{"type":"ScholarlyArticle","title":"HiCD: Hyperbolic Insight Through Decomposed Educational Graphs for Long-Tailed Cognitive Diagnosis","authors":[{"name":"Shengwei Ji","slug":"shengwei-ji","given":"Shengwei","family":"Ji","affiliation":""},{"name":"Wenli Wang","slug":"wenli-wang","given":"Wenli","family":"Wang","affiliation":""},{"name":"Yongqiang Xie","slug":"yongqiang-xie","given":"Yongqiang","family":"Xie","affiliation":""},{"name":"Fei Liu","slug":"fei-liu","given":"Fei","family":"Liu","affiliation":""},{"name":"Yonghui Yang","slug":"yonghui-yang","given":"Yonghui","family":"Yang","affiliation":""}],"abstract":"Cognitive diagnosis (CD) aims to infer students' mastery of knowledge concepts from their response behaviors and constitutes a core component of intelligent education and personalized learning. However, existing graph-based CD models struggle to handle the pronounced long-tail distributions in educational data, where most students and concepts interact with only a limited number of exercises, resulting in suboptimal representation learning and poor generalization to low-frequency instances. To address this challenge, we propose HiCD (Hyperbolic insight for Cognitive Diagnosis), a novel hyperbolic model that embeds students, exercises, and concepts into non-Euclidean space. By exploiting the exponential representational capacity of hyperbolic geometry, HiCD naturally captures hierarchical and sparse structures, effectively alleviating long-tail bias while enhancing embedding expressiveness. A key contribution of HiCD is a hyperbolic diagnostic function that operates directly on the manifold, avoiding Euclidean approximations and preserving geometric consistency. Moreover, HiCD decomposes the educational graph into three semantically distinct subgraphs and assigns each a dedicated curvature, enabling adaptive geometric modeling of heterogeneous relations. Extensive experiments on multiple benchmark datasets demonstrate that HiCD consistently improves diagnostic accuracy and robustness, particularly under severe long-tail scenarios. The source code is available at: https://github.com/CyberXie/HiCD.","doi":"10.24963/ijcai.2026/299","url":"https://ijcai.org/proceedings/2026/299","pdf":"https://ijcai.org/proceedings/2026/0299.pdf","markdown":"https://ijcai.org/proceedings/2026/299.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/299","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":299,"pages":{"first":2689,"last":2697},"keywords":["Humans and AI: Cognitive modeling","Humans and AI: Personalization and user modeling","Data Mining: Mining graphs","Humans and AI: Computer-aided education"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}