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ABSTRACT
A brain–computer interface (BCI) bridges brain and computer activities through the computer engineering technique of capturing and analysing a person’s biological and neurological response signals. This article reviews 37 empirical studies published in the past five years regarding the application of BCIs in mainstream education contexts for students’ learning regulation. The characteristics of the research methodology, technologies, targeted application scenarios and main outcomes of the reviewed studies are presented in detail and summarised. The results show that most of the research in this area has concentrated on the effects of neurofeedback on the learning regulation process, optimising data-processing algorithms, developing criteria, and designing BCI-based instructional environments. Neurofeedback- and biofeedback-based BCI approaches can help to monitor learning states and have positive effects on learning regulation. The paper also discusses the future development of BCIs as an effective technique for learning analysis and regulation, and it proposes a prototype of neurofeedback- and biofeedback-based BCI learning regulation systems and their application scenarios. By highlighting the current applications and possible future trends of the use of BCIs in mainstream education and identifying the challenges involved, this review can guide the development of smart education in the age of artificial intelligence.
Disclosure statement
No potential conflict of interest was reported by the author(s).