---
title: "HiCD: Hyperbolic Insight Through Decomposed Educational Graphs for Long-Tailed Cognitive Diagnosis"
authors:
  - "Shengwei Ji"
  - "Wenli Wang"
  - "Yongqiang Xie"
  - "Fei Liu"
  - "Yonghui Yang"
doi: "10.24963/ijcai.2026/299"
url: "https://ijcai.org/proceedings/2026/299"
pdf: "https://ijcai.org/proceedings/2026/0299.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "2689-2697"
track: "Main Track"
keywords:
  - "Humans and AI: Cognitive modeling"
  - "Humans and AI: Personalization and user modeling"
  - "Data Mining: Mining graphs"
  - "Humans and AI: Computer-aided education"
---

# HiCD: Hyperbolic Insight Through Decomposed Educational Graphs for Long-Tailed Cognitive Diagnosis

Shengwei Ji, Wenli Wang, Yongqiang Xie, Fei Liu, Yonghui Yang

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 2689-2697. DOI: [10.24963/ijcai.2026/299](https://doi.org/10.24963/ijcai.2026/299)

## 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.