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Jennifer Dela Torre, Higher Colleges of Technology, United Arab EmiratesJero Sayco, Higher Colleges of Technology, United Arab Emirates
Abstract
The emergence of generative artificial intelligence (GenAI), in particular large language models (LLMs), is reshaping mathematics education by offering new forms of tutoring, feedback, problem generation, and personalized learning. This systematic review, following the PRISMA methodology, synthesizes empirical studies published from 2020 to 2025 that examine the application of GenAI in mathematics learning at secondary and tertiary levels. From an initial yield of 68 records across multiple databases, 32 were retained after screening and quality assessment, with 22 meeting criteria for detailed synthesis. The review finds that GenAI tools can significantly support mathematical problem solving, provide timely feedback, adapt instruction to learner needs, and increase accessibility. However, issues of output correctness, over-reliance, reduced deep reasoning, academic integrity, and limited long-term evidence remain. The paper concludes with recommendations for pedagogical integration, teacher training, assessment redesign, and future research directions.
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Conference: ACEID2026Stream: Innovation & Technology
This paper is part of the ACEID2026 Conference Proceedings (View)
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To cite this article:
Torre J., & Sayco J. (2026) Generative Artificial Intelligence in Mathematics Education: A Systematic Literature Review Using the PRISMA Framework ISSN: 2189-101X – The Asian Conference on Education & International Development 2026 Official Conference Proceedings (pp. 591-606) https://doi.org/10.22492/issn.2189-101X.2026.48
To link to this article: https://doi.org/10.22492/issn.2189-101X.2026.48
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