How artificial intelligence and neurolearning enhance understanding and problem-solving through intelligent tutors

Abstract

The incorporation of artificial intelligence into educational systems has substantially reshaped teaching and learning processes, particularly through the use of intelligent tutors capable of dynamically adjusting content, learning pace and pedagogical strategies based on the analysis of students’ cognitive behaviour. These systems integrate adaptive and predictive models that take into account cognitive, metacognitive and contextual variables, thereby expanding the possibilities for personalised education focused on deep understanding. Within this framework, neurolearning provides a scientific foundation that explains how neural processes related to attention, memory and emotion influence knowledge construction and problem-solving. The articulation between artificial intelligence and neurolearning enables the design of adaptive learning environments that optimise motivation, self-regulation and the transfer of knowledge to problem-based situations. From this perspective, intelligent tutors are consolidated as high-impact pedagogical mediators, as they move beyond content automation to incorporate adaptive feedback and cognitive scaffolding. The analysis of the scientific literature shows that this technological and pedagogical convergence fosters deeper, more contextualised and sustainable learning, significantly strengthening understanding and the development of complex problem-solving skills across diverse educational contexts.