Trends and Prospects of Using Brain-Computer Interfaces in Education

Abstract

The relevance of the brain-computer interfaces (BCI) implementation in the field of edu-cation is conditioned by the realization of long-life and individualized learning concepts, as well as the requirement of effective and affordable automated learning systems. The article presents the analysis of studies on BCI usage in the educational process, in order to systema-tize the evidence, identify emerging trends and determine the difficulties and prospects of their applications in education. Nowadays, two main directions of BCI application for the purpose of training quality im-provement are revealed. In the first direction, the researchers’ attention is focused on psycho-physiology, meaning the identification of student’s current state characteristics and its timely correction with the teacher’s help (or self-correction). The second one emphasizes the peda-gogical aspect of BCI usage, such as monitoring the student’s cognitive activity in the process of course content perception to determine the most optimal parameters and conditions of its presentation. In the first case, the change of a student’s state or activity is emphasized, in the second one the changes relate to correction of learning content and its delivery. Among the main difficulties of using BCI in education are the following: problems with the equipment of modern BCI systems, the lack of clear classifications of neurophysiological correlates of various mental phenomena, the difficulty of consideration and differentiation of all the factors affecting a user during his interaction with BCI in natural environment. The prospects of BCI usage in learning are proposed: 1. Prediction of learning activity productivity; 2. Development of students’ self-control in the educational process; 3. Real-time identification of cognitive and affective students’ states in learning certain subjects (mathematics, physics, computer science, etc.); 4. Assessment of the impact of electronic learning tools on the process of information acquisition; 5. Monitoring the dynamics of cognitive activity intensity in students while solving dif-ferent learning tasks; 6. Identification of the available amount of information for its successful processing at the neurophysiological level to optimize the delivery of learning materials.