AI and Neurolearning in Action: The Impact of Intelligent Tutoring Systems on Conceptual Understanding and Problem-Solving in Educational Contexts

Abstract

The integration of artificial intelligence into educational contexts has led to significant transformations in teaching and learning processes, particularly through the development of intelligent tutoring systems capable of adapting content, instructional strategies and learning pace to students’ cognitive needs. These systems, supported by machine learning algorithms and adaptive approaches, promote personalised learning and the strengthening of problem-solving skills. In alignment with this perspective, neurolearning provides an explanatory framework for understanding the neurocognitive processes involved in attention, memory, motivation and self-regulation, which are essential for knowledge construction. Within this context, the aim of the study was to critically and rigorously analyse the scientific contributions related to intelligent tutoring systems grounded in artificial intelligence and neurolearning principles, in order to explain their influence on comprehension and the development of problem-solving skills across diverse educational settings. The research adopted a descriptive–analytical documentary approach, based on the examination of academic sources selected in accordance with the PRISMA protocol. The findings indicate that intelligent tutoring systems, when designed in coherence with neuroeducational principles, enhance learning effectiveness by providing adaptive feedback, progressive task sequencing and personalised cognitive support.