Intelligent educational decision-making system driven by multimodal data fusion and knowledge graphs

This paper presents an intelligent educational decision-making framework that integrates multimodal data fusion with structured knowledge graph reasoning to enhance personalized learning experiences. To address challenges associated with heterogeneous data integration and interpretability, we propose the Cognizant Instructional Field Network (CIFNet), a hybrid neural-symbolic architecture. CIFNet combines symbolic representations of learner states with deep contextual embeddings, supporting dynamic and interpretable decision-making processes in educational environments. It jointly models epistemic progression, pedagogical intents, and instructional dependencies while accounting for uncertainty and sparse feedback. Building on CIFNet, we introduce the Pedagogical Inference Controller (PIC), a meta-cognitive strategic layer designed to refine instructional actions through strategic utility estimation, regret-aware adaptation, uncertainty-weighted exploration, and curriculum alignment. By simulating counterfactual instructional outcomes and prioritizing the reduction of knowledge gaps, PIC aims to promote pedagogically coherent and learner-centered interventions. Experimental evaluations across multiple educational datasets indicate that the proposed framework achieves promising improvements over traditional baselines and several recent deep learning models in predictive accuracy and learning-related metrics. While the results demonstrate the potential of combining symbolic reasoning with neural representation learning for more transparent and adaptive educational decision-making, further studies–particularly in real classroom environments–are needed to fully assess the system’s broader applicability and long-term impact.