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

This paper presents MWPTutor, an LLM-based intelligent tutoring system for math word problems that combines structured pedagogical strategies (finite state transducers) with LLM flexibility, and evaluates it against GPT-4 through human evaluation studies. The system implements guardrails to prevent common tutoring pitfalls like answer-leaking while maintaining pedagogical control through predefined teaching strategies.

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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