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Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling (2403.14071v1)

Published 21 Mar 2024 in cs.HC

Abstract: As the recent LLMs(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.

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Authors (5)
  1. Minju Park (5 papers)
  2. Sojung Kim (7 papers)
  3. Seunghyun Lee (60 papers)
  4. Soonwoo Kwon (17 papers)
  5. Kyuseok Kim (4 papers)
Citations (7)
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