AI-Driven Adaptive Learning Platform for Hyper-Personalized Education using Real-Time Analytics and Cognitive Modeling

The contribution of this paper is a novel adaptive learning framework, Neuro-Symbolic Cognitive Twin (NSCT) that combines symbolic knowledge modeling, affect aware neural state estimation, and real time strategy simulation to realize hyper personalized education. This is different from the existing adaptive systems that depend on the static rules or purely performance driven model: NSCT combines a cognitive knowledge graph with a transformer base affective estimator to generate a dynamic and interpretable representation of the cognitive emotional state. This simulation-based foresight engine designs and immediately simulates not a few but many instructional strategies and then picks from among them the optimal one that will lead to the highest mastery and engagement. The evaluation of the system used a synthetic dataset simulating 2,000 learner interactions on five different pedagogical strategies, and with each of 20 domain concepts. Experimental results with state-of-the-art baselines, Knewton, BKT, DKT and affect-aware models show NSCT outperforms the rest in terms of increased strategy match accuracy by 46.5% and mastery gain of 91.7% and is emotionally stable. Based on these findings, NSCT is presented as a complete solution to full time personalized digital education including both effective and academic benefits.