AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails

This paper presents MWPTutor, an LLM-based tutoring system that combines GPT-4 with finite state transducers to provide pedagogically-structured tutoring for math word problems, with guardrails to prevent answer leakage and maintain desired teaching strategies. The system is evaluated through human assessment comparing it to unstructured GPT-4 tutoring on two math word problem datasets.

Large Language Models (LLMs) have found several use cases in education, ranging from automatic question generation to essay evaluation. In this paper, we explore the potential of using LLMs to author Intelligent Tutoring Systems. A common pitfall of using LLMs as tutors is their straying from desired pedagogical strategies such as leaking the answer to the student, and in general, providing no guarantees on the validity or appropriateness of the tutor assistance. We argue that while LLMs with ce

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