v18i12.43579 Siregar, N. C. (2020, May). Interest STEM based on family background for secondary school students: Validity and reliability instrument using Rasch model analysis. In Proceeding in RSU International Research Conference. https://rsucon.rsu.ac.th/proceedings Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI‐driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921-1947. https://doi.org/10.1002/sd.3221 Tang, W. K.-W. (2025). Artificial intelligence in mathematics education: Trends, challenges, and opportunities. International Journal of Research in Mathematics Education, 3(1), 75–90. https://doi.org/10.24090/ijrme.v3i1.13496 Vieriu, A.-M. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), Article 343. https://doi.org/10.3390/educsci15030343 Wan, Y., Li, R., Li, W., & Du, H. (2025). Impact pathways of AI-supported instruction on learning behaviors, competence development, and academic achievement in engineering education. Sustainability, 17(17), 8059. https://doi.org/10.3390/su17178059 Wang, S., Sun, Z., Wang, H., Yang, D., & Zhang, H. (2025). Enhancing student acceptance of artificial intelligence-driven hybrid learning in business education: Interaction between self-efficacy, playfulness, emotional engagement, and university support. The International Journal of Management Education, 23(2), 101184. https://doi.org/10.1016/j.ijme.2025.101184 Wang, S., Wang, F., & Zhu, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, Article 124167. https://doi.org/10.1016/j.eswa.2024.124167 Wang. (2025). Research on AIGC-driven personalized mathematics learning system. SHS Web of Conferences, 215, 01003. https://doi.org/10.1051/shsconf/202521501003 Xiao, Y., Sun, J., & Li, M. (2023). The impact of emotional and cognitive engagement on students’ academic performance in higher education. Frontiers in Psychology, 14, 1123456. https://doi.org/10.3389/fpsyg.2023.1123456 Xu, Q., Liu, Y., & Li, X. (2025). Unlocking student potential: How AI-driven personalized feedback shapes goal achievement, self-efficacy, and learning engagement through a self-determination lens. Learning and Motivation, 91, 102138. https://doi.org/10.1016/j.lmot.2025.102138




DOI: https://doi.org/10.24952/ejpm.v2i2.17359

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Nur Choiro Siregar, Roslinda Rosli, Rahmadani Siregar

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Editorial Office:

Pascasarjana UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan; Jl. T. Rizal Nurdin Km. 4,5 Sihitang 22733 Padangsidimpuan, North Sumatera, Indonesian. Phone: (+62) 634  22080  Faximili: (+62) 634 24022 e-mail: educofa@uinsyahada.ac.id

Educofa by Uin Syhada is licensed under CC BY-SA 4.0