H2CD: Semantic‐Enhanced Heterogeneous Hypergraph Network With Large Language Model for Cognitive Diagnosis

Cognitive diagnosis aims to infer learners' knowledge states from their exercise responses, enabling personalised education at scale. Existing methods represent exercises solely by coarse‐grained knowledge component annotations, overlooking semantic content and step‐level cognitive processes. This causes diagnostic confounding, in which exercises requiring fundamentally different reasoning pathways become indistinguishable in learned representations. Such confounding degrades diagnostic accuracy by conflating distinct learning deficiencies. To address this, we propose H2CD, a semantic‐enhanced heterogeneous hypergraph framework that unifies LLM‐based cognitive decomposition with structural learning. First, we leverage a large language model to decompose exercises into step‐by‐step solution paths and extract fine‐grained step‐level KCs, capturing procedural knowledge beyond predefined annotations. Second, we construct a heterogeneous hypergraph with two types of hyperedges: knowledge hyperedges that connect exercises to their associated multi‐granular KCs, preserving compositional knowledge structures, and interaction hyperedges that capture personalised student‐exercise relationships. Third, a two‐stage hypergraph neural network performs knowledge structure propagation on static exercise‐KC relationships and interaction propagation on dynamic student‐exercise patterns. Finally, a KC‐aware prediction layer projects representations to a learnable diagnostic axis, enabling interpretable mastery assessment at individual KC granularity. Extensive experiments on three real‐world educational datasets demonstrate that H2CD outperforms state‐of‐the‐art methods in both AUC and accuracy.