THE USE OF ARTIFICIAL INTELLIGENCE IN LEARNIG: A NARRATIVE REVIEW OF RECENT LITERATURE

Abstract

This article sought to comprehensively analyze how Artificial Intelligence (AI) has been integrated into learning processes, highlighting its main contributions, potentialities, and challenges according to recent literature. To achieve this objective, a narrative review was conducted, gathering contemporary studies that discuss learning personalization, the use of intelligent tutoring systems, the application of learning analytics, and the advancement of generative technologies in educational contexts. The results showed that AI-based tools enhance pedagogical adaptability by adjusting content, pace, and learning paths according to students’ performance. It was also observed that data-driven algorithmic analyses contribute to more accurate diagnostics, supporting rapid, evidence-based pedagogical interventions. In addition, generative technologies expand possibilities for interaction, creation of learning materials, and continuous student support. However, the literature also points to challenges related to data privacy, algorithmic bias, system transparency, and inequalities in institutional access. Thus, it is concluded that the impact of AI on learning depends on the responsible integration of technological innovation, adequate teacher training, and ethical principles that ensure an inclusive, safe, and socially engaged use of these technologies.