  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's persona remain challenging in training
and evaluating conversational ITSs. In this work, we propose a framework to
construct profiles of different student groups by refining and integrating both
cognitive and noncognitive aspects, and leverage LLMs for personality-aware
student simulation in a language learning scenario. We further enhance the
framework with multi-aspect validation, and conduct extensive analysis from
both teacher and student perspectives. Our experimental results show that
state-of-the-art LLMs can produce diverse student responses according to the
given language ability and personality traits, and trigger teacher's adaptive
scaffolding strategies.
