Multi-Agent Debate based Concept Augmentation for Enhanced Cognitive Diagnosis

Cognitive Diagnosis (CD) models are constrained by the data quality of students' response logs. Recent advancements in Large Language Model (LLM) based data augmentation show promise for enhancing CD. However, ensuring the reliability and accuracy of LLM-generated annotations remains a significant challenge. In this paper, we propose Multi-Agent based Concept Augmentation for Cognitive Diagnosis (MACA-CD), a novel approach that enhances CD by generating and fusing reliable concept descriptions and relations based solely on concept names. MACA-CD consists of two main components: (1) a Multi-Agent Debate (MAD) based concept augmentation process that generates diverse and reliable concept descriptions and relations, reducing reliance on behavioral data. For concept descriptions, two agents generate outputs that include definitions, core features, and real-world applications, and continue debating until a judge agent determines that consensus has been reached. Concept relations are then identified using a Breadth-First Search approach to efficiently and progressively uncover relationships based on concept descriptions, with each step carried out by MAD. (2) a concept augmentation-enhanced CD model that refines concept embeddings using a graph self-supervised learning fusion layer and a pairwise comparator-based Description Fusion Layer, leading to more reliable and accurate concept embeddings. Experimental results on three real-world datasets show that MACA-CD consistently outperforms existing methods under various realworld scenarios.