AI Implementation to Enhance UDL in Basic Education: An Adaptive Model for Diverse Classrooms
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The article examines how artificial intelligence can enhance Universal Design for Learning in basic education within classrooms characterized by high student diversity. Its purpose is to analyze the articulation between both perspectives and propose an adaptive understanding that broadens students’ access, participation, and expression from an inclusive standpoint. Methodologically, it develops an exploratory bibliographic review of a documentary nature, with a qualitative approach and a descriptive-interpretative scope, supported by the search and thematic analysis of academic and institutional literature identified in databases such as Scopus, Web of Science, ERIC, SciELO, Dialnet, and Google Scholar. The findings show that artificial intelligence can strengthen UDL in four main dimensions: the flexible personalization of content and learning activities, the improvement of accessibility and multiple representation of information, immediate feedback to support self-regulation and progress monitoring, and the expansion of forms of participation and diverse student expression. However, the study concludes that these benefits are not automatic, since they depend on teacher training, adequate infrastructure, human supervision, ethical criteria, and equity-centered governance. Consequently, artificial intelligence is only pedagogically valuable when it expands inclusive opportunities without replacing teachers’ professional judgment.
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