Interactive LLM-based Tutoring of Math and Physics Students
Session Number
CMPS(ai) 13
Advisor(s)
Anderson Trimm, Illinois Mathematics and Science Academy
Discipline
Computer Science
Start Date
17-4-2025 10:15 AM
End Date
17-4-2025 10:30 AM
Abstract
Large language models (LLMs) show promise for educational applications but frequently hallucinate incorrect answers, making them unreliable for tutoring. Even on well-known problems, LLMs often produce factually incorrect responses, misinterpret prompts, or fail to follow instructions. We address this issue with a chain-of-thought-based grounding method, requiring the model to generate and internally verify a structured reasoning process before interacting with students. We implement this approach in CᴏTᴜᴛᴏʀ, a tutoring system designed to provide mathematically rigorous, self-verified explanations.
Interactive LLM-based Tutoring of Math and Physics Students
Large language models (LLMs) show promise for educational applications but frequently hallucinate incorrect answers, making them unreliable for tutoring. Even on well-known problems, LLMs often produce factually incorrect responses, misinterpret prompts, or fail to follow instructions. We address this issue with a chain-of-thought-based grounding method, requiring the model to generate and internally verify a structured reasoning process before interacting with students. We implement this approach in CᴏTᴜᴛᴏʀ, a tutoring system designed to provide mathematically rigorous, self-verified explanations.