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This review aims to explore how integrating cognitive load theory, educational neuroscience, and AI can optimize learning environments.

February 15, 2025Brain SciencesOpen Access

Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy

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Systematic review examines AI and educational neuroscience improving learning efficacy in diverse learners, highlighting implications for practice.

This review aims to explore how integrating cognitive load theory, educational neuroscience, and AI can optimize learning environments.
Systematic review of 103 papers on the integration of cognitive load theory, AI, and ML in education.
Evaluation of neuroadaptive technologies and personalized feedback systems.
Analysis of challenges including data privacy, ethical concerns, and algorithmic bias.
AI and ML improve Learning Efficacy by managing cognitive load and personalizing instruction.
Deep Learning models like CNNs and RNNs enhance the efficiency of adaptive learning systems.
Multimodal approaches using EEG and other tools mitigate variability and noise in data.

Gkintoni et al. (2025) studied this question.

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Systematic review examines AI and educational neuroscience improving learning efficacy in diverse learners, highlighting implications for practice.