Mathematics Education in the AI Era: Preparing Teachers for Evolving Classroom Demands
Keywords:
Artificial intelligence; Mathematics education; Teacher preparation; Ethics; Inclusion and equityAbstract
With artificial intelligence (AI) redefining mathematics education, teachers must exhibit new competencies to be compliant with the evolving modern classroom requirements. This conceptual study investigated the transformation of mathematics education amid the rise of AI, centering on how to prepare teachers to meet new classroom demands. Guided by Activity Theory and AI Competency Frameworks for Teachers, the study synthesized current literature for strategic insights on teacher competencies, ethical practices, and inclusive, technology-based education principles. The research examined significant challenges and prospects associated with AI adoption, including the imperative for robust teacher preparation, the formulation of fair and transparent policies, and efforts to mitigate bias for all students. Through narrative literature review and thematic analysis, the study identified dominant themes and offered actionable recommendations for teacher education, institutional policy, and continuous professional development. By articulating the requisite skills and ethical imperatives for successful AI integration in mathematics education, this investigation informs adaptive teaching practices and establishes a trajectory for further scholarly inquiry in the field. The conclusion underscores the necessity of equipping teachers to responsibly maximize AI’s potential while promoting ethical and equitable mathematics classrooms.
https://doi.org/10.26803/ijlter.24.10.19
References
Abboud, M., Goodchild, S., Jaworski, B., Potari, D., Robert, A., & Rogalski, J. (2018). Use of activity theory to make sense of mathematics teaching: A dialogue between perspectives/test. Annales de Didactique et de Sciences Cognitives. Revue internationale de didactique des mathématiques, (Special issue), 61-92. https://doi.org/10.4000/adsc.514
Adams, C., Pente, P., Lemermeyer, G., & Rockwell, G. (2023). Ethical principles for artificial intelligence in K-12 education. Computers and Education: Artificial Intelligence, 4, 100131. http://dx.doi.org/10.1016/j.caeai.2023.100131
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099. http://dx.doi.org/10.1016/j.caeai.2022.100099
Basheer, A. (2022). The art and science of writing narrative reviews. International Journal of Advanced Medical and Health Research, 9(2), 124-126. http://dx.doi.org/10.4103/ijamr.ijamr_234_22
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://dx.doi.org/10.1191/1478088706qp063oa
Cheng, W.-K. (2025). Role of mathematics teachers in learner’s diversity using AI tools. Engineering Proceedings, 89(1), 19.https://doi.org/10.3390/engproc2025089019
Chiu, T. K., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6. http://dx.doi.org/10.1016/j.caeo.2024.100171
Cukurova, M., & Miao, F. (2024). AI competency framework for teachers. UNESCO Publishing.
Eden, C. A., Chisom, O. N., & Adeniyi, I. S. (2024). Integrating AI in education: Opportunities, challenges, and ethical considerations. Magna Scientia Advanced Research and Reviews, 10(2), 006-013. https://doi.org/10.30574/msarr.2024.10.2.0039
Engelbrecht, J., & Borba, M. C. (2024). Recent developments in using digital technology in mathematics education. ZDM–Mathematics Education, 56(2), 281-292.
http://dx.doi.org/10.1007/s11858-023-01530-2
Engeström, Y. (1987) Learning by expanding: An activity theoretical approach to developmental research. Helsinki: Orienta-Konsultit.
Filo, Y., Rabin, E., & Mor, Y. (2024). An artificial intelligence competency framework for teachers and students: Co-created with teachers. European Journal of Open, Distance & E-Learning, 26(1). http://doi.org/10.2478/eurodl-2024-0012
Flores-Viva, J. M., & García-Peñalvo, F. J. (2023). Reflections on the ethics, potential, and challenges of artificial intelligence in the framework of quality education (SDG4). Comunicar: Media Education Research Journal, 31(74), 35-44. http://dx.doi.org/10.3916/C74-2023-03
Gabriel, F., Kennedy, J., Marrone, R., & Leonard, S. (2025). Pragmatic AI in education and its role in mathematics learning and teaching. npj Science of Learning, 10(1), 1-7. http://dx.doi.org/10.1038/s41539-025-00315-4
Heafner, T., & Ziv, E. (2024, March). Artificial intelligence in education: preparing educators and learners for the future. In Society for Information Technology & Teacher Education International Conference (pp. 768-778). Association for the Advancement of Computing in Education (AACE).
Holstein, K., & Doroudi, S. (2022). Equity and artificial intelligence in education. In The ethics of artificial intelligence in education (pp. 151-173). Routledge.
Josselson, R., & Hammack, P. L. (2021). Essentials of narrative analysis. American Psychological Association. https://psycnet.apa.org/doi/10.1037/0000246-000
Karsenti, T. (2019). Artificial intelligence in education: The urgent need to prepare teachers for tomorrow’s schools. Formation et profession, 27(1). http://dx.doi.org/10.18162/fp.2019.a166
Kayyali, M. (2025). The evolution of AI in education from concept to classroom. In Navigating barriers to ai implementation in the classroom (pp. 325-368). IGI Global Scientific Publishing. http://doi.org/10.4018/979-8-3373-1827-1.ch013
Kim, S., Jang, Y., Choi, S., Kim, W., Jung, H., Kim, S., & Kim, H. (2021). Analyzing teacher competency with TPACK for K-12 AI education. KI-Künstliche Intelligenz, 35(2), 139-151. http://dx.doi.org/10.1007/s13218-022-00770-w
Kohnke, S., & Zaugg, T. (2025). Artificial intelligence: An untapped opportunity for equity and access in STEM education. Education Sciences, 15(1), 68. http://doi.org/10.3390/educsci15010068
Lata, P. (2024). Towards equitable learning: Exploring artificial intelligence in inclusive education. International Journal of Law Management & Humanities, 7(5), 416–434. https://dx.doi.org/10.10000/IJLMH.118279
Lin, Y. T., Hung, T. W., & Huang, L. T. L. (2021). Engineering equity: How AI can help reduce the harm of implicit bias. Philosophy & Technology, 34(Suppl 1), 65-90.
Loc, N. P., Oanh, N. P. P., Thao, N. P., Van De, T., & Triet, L. V. M. (2022). Activity theory as a framework for teaching mathematics: An experimental study. Heliyon, 8(10). http://dx.doi.org/10.1016/j.heliyon.2022.e10789
Madaan, S., Sai, T. B., Mandal, R., Kar, A., Sharma, M., Selvakumar, P., & Manjunath, T. C. (2025). Math Instruction and AI. In M. Kessinger, G. Nelson, L. Lennex, & K. Nettleton (Eds.), Examining AI disruption in educational settings: Challenges and opportunities (pp. 161-184). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7873-1.ch006
McGlone, C., Greenlee, M., & Leinwand, S. (2025). Navigating AI in the mathematics classroom. INTED2025 Proceedings, 2654–2657. https://doi.org/10.21125/inted.2025.0716
Mikeladze, T., Meijer, P. C., & Verhoeff, R. P. (2024). A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review. European Journal of Education, 59(3), e12663. https://doi.org/10.1111/ejed.12663
Mouta, A., Torrecilla-Sánchez, E. M., & Pinto-Llorente, A. M. (2025). Comprehensive professional learning for teacher agency in addressing ethical challenges of AIED: Insights from educational design research. Education and Information Technologies, 30(3), 3343-3387. http://dx.doi.org/10.1007/s10639-024-12946-y
Mukhibin, A., Wahyudin, Rusyid, H. K., Lutfi, A., & Siahaan, E. Y. S. (2023). Mathematics education in the age of artificial intelligence: How artificial intelligence can serve mathematical human learning. The Social Science Journal. Advance online publication. https://dx.doi.org/10.1080/03623319.2023.2243688
Mutawa, A. M., & Sruthi, S. (2025). UNESCO's AI competency framework: Challenges and opportunities in educational settings. Impacts of generative AI on the future of research and education, 75-96. http://dx.doi.org/10.4018/979-8-3693-0884-4.ch004
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221-4241. http://dx.doi.org/10.1007/s10639-022-11316-w
Pagliara, S. M., Bonavolontà, G., Pia, M., Falchi, S., Zurru, A. L., Fenu, G., & Mura, A. (2024). The integration of artificial intelligence in inclusive education: A scoping review. Information, 15(12), 774. http://dx.doi.org/10.3390/info15120774
Pautasso, M. (2019). The structure and conduct of a narrative literature review. A guide to the scientific career: Virtues, communication, research and academic writing, 299-310. http://dx.doi.org/10.1002/9781118907283.ch31
Payadnya, I. P. A. A., Putri, G. A. M. A., Suwija, I. K., Saelee, S., & Jayantika, I. G. A. N. T. (2025). Cultural integration in AI-enhanced mathematics education: Insights from Southeast Asian educators. Journal for Multicultural Education, 19(1), 58-72. https://dx.doi.org/10.1108/JME-09-2024-0119
Peterson, R. (2024). AI in the K-12 mathematics classroom: An ethical study of artificial intelligence in education (master’s thesis, Harvard University).
Roshanaei, M. (2024). Towards best practices for mitigating artificial intelligence implicit bias in shaping diversity, inclusion and equity in higher education. Education and Information Technologies, 29(14), 18959-18984. http://doi.org/10.1007/s10639-024-12605-2
Salas-Pilco, S. Z., Xiao, K., & Oshima, J. (2022). Artificial intelligence and new technologies in inclusive education for minority students: A systematic review. Sustainability, 14(20), 13572. http://dx.doi.org/10.3390/su142013572
Sannino, A., Daniels, H., & Gutiérrez, K. D. (Eds.). (2009). Learning and expanding with activity theory. Cambridge University Press. https://dx.doi.org/10.1017/CBO9780511809989
Sato, E., Shyyan, V., Chauhan, S., & Christensen, L. (2024). Putting AI in fair: A framework for equity in AI-driven learner models and inclusive assessments. Journal of Measurement and Evaluation in Education and Psychology, 15(Special Issue), 263-281. http://dx.doi.org/10.21031/epod.1526527
Shen, L., & Su, A. (2020). The changing roles of teachers with AI. In D. Burgos (Ed.), Revolutionizing education in the age of AI and machine learning (pp. 1–25). IGI
Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023). Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Computers in Human Behavior, 146, 107798. http://dx.doi.org/10.1016/j.chb.2023.107798
Zreik, M. (2024). Redefining mathematics education in the age of artificial intelligence: Challenges and opportunities. In E. Njurai & A. Uworwabayeho (Eds.), Impacts of Globalization and Innovation in Mathematics Education (pp. 19-40). IGI Global Scientific Publishing. https://dx.doi.org/10.4018/979-8-3693-2873-6.ch002
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Thuthukile Jita, Loyiso Currell Jita, Adebayo Akinyinka Omoniyi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published by IJLTER are licensed under a Creative Commons Attribution Non-Commercial No-Derivatives 4.0 International License (CCBY-NC-ND4.0).
