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.

翻译：将问题情境化以匹配学生兴趣能够显著提升学习效果。然而，由于资源和时间限制，这一任务常面临规模化挑战。近年来，GPT-4等大型语言模型的进步为这些问题提供了潜在解决方案。本研究探索了GPT-4在智能辅导系统CTAT中对问题进行情境化的能力，旨在提高学生参与度并增强学习成效。通过迭代提示工程，我们实现了有意义的语境化处理，既保留了问题的难度和原始意图，也未改变数值或过度复杂化题目。尽管我们的研究突显了大型语言模型在教育领域的潜力，但我们承认当前存在的局限性（尤其在几何问题方面），并强调持续评估与研究的必要性。未来工作包括开展系统性研究以评估该工具对学生学习成果的影响，以及通过功能增强以支持更广泛的问题类型。