Agent4CD: Generative Agent for Cognitive Diagnosis in Intelligent Education

Cognitive diagnosis (CD) is a central assessment approach in intelligent education that aims to uncover learners' knowledge mastery and latent cognitive abilities under examination conditions. However, existing models primarily infer learners' knowledge state based on their final performance, thus failing to capture the reasoning trajectories during problem-solving and struggling to address the dissociation between ability and performance caused by short-term affective fluctuations. To address these challenges, we propose a generative agent for CD (Agent4CD) in intelligent education that enhances interpretability and provides greater personalization and adaptability. Specifically, the framework constructs learner profiles and incorporates a memory module to integrate historical responses. It leverages the reasoning capabilities of large language models (LLMs) to dynamically model cognitive paths and knowledge evolution. The affect perception module utilizes LLMs' contextual understanding to identify students' transient affective state, helping to mitigate misjudgments of ability caused by affective fluctuations. Meanwhile, a dual-reflection mechanism supports both real-time intervention and long-term ability development. Experiments demonstrate that Agent4CD outperforms existing methods in diagnostic accuracy, interpretability, and personalization. To facilitate further research, data and code are publicly available at https://github.com/xinjiesun-ustc/Agent4CD.