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

This paper presents Bridge, a method that translates expert math educators' decision-making processes into a model for LLMs to remediate student math mistakes. The authors construct and publish a dataset of 700 real K-12 tutoring conversations (grades 1-5) annotated with expert decisions and conduct human evaluations comparing expert, novice, and LLM responses to student errors.

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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