Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Ben Liu, Jihan Zhang, Fangquan Lin, Xu Jia, Min Peng

TL;DR

<3-5 sentence high-level summary>PACE addresses the limitation of one-size-fits-all math tutoring by simulating student learning styles through the Felder-Silverman model and guiding Socratic conversations. It builds a dedicated personalized teaching framework (PACE) and a dataset of 1,410 dialogue exchanges via LLM role-play, trained with LoRA on $LLaMA2$-7B-chat. The paper introduces multi-aspect evaluation (reference-based and GPT-4 based) and shows that PACE outperforms baselines, including EduChat, in personalization, engagement, and motivation, with strong generalization to unseen personas. This work demonstrates a practical path to scalable, persona-aware math tutoring with potential to enhance student engagement and learning outcomes.

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.

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

TL;DR

<3-5 sentence high-level summary>PACE addresses the limitation of one-size-fits-all math tutoring by simulating student learning styles through the Felder-Silverman model and guiding Socratic conversations. It builds a dedicated personalized teaching framework (PACE) and a dataset of 1,410 dialogue exchanges via LLM role-play, trained with LoRA on -7B-chat. The paper introduces multi-aspect evaluation (reference-based and GPT-4 based) and shows that PACE outperforms baselines, including EduChat, in personalization, engagement, and motivation, with strong generalization to unseen personas. This work demonstrates a practical path to scalable, persona-aware math tutoring with potential to enhance student engagement and learning outcomes.

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.

Paper Structure

This paper contains 26 sections, 5 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Intelligent Tutoring Systems
  4. AI in Education
  5. Methods
  6. Personality-aware Tutoring Agent
  7. Simulating Learning Styles
  8. Conceptualizing Personalized Teaching Strategies
  9. Socratic-style Conversations
  10. Personalized Teaching Conversation Construction
  11. Raw Data Collection
  12. Dialogue Synthesis via LLMs
  13. Human Annotation
  14. PACE Training
  15. Multi-aspect Evaluation Criteria
  16. ...and 11 more sections

Figures (5)

  • Figure 1: Comparison of personalized teaching with existing systems.
  • Figure 2: An example of how PACE communicates with a student and corresponding prompts both sides to generate expected dialogues as training data.
  • Figure 3: The overall design framework of our PACE.
  • Figure 4: Multi-aspect assessment comparisons using GPT-4. Higher values indicate better performance.
  • Figure 5: Evaluation of different models on unseen student personas across defined criteria.