Artificial Intelligence in Mathematics Education: Trends, Challenges, and Opportunities
DOI:
https://doi.org/10.24090/ijrme.v3i1.13496Keywords:
artificial intelligence, mathematics education, Personalised learning, educational technologyAbstract
This paper examines the transformative influence of artificial intelligence (AI) on mathematics education. It delineates the evolution of educational technology, progressing from basic computational tools to advanced AI-driven systems that enhance personalised learning experiences. The study highlights how AI adapt to individual student needs, increase engagement, and promote deeper understanding of mathematical concepts by analysing contemporary trends such as intelligent tutoring systems, automated assessments, and personalised learning platforms. Additionally, it explores the potential of AI to foster inclusive learning environments, particularly for students with special needs. The paper also considers challenges related to infrastructure, teacher preparedness, and equity in AI implementation, alongside future directions for research and innovation in the application of AI within mathematics education.References
Alali, R., & Wardat, Y. (2024). Opportunities and Challenges of Integrating Generative Artificial Intelligence in Education. International Journal of Religion, 5(7), 784–793. https://doi.org/10.61707/8y29gv34
Atchley, P., Pannell, H., Wofford, K., Hopkins, M., & Atchley, R. A. (2024). Human and AI collaboration in the higher education environment: opportunities and concerns. Cognitive Research: Principles and Implications, 9(1). https://doi.org/10.1186/s41235-024-00547-9
Awang, L. A., Yusop, F. D., & Danaee, M. (2025). Current practices and future direction of artificial intelligence in mathematics education: A systematic review. International Electronic Journal of Mathematics Education, 20(2), em0823. https://doi.org/10.29333/iejme/16006
Beaudouin-Lafon, M., Bødker, S., & Mackay, W. E. (2021). Generative Theories of Interaction. ACM Transactions on Computer-Human Interaction, 28(6), 1–54. https://doi.org/10.1145/3468505
Bernacki, M. L., & Walkington, C. (2018). The role of situational interest in personalized learning. Journal of Educational Psychology, 110(6), 864.https://doi.org/10.1037/edu0000250
Botelho, A., Baral, S., Erickson, J. A., Benachamardi, P., & Heffernan, N. T. (2023). Leveraging natural language processing to support automated assessment and feedback for student open responses in mathematics. Journal of computer assisted learning, 39(3), 823-840. https://doi.org/10.1111/jcal.12793
Chalkiadakis, A., Seremetaki, A., Kanellou, A., Kallishi, M., Mastrokoukou, S., Morfopoulou, A., & Moraitaki, M. (2024). Impact of Artificial Intelligence and Virtual Reality on Educational Inclusion: A Systematic Review of Technologies Supporting Students with Disabilities. Education Sciences, 14(11), 1223. https://doi.org/10.3390/educsci14111223
Chan, K. K. W., & Tang, W. K. W. (2025). Evaluating English Teachers' Artificial Intelligence Readiness and Training Needs with a TPACK-Based Model. World Journal of English Language, 15(1), 129-145. https://doi.org/10.5430/wjel.v15n1p129
Chen, J. J., & Lin, J. C. (2023). Artificial intelligence as a double-edged sword: Wielding the POWER principles to maximize its positive effects and minimize its negative effects. Contemporary Issues in Early Childhood, 25(1), 146–153. https://doi.org/10.1177/14639491231169813
Cooper, G., & Tang, K.-S. (2024). Pixels and Pedagogy: Examining Science Education Imagery by Generative Artificial Intelligence. Journal of Science Education and Technology, 33(4), 556–568. https://doi.org/10.1007/s10956-024-10104-0
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
Ezzaim, A., Aqqal, A., Dahbi, A., & Haidine, A. (2023). AI-Based Adaptive Learning: A Systematic Mapping of the Literature. JUCS - Journal of Universal Computer Science, 29(10), 1161–1198. https://doi.org/10.3897/jucs.90528
Fuentes-Riffo, K., Friz-Carrillo, M., Kotz-Grabole, G., Sanhueza-Campos, C., Espejo-Burkart, F., Salcedo-Lagos, P., & Pinacho-Davidson, P. (2023). The Influence of Gamification on High School Students’ Motivation in Geometry Lessons. Sustainability, 15(21), 15615. https://doi.org/10.3390/su152115615
Gurung, A., & Heffernan, N. T. (2022, July). Exploring Fairness in Automated Grading and Feedback Generation of Open-Response Math Problems. In International Conference on Artificial Intelligence in Education (pp. 71-76). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-11647-6_12
• Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational psychologist, 41(2), 111-127. https://doi.org/10.1207/s15326985ep4102_4
Høgheim, S., & Reber, R. (2015). Supporting interest of middle school students in mathematics through context personalization and example choice. Contemporary Educational Psychology, 42, 17-25.https://doi.org/10.1016/j.cedpsych.2015.03.006
Inoferio, H. V., Damin, M., Espartero, M., Chavez, J. V., & Asiri, M. (2024). Coping with math anxiety and lack of confidence through AI-assisted Learning. Environment and Social Psychology, 9(5). https://doi.org/10.54517/esp.v9i5.2228
Kaput, J., Hegedus, S., & Lesh, R. (2020). Technology becoming infrastructural in mathematics education. In Foundations for the future in mathematics education (pp. 173-191). Routledge.
• Kooli, C., & Yusuf, N. (2025). Transforming educational assessment: Insights into the use of ChatGPT and large language models in grading. International Journal of Human–Computer Interaction, 41(5), 3388- 3399.https://doi.org/10.1080/10447318.2024.2338330
Lavidas, K., Apostolou, Z., & Papadakis, S. (2022). Challenges and Opportunities of Mathematics in Digital Times: Preschool Teachers’ Views. Education Sciences, 12(7), 459. https://doi.org/10.3390/educsci12070459
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
Lopez-Caudana, E., Ramirez-Montoya, M. S., Rodríguez-Abitia, G., & Martínez-Pérez, S. (2020). Using Robotics to Enhance Active Learning in Mathematics: A Multi-Scenario Study. Mathematics, 8(12), 2163. https://doi.org/10.3390/math8122163
Maghsudi, S., Van Der Schaar, M., Lan, A., & Xu, J. (2021). Personalized Education in the Artificial Intelligence Era: What to Expect Next. IEEE Signal Processing Magazine, 38(3), 37–50. https://doi.org/10.1109/msp.2021.3055032
Mossige, M., Arendal, E., Svendsen, H. B., & Kongskov, L. (2023). How do technologies meet the needs of the writer with dyslexia? An examination of functions scaffolding the transcription and proofreading in text production aimed towards researchers and practitioners in education. Dyslexia, 29(4), 408–425. https://doi.org/10.1002/dys.1752
Nagaraj, B. K., N, S. K., A, K., Sachdev, H. K., S, A., & R, S. B. (2023). The Emerging Role of Artificial Intelligence in STEM Higher Education: A Critical Review. International Research Journal of Multidisciplinary Technovation, 1–19. https://doi.org/10.54392/irjmt2351
Opesemowo, O. A. G., & Adewuyi, H. O. (2024). A systematic review of artificial intelligence in mathematics education: The emergence of 4IR. Eurasia Journal of Mathematics, Science and Technology Education, 20(7), em2478. https://doi.org/10.29333/ejmste/14762
Rasheed, Z., Ghwanmeh, S., & Abualkishik, A. Z. (2023). Harnessing Artificial Intelligence for Personalized Learning: A Systematic Review. Data and Metadata, 2, 146. https://doi.org/10.56294/dm2023146
Remoto, J. P. (2023). ChatGPT and other AIs: Personal relief and limitations among mathematics-oriented learners. Environment and Social Psychology, 9(1). https://doi.org/10.54517/esp.v9i1.1911
Renninger, K. A., & Pozos-Brewer, R. K. (2015). Interest, Psychology of. In International Encyclopedia of the Social & Behavioral Sciences (pp. 378–385). Elsevier Inc.
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International journal of artificial intelligence in education, 26, 582-599.https://doi.org/10.1007/s40593-016-0110-3
Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online mathematics homework increases student achievement. AERA open, 2(4), 2332858416673968.https://doi.org/10.1177/2332858416673968
Santos, O. C., Kravcik, M., & Boticario, J. G. (2016). Preface to special issue on user modelling to support personalization in enhanced educational settings. International Journal of Artificial Intelligence in Education, 26(3), 809-820. https://doi.org/10.1007/s40593-016-0114-z
Song, C., Shin, S.-Y., & Shin, K.-S. (2024). Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways. Applied Sciences, 14(2), 916. https://doi.org/10.3390/app14020916
Torres-Peña, R. C., Ariza, E. A., Chacuto-López, E., Vergara, D., & Peña-González, D. (2024). Updating Calculus Teaching with AI: A Classroom Experience. Education Sciences, 14(9), 1019. https://doi.org/10.3390/educsci14091019
Vegas, E., & Winthrop, R. (2020). Beyond reopening schools: How education can emerge stronger than before COVID-19. Brookings.
Walkington, C., & Bernacki, M. L. (2019). Personalizing algebra to students’ individual interests in an intelligent tutoring system: Moderators of impact. International Journal of Artificial Intelligence in Education, 29, 58-88. https://doi.org/10.1007/s40593-018-0168-1
Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2020). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793–803. https://doi.org/10.1080/10494820.2020.1808794
Wardat, Y., Jarrah, A. M., Tashtoush, M. A., & Alali, R. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272
Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00448-3
Wijaya, T. T., Yu, Q., Cao, Y., He, Y., & Leung, F. K. (2024). Latent Profile Analysis of AI Literacy and Trust in Mathematics Teachers and Their Relations with AI Dependency and 21st-Century Skills. Behavioral Sciences, 14(11), 1008. https://doi.org/10.3390/bs14111008
Williams, R. T. (2024). The ethical implications of using generative chatbots in higher education. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1331607
Wong, J.M.S., Tang, W.K.W., and Li, K.C. (2025). Digital transformation in higher education: tertiary students’ perspectives on online learning and its implications for the future. International Journal of Innovation and Learning, 37(5),1-18. https://doi.org/10.1504/IJIL.2025.144600
Yi, L., Liu, D., Jiang, T., & Xian, Y. (2024). The effectiveness of AI on K-12 students’ mathematics learning: A systematic review and meta-analysis. International Journal of Science and Mathematics Education, 1-22. https://doi.org/10.1007/s10763-024-10499-7
Zuo, M., Hu, Y., Ma, Y., Kong, S., & Xiao, M. (2023). The Effects of Using Scaffolding in Online Learning: A Meta-Analysis. Education Sciences, 13(7), 705. https://doi.org/10.3390/educsci13070705
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