Development of an Interactive Real-Time Student Learning Platform with AI-Based Knowledge Gap Detection

The new Adaptive Cognitive Trajectory Modeling (ACTM) framework presents a smart analyticaldriven model to customize online learning contexts by dynamically analyzing and optimizing predictive feedback. Since quasi-experimenter adaptive systems only use performance indicators, ACTM incorporates behavioral, semantic and temporal dimensions to model the varying comprehension process in each learner. The framework employs neural-symbolic reasoning and attention-weighted inferencing in constructing a CSG in cases where there are relationships between such concepts, and in ongoing construction of the CSG. The information about user interaction (response latency, sequence navigation latency and contextual errors manifested in the form of real-time data stream on interaction with the environment) is inputted in the deep recurrent encoder and calculate the cognitive drift and cognitive stability of the learner. Reinforcement controller changes the difficulty of the content, pace, and questions generation dynamically, both to optimize the interaction and memorization. Due to this cyclic process of feedback, ACTM anticipates potential sources of knowledge shortage before it is translated into performance discrepancy. The adaptive trajectory mapping that the model offers allows the learner to work more and independently and the precision of instruction gives a detailed insight into the learning behavior of individuals. The preconditions to this model precondition the introduction of smart systems of education that, in the long-term, will aid in achieving cognitive wearability, contribute to an increase in conceptual expertise, and remove chronic educational disparities between various cohorts of consumers. The proposed method has the overall accuracy of 94.7% which depicts improved performance compared with all existing models.