Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems

This paper proposes a framework for simulating students with different personality traits and language abilities to train and evaluate conversational Intelligent Tutoring Systems, with a case study on image description for language learning in primary school students. The work includes multi-aspect validation of how LLM-based tutors adapt scaffolding strategies to different student personalities.

Intelligent Tutoring Systems (ITSs) can provide personalized and self-paced learning experience. The emergence of large language models (LLMs) further enables better human-machine interaction, and facilitates the development of conversational ITSs in various disciplines such as math and language learning. In dialogic teaching, recognizing and adapting to individual characteristics can significantly enhance student engagement and learning efficiency. However, characterizing and simulating student

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