Research on Personalized Cognitive Graph Based on Large Language Models (LLM) for Education

Traditional educational systems struggle to model dynamic cognitive processes, limiting personalized interventions. This paper presents a Learner Cognitive Graph (LCG) framework using educational large language models with bias mitigation to address this challenge. We introduce a Dynamic Cognition Graph (DCG) to represent spatiotemporal interactions among students, knowledge, and exercises, capturing cognitive evolution and state transitions. A reverse Turing test-driven agent collects multi-modal behavioral data via structured prompts with hallucination control, while dynamic graph neural networks and reinforcement learning enable behavior prediction and personalized intervention optimization. The framework forms a closed loop from perception to adaptive support, enhancing cognitive modeling precision and providing scalable learning support. Key innovations include heterogeneous DCG construction, interactive data extraction with bias detection, and data-driven intervention design. This work advances intelligent educational systems while addressing inherent biases in large language models.