Integration of neuroeducation, Universal Design for Learning, and artificial intelligence: effects on learning personalisation and academic performance

Abstract

Contemporary educational transformation has created the need to rethink traditional teaching models in order to move towards approaches capable of responding to the cognitive, emotional, and educational diversity of university students. In this process, the integration of neuroeducation, the Universal Design for Learning (UDL), and artificial intelligence emerges as an alternative aimed at developing more inclusive, adaptive educational experiences centred on the particularities of human learning. Neuroeducation provides foundational insights into the mechanisms involved in the acquisition and consolidation of knowledge; UDL incorporates principles designed to ensure pedagogical accessibility through multiple means of engagement and representation; whilst artificial intelligence expands the possibilities for educational analysis, monitoring, and adaptation. The objective was to analyse the effects derived from the integration of these approaches on learning personalisation and academic performance, in order to understand their contribution to the development of educational models oriented towards the diversity of learning needs. To this end, a structured process of identification, selection, and analysis of scientific evidence was carried out in accordance with PRISMA guidelines. The analysis revealed that this convergence strengthens more flexible learning experiences, optimises academic support processes, and fosters conditions for improving academic performance within contemporary higher education.