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Scaling high-quality tutoring remains a major challenge in education. Due togrowing demand, many platforms employ novice tutors who, unlike experiencededucators, struggle to address student mistakes and thus fail to seize primelearning opportunities. Our work explores the potential of large languagemodels (LLMs) to close the novice-expert knowledge gap in remediating mathmistakes. We contribute Bridge, a method that uses cognitive task analysis totranslate an expert's latent thought process into a decision-making model forremediation. This involves an expert identifying (A) the student's error, (B) aremediation strategy, and (C) their intention before generating a response. Weconstruct a dataset of 700 real tutoring conversations, annotated by expertswith their decisions. We evaluate state-of-the-art LLMs on our dataset and findthat the expert's decision-making model is critical for LLMs to close the gap:responses from GPT4 with expert decisions (e.g., "simplify the problem") are+76% more preferred than without. Additionally, context-sensitive decisions arecritical to closing pedagogical gaps: random decisions decrease GPT4's responsequality by -97% than expert decisions. Our work shows the potential ofembedding expert thought processes in LLM generations to enhance theircapability to bridge novice-expert knowledge gaps. Our dataset and code can befound at: \url{https://github.com/rosewang2008/bridge}.

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