MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education

• URL: http://arxiv.org/abs/2404.06711v1
• Date: Wed, 10 Apr 2024 03:35:51 GMT
• Title: MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education
• Authors: Murong Yue, Wijdane Mifdal, Yixuan Zhang, Jennifer Suh, Ziyu Yao,
• Abstract summary: Large language models (LLMs) have recently demonstrated strong capability in both modeling mathematical problems and simulating characters. We present MATHVC, the very first LLM-powered virtual classroom containing multiple LLM-simulated student characters. We propose three innovations: integrating MM domain knowledge into the simulation, defining a symbolic schema as the ground for character simulation, and designing a meta planner at the platform level to drive the conversational procedure.
• Score: 19.549398447035376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mathematical modeling (MM) is considered a fundamental skill for students in STEM disciplines. Practicing the MM skill is often the most effective when students can engage in group discussion and collaborative problem-solving. However, due to unevenly distributed teachers and educational resources needed to monitor such group activities, students do not always receive equal opportunities for this practice. Excitingly, large language models (LLMs) have recently demonstrated strong capability in both modeling mathematical problems and simulating characters with different traits and properties. Drawing inspiration from the advancement of LLMs, in this work, we present MATHVC, the very first LLM-powered virtual classroom containing multiple LLM-simulated student characters, with whom a human student can practice their MM skill. To encourage each LLM character's behaviors to be aligned with their specified math-relevant properties (termed "characteristics alignment") and the overall conversational procedure to be close to an authentic student MM discussion (termed "conversational procedural alignment"), we proposed three innovations: integrating MM domain knowledge into the simulation, defining a symbolic schema as the ground for character simulation, and designing a meta planner at the platform level to drive the conversational procedure. Through experiments and ablation studies, we confirmed the effectiveness of our simulation approach and showed the promise for MATHVC to benefit real-life students in the future.

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