Learning diagnosis is a critical task that monitors students' cognitive state during educational activities, with the goal of enhancing learning outcomes. With advancements in language models (LMs), many AI-driven educational studies have shifted towards conversational learning scenarios, where students engage in multi-turn interactive dialogues with tutors. However, conversational learning diagnosis remains underdeveloped, and most existing techniques acquire students' cognitive state through intuitive instructional prompts on LMs to analyze the dialogue text. This direct prompting approach lacks a solid psychological foundation and fails to ensure the reliability of the generated analytical text. In this study, we introduce ParLD, a preview-analyze-reason framework for conversational learning diagnosis, which leverages multi-agent collaboration to diagnose students' cognitive state over multiple dialogue turns. Specifically, ParLD comprises three main components: (1) Behavior Previewer, which generates a student behavior schema based on previous states and learning content; (2) State Analyzer, which diagnoses the tutor-student dialogue and behavior schema to update the cognitive state; and (3) Performance Reasoner, which predicts the student's future responses and provides verifiable feedback to support ParLD's self-reflection with the Chain Reflector. They operate sequentially and iteratively during each interaction turn to diagnose the student's cognitive state. We conduct experiments to evaluate both performance prediction and tutoring support, emphasizing the effectiveness of ParLD in providing reliable and insightful learning diagnosis.

翻译：学习诊断是一项关键任务，旨在监测学生在教育活动中的认知状态，以提升学习成效。随着语言模型（LMs）的发展，许多人工智能驱动的教育研究已转向对话式学习场景，即学生与导师进行多轮交互式对话。然而，对话式学习诊断领域仍不成熟，现有技术大多通过语言模型上的直观教学提示来分析对话文本，从而获取学生的认知状态。这种直接提示方法缺乏坚实的心理学基础，无法确保生成分析文本的可靠性。本研究提出ParLD，一种用于对话式学习诊断的预览-分析-推理框架，该框架利用多智能体协作来诊断学生在多轮对话中的认知状态。具体而言，ParLD包含三个主要组件：（1）行为预览器，基于先前状态和学习内容生成学生行为模式；（2）状态分析器，通过分析师生对话和行为模式来更新认知状态；（3）表现推理器，预测学生未来的回答并提供可验证的反馈，通过链式反思器支持ParLD的自我反思。这些组件在每次交互轮次中顺序且迭代地运行，以诊断学生的认知状态。我们通过实验评估了其在表现预测和辅导支持两方面的性能，重点验证了ParLD在提供可靠且具有洞察力的学习诊断方面的有效性。