Research on a Mathematics Thinking Visualization Teaching Model Supported by Generative AI

DOI

10.2991/978-2-38476-611-6_35How to use a DOI?

Keywords

Generative AI; Mathematics Thinking Visualization; Personalized Learning; Teaching Model; Intelligent Education Reform

Abstract

Traditional teacher-led math teaching struggles with personalized learning and abstract concepts. Generative AI offers strong potential for personalization, real-time feedback, and problem generation. This study deeply integrates generative AI with mathematics thinking visualization to dynamically present abstract thought processes, while using AI to analyze student data and generate personalized learning paths. This model supports teaching according to aptitude and provides a replicable approach for integrating generative AI with math thinking visualization, advancing math education reform in the intelligent era.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 3.0 International License (http://creativecommons.org/licenses/by-nc/3.0/), 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  - Jiaxin Xu
PY  - 2026
DA  - 2026/09/07
TI  - Research on a Mathematics Thinking Visualization Teaching Model Supported by Generative AI
BT  - Proceedings of the 2026 12th International Conference on Digital Humanities and Frontiers in Social Sciences (DHFSS 2026)
PB  - Atlantis Press
SP  - 313
EP  - 321
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-611-6_35
DO  - 10.2991/978-2-38476-611-6_35
ID  - Xu2026
ER  -