  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.
