Research Evolution of Artificial Intelligence in Mathematics Learning: A Bibliometric Review from 2015 to 2026

Abstract

Background: The recent growth of Artificial Intelligence (AI) has attracted increasing attention in mathematics learning research, yet a comprehensive understanding of its development remains limited.

Objective: This study aims to explore the research evolution of AI in mathematics learning from 2015 to 2026 using a bibliometric approach.

Method: Data were collected from the Scopus database, yielding 169 publications comprising journal articles and conference papers. Analysis was conducted using Bibliometrix and VOSviewer to examine publication trends, leading sources, contributing countries, influential references, keyword cooccurrence, and thematic evolution.

Results: The findings reveal a significant growth in publications, particularly after 2021, reflecting expanding research interest. The United States and China are the most productive contributors, while several recent works demonstrate strong citation impact. Keyword and thematic analyses reveal a focus on the intersection of AI and mathematics education, with emerging topics such as generative AI, chatbots, and personalized learning.

Conclusion: Overall, the research of AI in mathematics learning has shifted from general technological exploration toward more pedagogically oriented applications. These findings suggest that educators and curriculum developers should prioritize AI tools for personalized and effective learning, while researchers are encouraged to focus empirically evaluating their impact in real classroom contexts.