Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy DOI Creative Commons
Evgenia Gkintoni, Hera Antonopoulou, Andrew Sortwell

et al.

Brain Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 203 - 203

Published: Feb. 15, 2025

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive systems, informed by neurophysiological insights, enhance personalized education for K-12 students adult learners. study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), other tools in assessing cognitive states guiding AI-powered interventions refine instructional strategies dynamically. Methods: reviews n = 103 papers related integration principles CLT with AI ML educational settings. evaluates progress made neuroadaptive technologies, especially real-time management load, feedback multimodal applications AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, scalability issues while pinpointing best practices robust effective implementation. Results: The results show that significantly improve Efficacy due managing load automatically, providing instruction, adapting pathways dynamically based data. Deep models Convolutional Neural Networks (CNNs), Recurrent (RNNs), Support Vector Machines (SVMs) classification accuracy, making systems more efficient scalable. Multimodal approaches system robustness mitigating signal variability noise-related limitations combining EEG fMRI, Electrocardiography (ECG), Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including considerations, security risks, accessibility disparities across learner demographics. Conclusions: are epitomes redefinition potentials solid frameworks, inclusive design, scalable methodologies must inform. Future studies will be necessary refining pre-processing techniques, expanding variety datasets, advancing developing high-accuracy, affordable, ethically responsible systems. future AI-enhanced should inclusive, equitable, various populations would surmount technological dilemmas.

Language: Английский

Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy DOI Creative Commons
Evgenia Gkintoni, Hera Antonopoulou, Andrew Sortwell

et al.

Brain Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 203 - 203

Published: Feb. 15, 2025

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive systems, informed by neurophysiological insights, enhance personalized education for K-12 students adult learners. study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), other tools in assessing cognitive states guiding AI-powered interventions refine instructional strategies dynamically. Methods: reviews n = 103 papers related integration principles CLT with AI ML educational settings. evaluates progress made neuroadaptive technologies, especially real-time management load, feedback multimodal applications AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, scalability issues while pinpointing best practices robust effective implementation. Results: The results show that significantly improve Efficacy due managing load automatically, providing instruction, adapting pathways dynamically based data. Deep models Convolutional Neural Networks (CNNs), Recurrent (RNNs), Support Vector Machines (SVMs) classification accuracy, making systems more efficient scalable. Multimodal approaches system robustness mitigating signal variability noise-related limitations combining EEG fMRI, Electrocardiography (ECG), Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including considerations, security risks, accessibility disparities across learner demographics. Conclusions: are epitomes redefinition potentials solid frameworks, inclusive design, scalable methodologies must inform. Future studies will be necessary refining pre-processing techniques, expanding variety datasets, advancing developing high-accuracy, affordable, ethically responsible systems. future AI-enhanced should inclusive, equitable, various populations would surmount technological dilemmas.

Language: Английский

Citations

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