The effectiveness of using neural interfaces in the educational process to optimize the cognitive load of students
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Abstract
Modern educational technologies face the problem of managing the cognitive load of students, which determines the effectiveness of learning material and the development of competencies. Neurointerfaces based on electroencephalography (EEG) represent a significant potential for objective assessment and dynamic regulation of cognitive load in real time. This study is aimed at establishing the effectiveness of using EEG neural interfaces to optimize the cognitive load of students in the educational process. The research methodology is based on an experimental design using a non-invasive 24-channel EEG system to monitor the neurophysiological parameters of 128 students (19-25 years old) while performing educational tasks of varying complexity using an adaptive learning system. Quantitative analysis of the results demonstrates a significant improvement in memorization (by 27,3%), concentration (by 31,6%) and information processing speed (by 24,8%) in the experimental group compared with the control group. The integration of multimodal EEG data using machine learning algorithms made it possible to achieve 87,5% accuracy in classifying cognitive load levels and automatically adapting educational content. The results of the study confirm the effectiveness of using neural interfaces to personalize the educational process and reveal new perspectives for the development of adaptive learning systems that take into account the individual neurocognitive characteristics of students.
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