As the recent Large Language Models(LLM's) become increasingly competent in zero-shot and few-shot reasoning across various domains, educators are showing a growing interest in leveraging these LLM's in conversation-based tutoring systems. However, building a conversation-based personalized tutoring system poses considerable challenges in accurately assessing the student and strategically incorporating the assessment into teaching within the conversation. In this paper, we discuss design considerations for a personalized tutoring system that involves the following two key components: (1) a student modeling with diagnostic components, and (2) a conversation-based tutor utilizing LLM with prompt engineering that incorporates student assessment outcomes and various instructional strategies. Based on these design considerations, we created a proof-of-concept tutoring system focused on personalization and tested it with 20 participants. The results substantiate that our system's framework facilitates personalization, with particular emphasis on the elements constituting student modeling. A web demo of our system is available at http://rlearning-its.com.

翻译：随着近期大型语言模型（LLM）在跨领域的零样本与小样本推理能力日益增强，教育工作者对将其应用于对话式辅导系统的兴趣与日俱增。然而，构建对话式个性化辅导系统在精确评估学生、并将评估结果策略性地融入教学对话中仍面临显著挑战。本文探讨了包含两个关键组件的个性化辅导系统设计考量：（1）包含诊断模块的学生建模；（2）基于LLM并采用提示工程将学生评估结果与多种教学策略相结合的对话式辅导系统。基于这些设计考量，我们构建了一个聚焦个性化学习的概念验证辅导系统，并在20名参与者中开展了测试。研究结果证实，该系统的框架有效支持了个性化学习，其中学生建模的构建要素尤为关键。系统网络演示可通过http://rlearning-its.com访问。