  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.
