GENERATIVE AI IN MATHEMATICS EDUCATION: FROM LEARNING COMPANION TO COGNITIVE SHORTCUT

Rival Muhamad Fauzi, Angga Nurohman, Dicky Zaini Abdullah Al As'ari

Abstract


The emergence of generative artificial intelligence (GenAI) presents new opportunities in mathematics education through interactive support, feedback, scaffolding, and problem solving, while also creating risks when AI becomes a shortcut that replaces students’ thinking processes. This study aims to map research developments on GenAI in mathematics education, identify opportunities and challenges associated with its use, and formulate directions for future research. A Systematic Literature Review (SLR) complemented by bibliometric analysis and thematic synthesis was conducted. Literature was searched through Scopus for publications from 2016 to 2025. Of 105 identified records, 45 studies met the inclusion criteria and were substantively analyzed following the PRISMA 2020 procedure. Bibliometric analysis using VOSviewer and thematic synthesis identified three major themes: AI as Learning Companion, AI as Cognitive Shortcut, and Future Research Directions. The findings reveal a shift from technology-oriented research toward pedagogical integration involving learning, teacher support, problem solving, and student–AI interaction. GenAI can strengthen learning when it supports students’ thinking processes but may undermine independence when it substitutes for mathematical reasoning. These findings highlight the importance of pedagogical design, student agency, AI literacy, teacher roles, and assessment practices that preserve students’ cognitive engagement.

Full Text:

PDF

References


Adams, S. S., Arel, I., Bach, J., Coop, R., Furlan, R., Goertzel, B., Hall, J., Samsonovich, A. V., Scheutz, M., Schlesinger, M., Shapiro, S. C., & Sowa, J. F. (2012). Mapping the landscape of human-level artificial general intelligence. AI Magazine, 33(1), 25–41. https://doi.org/10.1609/aimag.v33i1.2322

Al Sharab, J. F., Matuk, A. S., Ali, M., & Chen, X. (2024). WIP: Exploring mathematical proficiency in AI-driven engineering environments: A survey-based study of current workforce needs. Proceedings - Frontiers in Education Conference, FIE. https://doi.org/10.1109/FIE61694.2024.10893062

Almoubayyed, H., Bastoni, R., Berman, S. R., Galasso, S., Jensen, M., Lester, L., Murphy, A., Swartz, M., Weldon, K., Fancsali, S. E., Gropen, J., & Ritter, S. (2023). Rewriting math word problems to improve learning outcomes for emerging readers: A randomized field trial in Carnegie Learning's MATHia. Communications in Computer and Information Science, 1831 CCIS, 200–205. https://doi.org/10.1007/978-3-031-36336-8_30

Anton, J., Cosentino, G., Sharma, K., Gelsomini, M., Mok, M., Giannakos, M., & Abrahamson, D. (2025). The human condition: Modal and interactive advantages of teacher over AI feedback on children's mathematical performance. Proceedings - 24th Annual ACM Interaction Design and Children Conference, IDC 2025, 183–203. https://doi.org/10.1145/3713043.3728863

Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122

del Olmo-Muñoz, J., González-Calero, J. A., Diago, P. D., Arnau, D., & Arevalillo-Herráez, M. (2023). Intelligent tutoring systems for word problem solving in COVID-19 days: Could they have been (part of) the solution? ZDM–Mathematics Education, 55(1), 35–48. https://doi.org/10.1007/s11858-022-01396-w

Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224

Dilling, F., & Herrmann, M. (2024). Using large language models to support pre-service teachers mathematical reasoning—an exploratory study on ChatGPT as an instrument for creating mathematical proofs in geometry. Frontiers in Artificial Intelligence, 7, 1460337. https://doi.org/10.3389/frai.2024.1460337

Drushlyak, M., Lukashova, T., Sabadosh, Y., Melnikov, I., & Semenikhina, O. (2024). Using ChatGPT for the development of critical thinking in youth: Example of inequality proof. 2024 47th ICT and Electronics Convention (MIPRO) Proceedings, 334–339. https://doi.org/10.1109/MIPRO60963.2024.10569759

Li, D., Osman, S., Alhassora, N. S. A., Kumar, J. A., & Husain, S. K. S. (2025). Empowering K-12 mathematics teachers with artificial intelligence: A systematic review of insights, applications, and challenges. IEEE Access, 13, 117198–117209. https://doi.org/10.1109/ACCESS.2025.3585784

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

Malik, R., Abdi, D., Wang, R., & Demszky, D. (2025). Scaffolding middle school mathematics curricula with large language models. British Journal of Educational Technology, 56(3), 999–1027. https://doi.org/10.1111/bjet.13571

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Pepin, B., Buchholtz, N., & Salinas-Hernández, U. (2025). A scoping survey of ChatGPT in mathematics education. Digital Experiences in Mathematics Education, 11, 9–41. https://doi.org/10.1007/s40751-025-00172-1

Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, 45. https://doi.org/10.1186/1471-2288-8-45

Thuy, P. T. D., Van, N. T., Trang, N. H., Thuong, N. M., Hien, V. T. T., & Chung, V. Q. (2025). Designing an AI-supported formative assessment model for pre-service mathematics teacher self-study in Vietnam. International Journal of Interactive Mobile Technologies, 19(22), 50–68. https://doi.org/10.3991/ijim.v19i22.57723

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523–538. https://doi.org/10.1007/s11192-009-0146-3

Walkington, C. (2025). The implications of generative artificial intelligence for mathematics education. School Science and Mathematics, 1–10. https://doi.org/10.1111/ssm.18356

Wang, K. D., Wu, Z., Tufts, L., Wieman, C., Salehi, S., & Haber, N. (2025). Scaffold or crutch? Examining college students’ use and views of generative AI tools for STEM education. 2025 IEEE Global Engineering Education Conference (EDUCON), 1–10. https://doi.org/10.1109/EDUCON62633.2025.11016406

Wardat, Y., Tashtoush, M. A., AlAli, R., & Saleh, S. (2024). Artificial intelligence in education: Mathematics teachers' perspectives, practices and challenges. Iraqi Journal for Computer Science and Mathematics, 5(1), 60–77. https://doi.org/10.52866/ijcsm.2024.05.01.004

Xing, W., Kim, T., Song, Y., Li, H., Li, C., & Kim, J. (2026). Unveiling interaction patterns between students and generative AI teachable agent: Focusing on students' agency and AI agents' authority. British Journal of Educational Technology. https://doi.org/10.1111/bjet.70038

Xing, W., Song, Y., Li, C., Liu, Z., Zhu, W., & Oh, H. (2025). Development of a generative AI-powered teachable agent for middle school mathematics learning: A design-based research study. British Journal of Educational Technology, 56, 2043–2077. https://doi.org/10.1111/bjet.13586

Yoon, H., Hwang, J., Lee, K., Roh, K. H., & Kwon, O. N. (2024). Students’ use of generative artificial intelligence for proving mathematical statements. ZDM–Mathematics Education, 56(7), 1531–1551. https://doi.org/10.1007/s11858-024-01629-0

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, 28. https://doi.org/10.1186/s40561-024-00316-7

Zhang, D., Wijaya, T. T., Wang, Y., Su, M., Li, X., et al. (2025). Exploring the relationship between AI literacy, AI trust, AI dependency, and 21st century skills in preservice mathematics teachers. Scientific Reports, 15, 14281. https://doi.org/10.1038/s41598-025-99127-0

Zhuang, Y., & Zhang, S. (2025). Integrating ChatGPT in mathematics teacher education: AI-based simulation role-playing to support practice-based teaching. International Journal of Artificial Intelligence in Education, 35(6), 3873–3895. https://doi.org/10.1007/s40593-025-00519-0




DOI: https://doi.org/10.37058/jarme.v8i2.18242

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Jurnal of Authentic Research on Mathematics Education (JARME)
Mater's Program in Mathematics Education, Universitas Siliwangi
Jl. Siliwangi no. 24 Kota Tasikmalaya - 46115
email: [email protected]
e-ISSN: 2655-7762
Licensed under�Lisensi Creative Commons Attribution 4.0 International
StatCounter: Detail
Analisis Web Menjadi Mudah - StatCounter

Indexed by�:

Moraref Satu Pencarian BASE - Mesin Pencari Akademik Bielefeld Jurnal Terindeks DRJI