Artificial intelligence and neurolearning: how intelligent tutors enhance understanding and problem-solving in students

Abstract

Artificial intelligence has brought about a significant transformation in the educational field through the development of intelligent tutoring systems that integrate principles of neurolearning, enabling the creation of more adaptive teaching environments that are sensitive to cognitive differences and oriented towards strengthening deep understanding and the resolution of complex problems. These systems analyse behavioural patterns, recurring errors and levels of attention, providing immediate feedback that stimulates metacognition and critical thinking. Nevertheless, gaps in conceptual understanding persist, particularly in areas such as mathematics and science, due to traditional educational models that prioritise uniform approaches and memorisation over meaningful learning. Within this framework, the objective is to analyse, through a bibliographic review, the existing scientific evidence on how intelligent tutors based on artificial intelligence, articulated with principles of neurolearning, influence students’ understanding and problem-solving, without considering their direct implementation. The analysis shows that this integration establishes a solid educational framework in which intelligent tutoring systems foster deeper, more autonomous and sustained learning processes by adapting content, regulating task complexity and providing immediate feedback, consolidating themselves as a strategic axis for educational innovation.