Visualizing Research Trends in AI-Powered Mathematics Education: Insights from VOS Viewer Mapping

DOI

10.2991/978-94-6239-644-9_16How to use a DOI?

Keywords

AI-powered mathematics education; VOS viewer; math anxiety; personalized learning

Abstract

Purpose:

This study aims to systematically map and analyse global research trends in AI-powered mathematics education, focusing on the adoption, effectiveness, and challenges of AI-driven tools in enhancing the teaching and learning of mathematics across diverse educational contexts.

Methodology:

A bibliometric review and visualization approach was employed. Relevant literature was sourced from major international meta databases, including Scopus, Web of Science (WoS), and IEEE Xplore. The search targeted article titles, keywords, and abstracts published between 2018 and 2025. VOS viewer was utilized to construct bibliometric maps identifying key themes, technological advancements, and collaborative research networks within the field.

Results:

Analysis of 120 peer-reviewed articles revealed predominant clusters in areas such as machine learning applications, adaptive learning platforms, and AI-driven assessment tools. Research output and collaborations are primarily concentrated in North America, Europe, and Asia. Among emerging themes, the use of AI to address math anxiety especially through culturally relevant, personalized interventions was identified as a significant but underexplored area, particularly in developing regions.

Conclusions:

This review highlights critical research gaps, emerging challenges, and future opportunities for scalable, ethical, and culturally sensitive AI solutions in mathematics education and personalized learning. The findings provide a valuable benchmark for researchers and policymakers seeking to maximize the educational impact of AI while addressing issues such as math anxiety in diverse learning environments.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Parum Sarda
AU  - Shweta Loonkar
AU  - Karishma Desai
PY  - 2026
DA  - 2026/04/19
TI  - Visualizing Research Trends in AI-Powered Mathematics Education: Insights from VOS Viewer Mapping
BT  - Proceedings of the Global Innovation and Technology Summit “AAROHAN 3.0”_Engineering track (GITS-EAS 2025)
PB  - Atlantis Press
SP  - 187
EP  - 198
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-644-9_16
DO  - 10.2991/978-94-6239-644-9_16
ID  - Sarda2026
ER  -