Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling
<|reference_start|>Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling: 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.<|reference_end|>
@article{park2024empowering,
    title={Empowering Personalized Learning through a Conversation-based Tutoring
  System with Student Modeling},
    author={Minju Park, Sojung Kim, Seunghyun Lee, Soonwoo Kwon, Kyuseok Kim},
    journal={arXiv preprint arXiv:2403.14071},
    year={2024},
    doi={10.1145/3613905.3651122},
    archivePrefix={arXiv},
    eprint={2403.14071},
    primaryClass={cs.HC}
}