Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems
This work addresses the problem of personalizing conversational tutoring systems for enhanced student engagement and learning efficiency, though it is incremental as it builds on existing LLM capabilities.
The authors tackled the challenge of simulating diverse student personas in conversational intelligent tutoring systems by proposing a framework that integrates cognitive and noncognitive profiles, leveraging large language models in a language learning scenario. Their results show that state-of-the-art LLMs can generate varied student responses based on language ability and personality traits, prompting adaptive teaching strategies.
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.