Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems

TL;DR

This paper presents a framework using large language models to simulate diverse student personas in conversational intelligent tutoring systems, enhancing personalization and adaptive teaching strategies.

Contribution

It introduces a novel method to construct and validate student profiles combining cognitive and noncognitive traits for improved simulation in language learning.

Findings

LLMs generate diverse student responses based on personality traits.

The framework enables adaptive scaffolding strategies by teachers.

Enhanced validation ensures realistic student behavior simulation.

Abstract

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.