DIDACTIC POTENTIAL OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN MATHEMATICAL MODELING
Keywords:
artificial intelligence, mathematical modeling, mathematics education, generative AI, higher education, problem-solving.Abstract
The rapid development of artificial intelligence (AI) technologies is creating new opportunities for improving the teaching and learning of mathematical modeling in higher education. Mathematical modeling requires students to transform real-world situations into mathematical representations, identify relevant variables, formulate assumptions, construct and solve models, interpret results, and evaluate the validity of obtained solutions. These stages require not only mathematical knowledge but also analytical thinking, creativity, critical reasoning, and decision-making skills.
References
1. Schindler, M., Cevikbas, M., & Lilienthal, A. J. (2026). Artificial intelligence in mathematics education: A two-dimensional conceptual framework and meta-review. ZDM – Mathematics Education, 58, 831–850.
2. Turmuzi, M., Azmi, S., & Kertiyani, N. M. I. (2025). ChatGPT in school mathematics education: A systematic review of opportunities, challenges, and pedagogical implications. Teaching and Teacher Education. https://doi.org/10.1016/j.tate.2025.105286
3. Miao, F., & Holmes, W. (2023). Guidance for Generative AI in Education and Research. Paris: UNESCO.
4. OECD. (2023). Is Education Losing the Race with Technology? AI’s Progress in Maths and Reading. Paris: OECD Publishing. https://doi.org/10.1787/73105f99-en
5. Cho, H. (2026). Relocating the locus of generation: The AI-GLM framework for generative learning in AI-mediated mathematics education. Educational Psychology Review, 38, Article 116.
