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

This paper explores using GPT-4 to automatically contextualize math problems in CTAT (an intelligent tutoring system) to align with individual student interests at scale, aiming to increase engagement and learning outcomes. The authors use iterative prompt engineering to personalize problem contexts while preserving difficulty and pedagogical intent.

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. Throu

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