Neurocognitive processes associated with learning and artificial neural networks: a post-pandemic review

Huarachi-Chuquimia G.; Claros-Rojas C.N.; Flores-Poves J.L.; Parra-Quispe A.L. · Clio. Revista de Historia, Ciencias Humanas y Pensamiento Critico. (2026) · DOI: 10.5281/zenodo.20804519

Resumen

This study aimed to analyze the relationship between neurocognitive processes associated with human learning and artificial neural networks in the post-pandemic context. A systematized bibliographic review with a qualitative and interpretive approach was conducted using scientific literature retrieved from Scopus, Web of Science, SciELO, and Dimensions. The search covered publications from 2020 to 2025, together with seminal sources required to frame learning, memory, artificial neural networks, deep learning, and NeuroAI. The selection process followed an adapted PRISMA 2020 logic: from 334 records identified, 38 studies and documents were included in the qualitative synthesis. The findings show that the pandemic and the subsequent transition to hybrid learning environments intensified the use of digital platforms, learning analytics, predictive models, and generative artificial intelligence, creating new demands on attention, working memory, self-regulation, reasoning, and decision-making. Convolutional, recurrent, and LSTM networks, attention mechanisms, Transformers, and deep reinforcement learning make it possible to establish functional analogies with selected neurocognitive processes; nevertheless, such links should not be understood as equivalences between the brain and machines. The study concludes that artificial neural networks offer relevant resources for modeling, analyzing, and supporting certain aspects of learning, provided that their application is guided by pedagogical criteria, technical transparency, human oversight, and ethical responsibility.

Instituciones: Universidad Nacional de San Agustín de Arequipa; Universidad Nacional de San Cristóbal de Huamanga; Universidad Nacional Jorge Basadre Grohmann