面向对话式智能辅导系统的个性化感知学生模拟
Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems
AI总结：
本文提出一个整合认知与非认知特征的框架，利用LLMs在语言学习场景中进行个性化感知的学生模拟，实验证明其能生成多样化学生反应并触发教师自适应策略。
摘要：
智能辅导系统（ITSs）能提供个性化、自定进度的学习体验。大型语言模型（LLMs）的出现进一步改善了人机交互，并促进了数学和语言学习等学科中对话式ITSs的发展。在对话式教学中，识别并适应个体特征能显著提升学生参与度和学习效率。然而，在训练和评估对话式ITSs时，刻画和模拟学生画像仍具挑战。在本研究中，我们提出一个框架，通过提炼和整合认知与非认知方面来构建不同学生群体的画像，并在语言学习场景中利用LLMs进行个性化感知的学生模拟。我们进一步通过多方面验证增强该框架，并从教师和学生视角进行了广泛分析。实验结果表明，最先进的LLMs能根据给定的语言能力和个性特征产生多样化的学生反应，并触发教师的自适应支架策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
