Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space

2026-02-26

AI Summary

This work addresses the challenge that existing knowledge tracing methods struggle to simultaneously model the hierarchical evolution of cognitive states and individual differences in perceiving item difficulty. To this end, the paper proposes a novel approach that integrates large language models (LLMs) with hyperbolic geometry for the first time. Specifically, LLMs are employed to construct teacher and student agents that generate semantically aware synthetic learning behavior data. In hyperbolic space, contrastive learning aligns key features between real and synthetic data, while adaptive curvature optimization explicitly captures the tree-like hierarchical structure of knowledge concepts. Evaluated on four real-world educational datasets, the proposed method significantly outperforms state-of-the-art baselines, achieving higher prediction accuracy while enhancing model interpretability through its geometric representation of knowledge hierarchies.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Knowledge Tracing (KT) diagnoses students' concept mastery through continuous learning state monitoring in education.Existing methods primarily focus on studying behavioral sequences based on ID or textual information.While 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 hyperbolic curvature, we explicitly model the tree-like hierarchical structure of knowledge points, precisely characterizing differences in learning curve morphology for knowledge points at different levels. Extensive experiments on four real-world educational datasets validate the effectiveness of our Large Language Model Hyperbolic Aligned Knowledge Tracing (L-HAKT) framework.

Problem

• Knowledge Tracing
• Hierarchical Behavior
• Cognitive States
• Problem Difficulty Perception
• Semantic Modeling

Innovation

• Large Language Model
• Knowledge Tracing
• Hyperbolic Space
• Hierarchical Behavior Alignment
• Contrastive Learning