EXPLORING THE USE OF AI IN ENGAGING MATHEMATICS TEACHING AND LEARNING

Authors: Wei Liu*, Yu Xing & Ardy Purwanto

ABSTRACT

While AI-supported tools have rapidly become a fixture in mathematics classrooms, their impact on student engagement remains a subject of debate. This study examines how secondary students (N=167) and teachers perceive and use these tools in practice. Quantitative results show that nearly all students use AI in some form—with ChatGPT being the most common—and usage remains consistent across genders.

Feedback from 125 students reveals a clear conflict: while they value the instant guidance and practice AI provides, they remain concerned about over-reliance and inaccurate answers. Interpreted through Cognitive Load Theory, these findings highlight a tension between the convenience of AI and the pedagogical need for ‘productive struggle,’ where deep understanding arises from effortful engagement.

Effective integration requires guiding students to become critical and reflective users of AI, while maintaining a focus on foundational mathematical understanding, reasoning skills, and responsible use. These results highlight the importance of pedagogical frameworks that balance the benefits of AI with the cultivation of critical thinking and sustained conceptual learning in mathematics classrooms.

Keywords: mathematics education, Artificial Intelligence, Cognitive Load Theory, Productive Struggle, Secondary Education

REFERENCES

  1. Adıgüzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152
  2. Alrakhawi, H. A., Jamiat, N. U. R. U. L. L. I. Z. A. M., & AbuNaser, S. S. (2023). Intelligent tutoring systems in education: A systematic review of usage, tools, effects and evaluation. Journal of Theoretical and Applied Information Technology, 101(4), 1205–1226. https://www.researchgate.net/publication/369019319
  3. Attard, C. (2012). Engagement with mathematics: What does it mean and what does it look like? Australian Primary Mathematics Classroom, 17(1), 9–13.
  4. Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. (2016). Adapting to individual differences in intelligent tutoring systems. In J. L. Green, G. Camilli, & P. B. Elmore (Eds.), International Handbook of Research on Teachers and Teaching (pp. 507–516). Springer.
  5. Boaler, J. (2015). Mathematical mindsets. https://education.ecu.edu/wp-content/uploads/sites/171/2019/01/Boaler-Ch.-5.pdf
  6. Castillo, E., Tapia, N., Palencia, S., & Pavón, C. (2024). Use of artificial intelligence in high school mathematics teaching. Revista Iberoamericana de Educación, 8(3).
  7. Commonwealth of Australia. (2008). National numeracy review report: Report to the Council of Australian Governments. Canberra: Human Capital Working Group, Council of Australian Governments.
  8. Dhimolea, T. K., Kaplan-Rakowski, R., & Lin, L. (2022). Supporting social and emotional well-being with artificial intelligence. In M. V. Albert, L. Lin, M. J. Spector, & L. S. Dunn (Eds.), Bridging human intelligence and artificial intelligence (pp. 123–137). Springer. https://doi.org/10.1007/978-3-030-84729-6_8
  9. (2023a). Advanced Algebra Georgia. https://www.edmentum.com/intl/curricula-catalog
  10. (2023b). Unpacking Exact Path: What the learning path looks like. https://www.edmentum.com/articles/exact-path-learning-path
  11. El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1), 53. https://doi.org/10.1186/s41239-021-00289-4
  12. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
  13. Fung, F., Tan, C. Y., & Chen, G. (2018). Student engagement and mathematics achievement: Unraveling main and interactive effects. Psychology in the Schools, 55(7), 815–831. https://doi.org/10.1002/pits.22139
  14. Hina, S., & Dominic, P. D. D. (2020). Information security policies’ compliance: A perspective for higher education institutions. Journal of Computer Information Systems, 60(3), 201–211. https://doi.org/10.1080/08874417.2018.1432996
  15. Hollands, F. M., & Tirthali, D. (2014). Adaptive learning technologies for education: A review of the literature. Educational Technology Research and Development, 62(4), 381–404. https://doi.org/10.1007/s11423-014-9333-4
  16. Igbokwe, J. C., Odulaja, B. A., Adediran, F. E., Adewusi, O. E., Daraojimba, R. E., & Okunade, B. A. (2024). Urban community development: Reviewing non profit impact in the USA and Africa. World Journal of Advanced Research and Reviews, 21(2), 113–123. https://doi.org/10.30574/wjarr.2024.21.2.0422
  17. Ingram, N. (2013). Mathematical engagement skills. In V. Steinle, L. Ball, & C. Bardini (Eds.), Mathematics education: Yesterday, today, and tomorrow (pp. 402–409). Proceedings of the 36th Annual Conference of the Mathematics Education Research Group of Australasia. MERGA.
  18. Ingersoll, R. M., & Tran, H. (2023). Teacher shortages and turnover in rural schools in the US: An organizational analysis. Educational Administration Quarterly, 59(2), 396–431. https://doi.org/10.1177/0013161X231159922
  19. Jasin, J., Ng, H. T., Atmosukarto, I., et al. (2023). The implementation of chatbot-mediated immediacy for synchronous communication in an online chemistry course. Education and Information Technologies, 28(8), 10665–10690. https://doi.org/10.1007/s10639-023-11602-1
  20. Khazanchi, R., Di Mitri, D., & Drachsler, H. (2025). The effect of AI based systems on mathematics achievement in rural context: A quantitative study. Journal of Computer Assisted Learning, 41(1), e13098. https://doi.org/10.1111/jcal.13098
  21. Khosravi, H., Sadiq, S., & Gasevic, D. (2020). Development and adoption of an adaptive learning system: Reflections and lessons learned. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (pp. 58–64). https://doi.org/10.1145/3328778.3366900
  22. Liu, W., Osmond, D., Affas, H. (2023). Pedagogical Practice- A Study about Designing Mathematics Investigation Tasks for Senior Students in Australia. International Journal of Social Science and Human Research,11(06),7146-7157. DOI: 10.47191/ijsshr/v6 i11-77
  23. Maamin, M., Maat, S. M., & Iksan, Z. H. (2021). The influence of student engagement on mathematical achievement among secondary school students. Mathematics, 10(1), 41. https://doi.org/10.3390/math10010041
  24. Mohammed, P. S., & Watson, E. N. (2019). Towards inclusive education in the age of artificial intelligence: Perspectives, challenges, and opportunities. In J. Knox, Y. Wang, & M. Gallagher (Eds.), Artificial intelligence and inclusive education: Perspectives on rethinking and reforming education (pp. 13–30). Springer. https://doi.org/10.1007/978-981-13-8161-4_2
  25. Molenaar, I., & Knoop-van Campen, C. A. (2018). How teachers make dashboard information actionable. IEEE Transactions on Learning Technologies, 12(3), 347–355. https://doi.org/10.1109/TLT.2018.2851585
  26. Ning, H., Liu, R., & Gan, W. (2020). Research on the application of artificial intelligence in education. Journal of Physics: Conference Series, 1544(1), 012165. https://doi.org/10.1088/1742-6596/1544/1/012165
  27. Ogborigbo, J. C., Sobowale, O. S., Amienwalen, E. I., Owoade, Y., Samson, A. T., & Egerson, J. (2024). Strategic integration of cyber security in business intelligence systems for data protection and competitive advantage. World Journal of Advanced Research and Reviews, 23(1), 81–96. https://doi.org/10.30574/wjarr.2024.23.1.1900
  28. Orhani, S. (2021). Artificial intelligence in teaching and learning mathematics. Kosovo Educational Research Journal, 2(3), 29–38.
  29. Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020). Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education, 28(6), 1549–1565. https://doi.org/10.1002/cae.22326
  30. Rane, N. (2023). Enhancing mathematical capabilities through ChatGPT and similar generative artificial intelligence: Roles and challenges in solving mathematical problems. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4603237
  31. Shikalepo, E. E. (2020). Challenges facing teaching at rural schools: A review of related literature. International Journal of Research and Innovation in Social Science, 4(5), 211–218.
  32. 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
  33. Sullivan, P., & McDonough, A. (2007). Eliciting positive student motivation for learning mathematics. In J. Watson & K. Beswick (Eds.), Mathematics: Essential Research, Essential Practice (Vol. 2, pp. 698–707). Mathematics Education Research Group of Australasia (MERGA).
  34. Sullivan, P., McDonough, A., & Turner Harrison, R. (2004). Students’ perceptions of factors contributing to successful participation in mathematics. In M. J. Høines & A. B. Fuglestad (Eds.), Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education (Vol. 3, pp. 289–296). Bergen University College.
  35. Supriyadi, E., & Kuncoro, K. S. (2023). Exploring the future of mathematics teaching: Insight with ChatGPT. Union: Jurnal Ilmiah Pendidikan Matematika, 11(2), 305–316. https://doi.org/10.30738/union.v11i2.14898
  36. (2024). Gradescope. https://www.gradescope.com/
  37. Victoria Department of Education and Training. (2004). Middle years of schooling: Overview of Victorian research 1998–2004. State of Victoria, Department of Education and Training.
  38. Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), 1–18.
  39. Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in higher education: Critical issues and perspectives. Teaching in Higher Education, 25(4), 351–365. https://doi.org/10.1080/13562517.2020.1744956
  40. Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet? — A systematic literature review on chatbots in education. Frontiers in Artificial Intelligence, 4, 654924. https://doi.org/10.3389/frai.2021.654924
  41. Yang, G. (2024). The current status and development trends of information technology aided mathematics teaching. Journal of Computer Technology and Electronic Research, 1(2), 334–? https://doi.org/10.70767/jcter.v1i2.334
  42. Zaman, B. U. (2024). Transforming education through AI: Benefits, risks, and ethical considerations (Preprint). Preprints.org. https://doi.org/10.20944/preprints202407.0859.v1
  43. Zhu, Y. Y. (2024). The impact of AI-assisted teaching on students’ learning and psychology. Journal of Education, Humanities and Social Sciences, 38, 111–116.