Intelligent teaching design assistant for primary mathematics: A large language model-driven framework with retrieval-augmented generation and problem-chain pedagogy
Danna Tang 1 , Ran Ding 2 3 * , Meng He 4 , Yushen Wang 5 , Kaka Cheng 6
More Detail
1 School of Mechanical Engineering, Suzhou University of Technology, CHINA2 School of Educational Science, Anhui Normal University, CHINA3 Xinhua Middle School, Gedian Economic & Technological Development Zone, Ezhou, CHINA4 Academy of Arts & Design, Tsinghua University, CHINA5 School of Engineering and Materials Science, Queen Mary University of London, UK6 Department of Mechanical Engineering, Imperial College London, UK* Corresponding Author

Abstract

Primary mathematics education faces systemic challenges in translating curriculum reforms into classroom practice, exacerbated by teachers’ cognitive overload and limited support for pedagogical innovation. This study develops an Intelligent Teaching Design Assistant grounded in socio-constructivist and cognitive load theories to address these challenges. Thirty-four primary mathematics teachers participated in a quasi-experimental study. The Intelligent Teaching Design Assistant integrates Large Language Models with multi-dimensional knowledge bases (curriculum standards, teaching strategies, student profiles) and a multi-agent architecture (process planner, student simulator). The Intelligent Teaching Design Assistant significantly outperformed generic Large Language Models, improving overall lesson plan quality. This work pioneers a replicable pathway for AI to empower teacher agency and advance 21st-century educational transformation.

License

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: Research Article

INT ELECT J MATH ED, Volume 21, Issue 1, February 2026, Article No: em0862

https://doi.org/10.29333/iejme/17447

Publication date: 01 Jan 2026

Online publication date: 19 Nov 2025

Article Views: 9

Article Downloads: 6

Open Access References How to cite this article