  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 Large Language Models (LLMs) to author
Intelligent Tutoring Systems. A common pitfall of LLMs is their straying from
desired pedagogical strategies such as leaking the answer to the student, and
in general, providing no guarantees. We posit that while LLMs with certain
guardrails can take the place of subject experts, the overall pedagogical
design still needs to be handcrafted for the best learning results. Based on
this principle, we create a sample end-to-end tutoring system named MWPTutor,
which uses LLMs to fill in the state space of a pre-defined finite state
transducer. This approach retains the structure and the pedagogy of traditional
tutoring systems that has been developed over the years by learning scientists
but brings in additional flexibility of LLM-based approaches. Through a human
evaluation study on two datasets based on math word problems, we show that our
hybrid approach achieves a better overall tutoring score than an instructed,
but otherwise free-form, GPT-4. MWPTutor is completely modular and opens up the
scope for the community to improve its performance by improving individual
modules or using different teaching strategies that it can follow.
