Contextualizing Problems to Student Interests at Scale in Intelligent Tutoring System Using Large Language Models

TL;DR

This paper investigates using GPT-4 to efficiently contextualize educational problems in an intelligent tutoring system, aiming to improve student engagement and learning outcomes at scale.

Contribution

It demonstrates the feasibility of leveraging large language models for scalable problem contextualization while maintaining problem integrity.

Findings

GPT-4 can effectively contextualize problems without altering difficulty

Iterative prompt engineering preserves problem intent and values

Limitations exist with geometry problems, requiring further research

Abstract

Contextualizing problems to align with student interests can significantly improve learning outcomes. However, this task often presents scalability challenges due to resource and time constraints. Recent advancements in Large Language Models (LLMs) like GPT-4 offer potential solutions to these issues. This study explores the ability of GPT-4 in the contextualization of problems within CTAT, an intelligent tutoring system, aiming to increase student engagement and enhance learning outcomes. Through iterative prompt engineering, we achieved meaningful contextualization that preserved the difficulty and original intent of the problem, thereby not altering values or overcomplicating the questions. While our research highlights the potential of LLMs in educational settings, we acknowledge current limitations, particularly with geometry problems, and emphasize the need for ongoing evaluation and research. Future work includes systematic studies to measure the impact of this tool on students' learning outcomes and enhancements to handle a broader range of problems.