FROM ANSWER GENERATION TO CONCEPTUAL UNDERSTANDING: A MIXED-METHODS INVESTIGATION OF GENERATIVE AI-SUPPORTED MATHEMATICS LEARNING
DOI:
https://doi.org/10.5281/zenodo.21766730Keywords:
Generative Artificial Intelligence, Mathematics Learning, Conceptual UnderstandingAbstract
The development of Generative Artificial Intelligence has brought significant changes to mathematics learning practices, particularly in providing fast and interactive support for students. However, the effectiveness of Generative AI is measured not only by its ability to generate answers but also by its contribution to the development of in-depth mathematical conceptual understanding. This study aims to examine the role of Generative AI in supporting the transition of mathematics learning from simply generating answers to strengthening students' conceptual understanding. The study used a literature review with a mixed-methods approach that integrates quantitative and qualitative findings from various relevant scientific publications. The results indicate that Generative AI has great potential to increase access to mathematical explanations, provide personalized feedback, and help students understand concepts through diverse representations and reasoning. On the other hand, excessive reliance on AI-generated answers has the potential to reduce students' cognitive engagement and hinder the development of critical thinking skills if not accompanied by appropriate pedagogical strategies. This study provides theoretical and practical implications for the development of mathematics learning models that effectively utilize Generative AI to support sustained conceptual understanding.
Downloads
References
Anwar, K., Sherlyna, A., Salsanifa, A. M., Tyas, H. A., Prayudistyan, S. E., Saputra, G. A., & Maulana, I. (2026a). GENERATIVE ARTIFICIAL INTELLIGENCE IN MATHEMATICS EDUCATION: A SYSTEMATIC REVIEW OF DATA-DRIVEN APPLICATIONS, LEARNING THEORIES, AND IMPLICATIONS FOR SUSTAINABLE DEVELOPMENT GOAL. AL JABAR: Jurnal Pendidikan Dan Pembelajaran Matematika, 5(2), 205–230. https://doi.org/10.46773/x224sd70
Anwar, K., Sherlyna, A., Salsanifa, A. M., Tyas, H. A., Prayudistyan, S. E., Saputra, G. A., & Maulana, I. (2026b). GENERATIVE ARTIFICIAL INTELLIGENCE IN MATHEMATICS EDUCATION: A SYSTEMATIC REVIEW OF DATA-DRIVEN APPLICATIONS, LEARNING THEORIES, AND IMPLICATIONS FOR SUSTAINABLE DEVELOPMENT GOAL. AL JABAR: Jurnal Pendidikan Dan Pembelajaran Matematika, 5(2), 205–230. https://doi.org/10.46773/x224sd70
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
Besas, J., Patac, L., & Jr, A. P. (2026). AI-Facilitated Self-Directed Learning and Mathematics Performance: A Mixed-Methods Study on ChatGPT Use among Generation Z Students. International Journal of Contemporary Educational Research, 13(1), 15–30. https://eric.ed.gov/?id=EJ1505621
Chamola, V., Dave, D., Goyal, I., & Sharma, S. (2026). Generative Artificial Intelligence in STEM Education: A Review of Applications, Benefits and Challenges. Expert Systems, 43(2), e70201. https://doi.org/10.1111/exsy.70201
Chen, S.-Y., Chen, W.-C., & Lai, C.-F. (2025). Generative AI as a reflective scaffold in a UAV-based STEM project: A mixed-methods study on students’ higher-order thinking and cognitive transformation. Education and Information Technologies, 30(17), 24787–24814. https://doi.org/10.1007/s10639-025-13758-4
Choi, W.-Y. (2026). Integrating Generative AI in University Mathematics: Reflective and collaborative learning as pedagogical safeguards. Education and Information Technologies. https://doi.org/10.1007/s10639-026-13945-x
Cosentino, G., Anton, J., Sharma, K., Gelsomini, M., Giannakos, M., & Abrahamson, D. (2025). Generative AI and multimodal data for educational feedback: Insights from embodied math learning. British Journal of Educational Technology, 56(5), 1686–1709. https://doi.org/10.1111/bjet.13587
Cruz, S., & Viseu, F. (2026). AI-supported hybrid learning in exploratory geometry: For higher education. European Journal of Science and Mathematics Education, 14(3), 471–488. https://doi.org/10.30935/scimath/18798
Daher, W., & Gierdien, F. (2024). Use of Language By generative AI Tools in Mathematical Problem Solving: The Case of ChatGPT. African Journal of Research in Mathematics, Science and Technology Education, 28(2), 222–235. https://doi.org/10.1080/18117295.2024.2384676
Gabrovšek, R., & Rihtaršič, D. (2025). Custom Generative Artificial Intelligence Tutors in Action: An Experimental Evaluation of Prompt Strategies in STEM Education. Sustainability, 17(21), 9508. https://doi.org/10.3390/su17219508
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2025). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518–2544. https://doi.org/10.1080/0144929X.2024.2394886
Kamau, B., & Shukla, N. (2026). Experimentation and Potential for Generative AI Tools in Gateway Mathematics. International Journal of Technology in Education and Science, 10(2), 191–218. https://eric.ed.gov/?id=EJ1506259
Liu, B., Zhang, W., & Wang, F. (2026). Can Generative Artificial Intelligence Effectively Enhance Students’ Mathematics Learning Outcomes?—A Meta-Analysis of Empirical Studies from 2023 to 2025. Education Sciences, 16(1), 140. https://doi.org/10.3390/educsci16010140
Liu, J., Sun, D., Sun, J., Wang, J., & Yu, P. L. H. (2025). Designing a generative AI enabled learning environment for mathematics word problem solving in primary schools: Learning performance, attitudes and interaction. Computers and Education: Artificial Intelligence, 9, 100438. https://doi.org/10.1016/j.caeai.2025.100438
Mailizar, M., Hidayat, M., Syukri, M., & Wijayanti, D. (2026). Exploring the impact of generative AI on pre-service mathematics teacher TPACK: A mixed-method study. Discover Education, 5(1), 453. https://doi.org/10.1007/s44217-026-01432-x
Maity, S., & Deroy, A. (2024). The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models (arXiv:2410.09576). arXiv. https://doi.org/10.48550/arXiv.2410.09576
Manik, M., Sudiarta, P. I. G. P., & Suparta, I. N. (2026a). The Role of Artificial Intelligence and Multimodal Approaches in Enhancing Learning Engagement and Mathematics Conceptual Understanding Among Junior High School Students: A Systematic Literature Review. EduMatSains : Jurnal Pendidikan, Matematika Dan Sains, 10(3), 134–149. https://doi.org/10.33541/edumatsains.v10i3.7597
Manik, M., Sudiarta, P. I. G. P., & Suparta, I. N. (2026b). The Role of Artificial Intelligence and Multimodal Approaches in Enhancing Learning Engagement and Mathematics Conceptual Understanding Among Junior High School Students: A Systematic Literature Review. EduMatSains : Jurnal Pendidikan, Matematika Dan Sains, 10(3), 134–149. https://doi.org/10.33541/edumatsains.v10i3.7597
Nanda, S. B., & Pradhan, D. K. (2025). Artificial Intelligence in Mathematics Education: A Systematic Review of Global Trends and Emerging Themes. Journal of Mathematics, Science and Technology Education, 2(2), 153–175. https://doi.org/10.12973/jmste.2.2.153
Okwuobasi, M. C. (2026). Adoption and pedagogical use of generative artificial intelligence in K-12 mathematics/science/language classrooms: Challenges and opportunities. International Journal of Applied Resilience and Sustainability, 2(2), 1188–1219. https://doi.org/10.70593/deepsci.0202049
Rane, N. (2023a). Enhancing Mathematical Capabilities through ChatGPT and Similar Generative Artificial Intelligence: Roles and Challenges in Solving Mathematical Problems (SSRN Scholarly Paper No. 4603237). Social Science Research Network. https://doi.org/10.2139/ssrn.4603237
Rane, N. (2023b). Enhancing Mathematical Capabilities through ChatGPT and Similar Generative Artificial Intelligence: Roles and Challenges in Solving Mathematical Problems (SSRN Scholarly Paper No. 4603237). Social Science Research Network. https://doi.org/10.2139/ssrn.4603237
Reddy, C. K., & Shojaee, P. (2025). Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges. Proceedings of the AAAI Conference on Artificial Intelligence, 39(27), 28601–28609. https://doi.org/10.1609/aaai.v39i27.35084
Shao, H., & Tang, G. (2025). Empowering Mathematical Problem-Posing Pedagogy with Generative AI: A Theoretical Framework and Case Study. 2025 14th International Conference on Educational and Information Technology (ICEIT), 111–115. https://doi.org/10.1109/ICEIT64364.2025.10975950
Shi, W. (2024). Leveraging Generative Artificial Intelligence to Enhance Mathematical Innovation and Practical Abilities of University Students. 1(11).
Song, Y., Kim, J., Liu, Z., Li, C., & Xing, W. (2026). Students’ perceived roles, opportunities, and challenges of a generative AI-powered teachable agent: A case of middle school math class. Journal of Research on Technology in Education, 58(3), 535–554. https://doi.org/10.1080/15391523.2024.2447727
Tassell, J., Novak, E., Little, T., & Feeney, E. (2026). Preparing Elementary Mathematics Teachers for Responsible AI Use: An Exploratory Mixed?Methods Study Using a Short Online Module and Scenario?Based Discussions. 777–789. https://www.learntechlib.org/primary/p/2129703/
Walkington, C. (n.d.). The implications of generative artificial intelligence for mathematics education. School Science and Mathematics, n/a(n/a). https://doi.org/10.1111/ssm.18356
Yang, X., Chen, R., & Chen, Z. (2026). AI-enabled blended teaching in Information Theory and Coding: An outcomes-based mixed-methods approach. Frontiers in Education, 11. https://doi.org/10.3389/feduc.2026.1752893
Zapata-Rivera, D., Torre, I., Lee, C.-S., Sarasa-Cabezuelo, A., Ghergulescu, I., & Libbrecht, P. (2024). Editorial: Generative AI in education. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1532896
Zhu, Q., & Luo, J. (2023). Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering, 23(041003). https://doi.org/10.1115/1.4056220
