PERSONALIZED LEARNING AND ARTIFICIAL INTELLIGENCE IN K-12 EDUCATION

Abstract

This article examines personalized learning mediated by Artificial Intelligence in Basic Education, considering the curricular and pedagogical transformations resulting from the incorporation of adaptive systems into the school environment. The general objective is to analyze how AI-driven platforms can adjust learning paths to students' individual needs, promoting greater engagement and efficiency in teaching processes. Regarding methodology, Bibliographical Research is adopted, grounded in the contributions of Gil (2019) for systematizing the theoretical survey and Severino (2018) for organizing the documentary analysis. The investigative path addresses the foundations of personalization, the implications of AI in curricular restructuring, the ethical and formative challenges for teachers, as well as the possibilities of integrating adaptive technologies with active methodologies. It is found that AI-assisted personalization offers significant potential for overcoming homogenizing models, provided it is accompanied by critical teacher training and institutional policies that ensure equity in access.