Computational Neuroscience in Higher Education: A Systematic Review on the Problems Addressed, Methods Used and Implications

Abstract

Computational neuroscience (CNS) has enabled significant advances in the understanding of cognitive processes through mathematical models and computational simulations, providing a more precise understanding of brain activity. However, its application in higher education remains limited, which restricts its potential to optimize teaching, cognitive and emotional regulation, and personalized learning. This study aims to examine the problems addressed by CNS, the methods used, and their implications for higher education, analyzing scientific articles from the ScienceDirect, PubMed, and Scopus databases through a systematic review study following the PRISMA guidelines. The results show that the application of methods such as EEG, BCI, neurofeedback, fNIRS, tDCS, and computational models has facilitated the adaptation of content and the assessment of cognitive load in students. However, its implementation still faces methodological, economic, and technological barriers, such as variability in neural responses and limited accessibility. It is concluded that CNS has a high potential to transform higher education, but its effective integration requires the adoption of regulatory and standardized frameworks, which promote the creation of specialized areas in CNS within their departments of psychopedagogy or neuroeducation, in order to promote its development, accessibility, and ethical application in educational environments.