Challenging Cognitive Load Theory. The Role Of Educational Neuroscience And Artificial Intelligence In Redefining Learning Efficacy - Info and Reading Options
By Dr Andrew Sortwell, Evgenia Gkintoni, Constantinos Halkiopoulos and Hera Antonopoulou
“Challenging Cognitive Load Theory. The Role Of Educational Neuroscience And Artificial Intelligence In Redefining Learning Efficacy” Metadata:
- Title: ➤ Challenging Cognitive Load Theory. The Role Of Educational Neuroscience And Artificial Intelligence In Redefining Learning Efficacy
- Authors: Dr Andrew SortwellEvgenia GkintoniConstantinos HalkiopoulosHera Antonopoulou
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- Internet Archive ID: osf-registrations-5m9j6-v1
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Abstract: Background/Objectives: This systematic review brings together Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) with an emphasis on how these may be used to guide creation of the best learning environments most effectively in educational contexts—offering personalized educational experiences in schools for K–12 students and adults. Methods: The paper involves a comprehensive review of N=104 studies and research findings related to the integration of CLT principles with AI and ML in educational settings. Neurophysiological tools like EEG and fNIRS are examined for their role in monitoring cognitive states, along with the application of AI-driven adaptive feedback systems. Ethical considerations, data privacy issues, and scalability challenges are also analyzed. Results: The review underscores the fact that AI and ML can dramatically improve learning outcomes by personalizing instruction, providing adaptive feedback, and managing cognitive load in real time. The integration of neurophysiological measurements with AI-driven feedback loops thus holds great promise for more personalized learning (PL) experiences. Ethical concerns related to data privacy and algorithmic biases are quite prevalent, though, hence stringent ethical frameworks and interdisciplinary collaborations are required. Conclusions: The study concludes that AI and ML hold substantial potential in transforming education but must be implemented with careful consideration of ethical, privacy, and inclusivity factors. Future research should focus on scalable, cost-effective approaches that are going to specifically benefit underserved populations and ensure that AI-enhanced education systems are inclusive for all learners.
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