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.

中文翻译：

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对话式智能辅导系统的个性感知学生模拟

智能辅导系统（ITS）可以提供个性化和自定进度的学习体验。大语言模型（LLM）的出现进一步实现了更好的人机交互，并促进了数学和语言学习等各个学科中会话式ITS的发展。在对话教学中，认识和适应个体特征可以显着提高学生的参与度和学习效率。然而，在训练和评估会话式智能交通系统中，表征和模拟学生的性格仍然具有挑战性。在这项工作中，我们提出了一个框架，通过完善和整合认知和非认知方面来构建不同学生群体的概况，并利用法学硕士在语言学习场景中进行人格意识的学生模拟。我们通过多方面验证进一步增强了框架，并从教师和学生的角度进行了广泛的分析。我们的实验结果表明，最先进的法学硕士可以根据给定的语言能力和个性特征产生不同的学生反应，并触发教师的适应性脚手架策略。