The evolving landscape of AI integration in mathematics education: A systematic review of trends (2015-2025)
Da Tien Nguyen 1 * , Quy Van Pham 2
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1 Hanoi Metropolitan University, Hanoi, VIETNAM2 Hung Vuong High School, Dong Nai, VIETNAM* Corresponding Author

Abstract

Over the past 10 years (2015-2025), the rapid development of artificial intelligence (AI) has brought educational technology to a new level, especially in the field of mathematics education. However, although there are many individual studies on specific applications of AI, a systematic and comprehensive overview of the main trends is still lacking. To fill this gap, this study conducts a systematic review aimed at exploring the development landscape of AI integration in mathematics education from 2015 to 2025. The study focuses on answering four main questions: (1) What is the level of research contribution on AI in mathematics education across regions and countries in the world? (2) What are the emerging trends in AI integration in mathematics education from 2015 to 2025? (3) Which AI tools are most commonly used in mathematics education? (4) How is the use of AI tools reflected at different educational levels? This study uses a systematic review approach to analyze academic literature published during the period 2015-2025. The results will provide a detailed map of technological trends, pedagogical models, and main application areas of AI in mathematics education at different levels. At the same time, the study also identifies common challenges and potential areas for future research. The results of this study will provide a valuable reference for educational policy makers, technology developers, teachers, and researchers, helping them make more informed decisions in exploiting the potential of AI to improve the quality of mathematics education.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Review Article

EURASIA J Math Sci Tech Ed, Volume 21, Issue 10, 2025, Article No: em2714

https://doi.org/10.29333/ejmste/17078

Publication date: 01 Oct 2025

Online publication date: 17 Sep 2025

Article Views: 22

Article Downloads: 10

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