MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment

• URL: http://arxiv.org/abs/2606.29049v1
• Date: Sat, 27 Jun 2026 19:10:31 GMT
• Title: MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment
• Abstract summary: MOSAIC is a novel framework that orchestrates semantic alignment with sequential modeling.<n>We introduce a cross-granularity consistency objective that jointly regularizes mastery estimation across concept, topic-cluster, and global proficiency levels.<n>Our method achieves AUC improvements of up to 3.4% and Accuracy gains of up to 2.5 % across all benchmarks.
• Score: 5.7029090660781465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Tracing (KT) is important for personalized education but traditionally suffers from two key limitations: a reliance on shallow ID-based representations that neglect semantic depth and a restriction to single-granularity mastery estimation that overlooks hierarchical knowledge dependencies. To address these challenges, we propose MOSAIC (Multi-granularity Online Semantic AI for Collaborative Knowledge), a novel framework that orchestrates LLM-driven semantic alignment with sequential modeling. Unlike methods that use LLMs solely as predictors, MOSAIC leverages a frozen LLM to generate dynamic, context-aware embeddings and hierarchical prediction prompts, explicitly capturing collaborative signals and peer interactions. Furthermore, we introduce a cross-granularity consistency objective that jointly regularizes mastery estimation across concept, topic-cluster, and global proficiency levels. Extensive experiments on ASSISTments, EdNet, and a newly collected large-scale MOOC dataset demonstrate that MOSAIC establishes new state-of-the-art results. Specifically, our method achieves AUC improvements of up to 3.4\% and Accuracy gains of up to 2.5 \% across all benchmarks. Notably, MOSAIC exhibits superior robustness in collaboration-rich environments and long-sequence scenarios (AUC 0.862 on MOOC), offering both high predictive precision and semantically grounded interpretability.

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