Counterfactual Graph Representation Learning for Fairness-Aware Cognitive Diagnosis

Cognitive diagnosis serves as a key component in personalized intelligent education, designed to accurately evaluate students’ knowledge states by analyzing their historical response data. It offers fundamental support for various educational applications such as adaptive learning and exercise recommendation. However, when leveraging student data, existing diagnostic models often incorporate sensitive attributes like family economic background and geographic location, which may lead to bias and unfairness. To address this issue, this paper introduces a Fairness-Aware Cognitive Diagnosis model (FACD) based on counterfactual graph representation learning. The approach builds student-centered causal subgraphs and integrates a graph variational autoencoder with adversarial learning to mitigate the influence of sensitive attributes on node representations. It further employs both central-node and neighbor-node perturbation strategies to generate counterfactual samples. A Siamese network is utilized to enforce representation consistency across different counterfactual scenarios, thereby deriving fair student contextual embeddings. Experimental results on the PISA 2015 dataset show that FACD outperforms conventional cognitive diagnosis models and their fairness-aware variants in terms of ACC, AUC, and RMSE. Ablation studies confirm the effectiveness and synergistic nature of each module. This work provides a viable pathway toward more reliable and equitable cognitive diagnosis systems.