Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes

This paper develops Bridge, a method that translates expert tutors' decision-making processes into a framework for LLMs to remediate elementary math mistakes, using a dataset of 700 real tutoring conversations with 1st-5th grade students. The work evaluates GPT-4 and Llama-2-70b on their ability to provide pedagogically sound responses to student errors when guided by expert decision models.

Scaling high-quality tutoring remains a major challenge in education. Due to growing demand, many platforms employ novice tutors who, unlike experienced educators, struggle to address student mistakes and thus fail to seize prime learning opportunities. Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap in remediating math mistakes. We contribute Bridge, a method that uses cognitive task analysis to translate an expert’s latent thought proces

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