<p>Cognitive diagnosis serves as a core method in intelligent education, aiming to learn students’ proficiency in knowledge concepts by analyzing the records of their exercises. Existing approaches perform well in individual-level cognitive diagnosis, they face limitations in group-level cognitive diagnosis tasks and are not suitable for collaborative group scenarios. However, existing group-level cognitive diagnosis methods still have the following limitations: 1) They fail to fully model the complex relationships among groups, students, and exercises, resulting in the learning of group representations lacking comprehensive capture of multi-dimensional interaction information; 2) they neglect the complementary information between student-level interactions and exercise-level group preferences, leading to shallow group representations. To overcome the previously discussed limitations, we propose a Graph Learning with Bidirectional Distillation framework for Group Cognitive Diagnosis (GDGCD). Specifically, to address Limitation 1, we use hypergraphs to model groups and connect students and exercises and design a novel student-level hypergraph neural network to aggregate node information, forming fine-grained student-level group representations. Meanwhile, in the exercise-level bipartite graph, groups are connected to exercises. A distinctive exercise-level representation of the group is constructed via the graph neural network. To handle Limitation 2, we introduce a mutual distillation model to integrate interaction features from both perspectives, enabling the generation of rich group representations. We perform extensive experiments on four real-world datasets, and the experimental results demonstrate the effectiveness of our GDGCD method over several recent SOTA approaches on RMSE and MAE metrics.</p>