The Collaborative Intelligent Mathematics Tutoring Agent Platform

This paper describes the development of a personalized intelligent tutoring agent (ITA) designed to improve the mathematics homework experience for students by catering to individual learning needs, providing instant access to explanatory videos, and conducting assessments that mimic human interaction. This paper explores how large language models can be integrated within an intelligent agent to solve mathematical problems and enhance mathematical reasoning, which can transform traditional approaches in mathematics education. Large-scale language models have the potential to improve personalised learning and problem-solving through AI-enabled tutoring agents. The system consists of two main components: homework process assistance and personalized homework tutoring. The effectiveness of this agent-based system was validated through rigorous testing, demonstrating its potential to significantly improve the quality and efficiency of mathematics education.

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Publication Details

Published
2024-12-04
DOI
https://doi.org/10.1109/ica63002.2024.00041
Citations
1
Primary Topic
Advanced Data Processing Techniques
Type
article
Field-Weighted Citation Impact
0.33
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article

The Collaborative Intelligent Mathematics Tutoring Agent Platform

Jun Shen, Qian Zhao, Fei Xie, Meng Hao et al.
1 citations
Advanced Data Processing Techniques
0.33
article

The Collaborative Intelligent Mathematics Tutoring Agent Platform

Jun Shen, Qian Zhao, Fei Xie, Meng Hao, Xiaosong Wang, Yi Luo
article en
1 citations

Abstract

This paper describes the development of a personalized intelligent tutoring agent (ITA) designed to improve the mathematics homework experience for students by catering to individual learning needs, providing instant access to explanatory videos, and conducting assessments that mimic human interaction. This paper explores how large language models can be integrated within an intelligent agent to solve mathematical problems and enhance mathematical reasoning, which can transform traditional approaches in mathematics education. Large-scale language models have the potential to improve personalised learning and problem-solving through AI-enabled tutoring agents. The system consists of two main components: homework process assistance and personalized homework tutoring. The effectiveness of this agent-based system was validated through rigorous testing, demonstrating its potential to significantly improve the quality and efficiency of mathematics education.

Shenyang University of Technology (CN), University of Wollongong (AU), Shandong Institute of Business and Technology (CN)
Quality Education
Openalex Percentile: Top 38%
Advanced Data Processing Techniques
0.33
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The Collaborative Intelligent Mathematics Tutoring Agent Platform — Jun Shen, Qian Zhao, et al. · (2024) | TGRS Research Map | TGRS