One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction

TL;DR

This paper introduces PACE, a personalized conversational tutoring agent for mathematics that adapts to individual learning styles using the Felder and Silverman model, improving engagement and learning outcomes.

Contribution

The paper presents a novel personalized tutoring system that models student learning styles and employs Socratic methods to enhance mathematics education.

Findings

PACE outperforms existing methods in personalization and motivation

Effective assessment of individual learning styles achieved

Significant improvement in student engagement and understanding

Abstract

Large language models (LLMs) have been increasingly employed in various intelligent educational systems, simulating human tutors to facilitate effective human-machine interaction. However, previous studies often overlook the significance of recognizing and adapting to individual learner characteristics. Such adaptation is crucial for enhancing student engagement and learning efficiency, particularly in mathematics instruction, where diverse learning styles require personalized strategies to promote comprehension and enthusiasm. In this paper, we propose a \textbf{P}erson\textbf{A}lized \textbf{C}onversational tutoring ag\textbf{E}nt (PACE) for mathematics instruction. PACE simulates students' learning styles based on the Felder and Silverman learning style model, aligning with each student's persona. In this way, our PACE can effectively assess the personality of students, allowing to develop individualized teaching strategies that resonate with their unique learning styles. To further enhance students' comprehension, PACE employs the Socratic teaching method to provide instant feedback and encourage deep thinking. By constructing personalized teaching data and training models, PACE demonstrates the ability to identify and adapt to the unique needs of each student, significantly improving the overall learning experience and outcomes. Moreover, we establish multi-aspect evaluation criteria and conduct extensive analysis to assess the performance of personalized teaching. Experimental results demonstrate the superiority of our model in personalizing the educational experience and motivating students compared to existing methods.