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[2602.22879] Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space
Summary
This article presents a novel approach to knowledge tracing using a Large Language Model (LLM) to enhance the understanding of student learning behaviors through hierarchical alignment in hyperbolic space.
Why It Matters
The research addresses limitations in traditional knowledge tracing methods by introducing a more sophisticated model that captures the complexities of cognitive states and individualized learning experiences. This advancement could significantly improve educational technologies and personalized learning systems.
Key Takeaways
- The proposed L-HAKT framework improves knowledge tracing by modeling hierarchical dependencies of knowledge points.
- Contrastive learning in hyperbolic space helps align synthetic and real data, enhancing the accuracy of learning assessments.
- The framework effectively characterizes learning curves across different knowledge levels, aiding in personalized education.
Computer Science > Artificial Intelligence arXiv:2602.22879 (cs) [Submitted on 26 Feb 2026] Title:Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space Authors:Xingcheng Fu, Shengpeng Wang, Yisen Gao, Xianxian Li, Chunpei Li, Qingyun Sun, Dongran Yu View a PDF of the paper titled Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space, by Xingcheng Fu and 6 other authors View PDF HTML (experimental) Abstract:Knowledge Tracing (KT) diagnoses students' concept mastery through continuous learning state monitoring in this http URL methods primarily focus on studying behavioral sequences based on ID or textual this http URL existing methods rely on ID-based sequences or shallow textual features, they often fail to capture (1) the hierarchical evolution of cognitive states and (2) individualized problem difficulty perception due to limited semantic modeling. Therefore, this paper proposes a Large Language Model Hyperbolic Aligned Knowledge Tracing(L-HAKT). First, the teacher agent deeply parses question semantics and explicitly constructs hierarchical dependencies of knowledge points; the student agent simulates learning behaviors to generate synthetic data. Then, contrastive learning is performed between synthetic and real data in hyperbolic space to reduce distribution differences in key features such as question difficulty and forgetting patterns. Finally, by optimizing h...
