EDUCATIONAL CONVERGENCES: NEUROLEARNING AND ARTIFICIAL INTELLIGENCE

Abstract

Artificial intelligence-assisted resources with educational applications have been attracting increasing attention on a global scale. Although these resources, integrated with learning principles, have transformative potential, there is still a gap in their articulation with artificial intelligence. Thus, this study aims to describe the profile of neurolearning linked to artificial intelligence, its applications and challenges. Method: a scoping review was conducted, with articles analyzed in the last four years (2022-2025), with searches in the SciELO, Science Direct, Pubmed/NCBI databases. The mnemonic combination PCC (Population, Context, Concept) was used to define the guiding research question and the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) checklist. Results: Twenty-three articles that met the pre-established criteria were included and, after analyzing convergent content on artificial intelligence and neurolearning, the importance of the effects of AI-assisted strategies, cognitive and pedagogical dynamics for personalizing teaching is evident. Final considerations: the effects of neurolearning and neuroplasticity can be further enhanced by artificial intelligence. Understanding how the brain processes, retains and uses information favors the performance of adaptive learning systems, optimizes educational strategies, promotes personalization and student protagonism. These changes bring ethical challenges, such as data privacy, AI biases and educational quality, requiring interdisciplinary collaboration and commitment to educational values.