COGNITIVE LOAD REDUCTION IN MEDIA LEARNING VIA AI
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6854Keywords:
Cognitive Load Theory, AI In Education, Adaptive Learning, Multimodal Sensing, Cnn–Lstm, Reinforcement Learning, Cognitive Efficiency, EEG, Eye-Tracking, Human–AI CollaborationAbstract [English]
The momentum of multimedia learning environments has increased the issues related to how to administer the cognitive load of learners. The paper describes a cognitively intelligent real-time detection of cognitive load and next-generation media optimization framework that is based on Cognitive Load Theory (CLT). The proposed system combines multimodal sensing (EEG, eye-tracking, affective cues) with a hybrid CNN-BLSTM inference engine and an RL-based adaptive controller to dynamically balance the intrinsic, extraneous and germane cognitive load. The model was experimentally validated using 120 participants who showed that the model had a detection accuracy of 91.3% and a positive correlation with self-reported mental effort (r = 0.84, p < 0.001). Students in the adaptive group were found to record a learning gain that was 62 higher and a cognitive efficiency that was 27 higher than that of the control group. The physiological patterns were associated with stable attention (VAS ↑11%) and moderate workload (CWI held constant within 0.55 0.65), which proved the maintenance of cognitive balance. The results support the idea that AI-mediated adaptation may be used to control mental effort, improve the results of learning, and implement the concepts of CLT in practice. The study lays a scalable and interpretable platform of human-centered, neuro-adaptive systems of learning that incorporate the cognitive theory and machine intelligence to learn designing of the next-generation educational systems.
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Copyright (c) 2025 Sangeet Saroha, Dr. Praveen Priyaranjan Nayak, Ms. Darshana Prajapati, Raman Verma, Manivannan Karunakaran, Kashish Gupta, Vijay Itnal
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