A distributed multi-agent framework for adaptive personalized online learning with dynamic knowledge graphs and learner modeling

A distributed multi-agent framework is developed to enhance adaptive personalized online learning. The framework integrates Large Language Models (LLMs), a Dynamic Educational Knowledge Graph (DEKG), Knowledge Tracing (KT), Reinforcement Learning (RL), and Explainable Artificial Intelligence (XAI) to support learner modeling, adaptive recommendation, and explanation. The architecture separates learner-facing interaction from distributed domain processing while enabling coordinated knowledge representation and adaptive decision-making. A controlled simulation-based feasibility study evaluates learner-state estimation and personalized recommendation. The results show that graph-enhanced KT provides a modest improvement over the independent KT baseline, while the graph-aware RL policy achieves the highest final mastery and mastery gain among the evaluated strategies, although its advantage does not extend to every evaluation metric. The findings also indicate that recommendation performance is influenced by reward design. Overall, the simulation provides preliminary evidence of the feasibility of the proposed framework. Further evaluation using real educational datasets and learner interactions is required to assess its effectiveness and generalizability.