CONTRIBUTIONS OF ARTIFICIAL INTELLIGENCE TO TEACHER PLANNING WITH ACTIVE METHODOLOGIES IN BASIC EDUCATION: A SYSTEMATIC LITERATURE REVIEW (SLR)

Abstract

This study aimed to analyze the contributions of Artificial Intelligence (AI) to teacher planning with active methodologies in Basic Education, identifying the main technologies employed, the associated methodologies, and the challenges related to their implementation in educational contexts. To this end, a qualitative and exploratory Systematic Literature Review (SLR) was conducted using the PICOC strategy and the Parsifal software for data organization, selection, and extraction. Searches were carried out in the SciELO and Springer Link Journals databases, covering articles published between 2023 and 2026. Initially, 74 studies were identified. After applying the inclusion, exclusion, and methodological quality criteria, nine studies were selected for data extraction. Subsequently, a full-text reading and in-depth analysis resulted in a final sample of six articles. The findings revealed that technologies such as ChatGPT, Generative Artificial Intelligence, Intelligent Tutoring Systems, Large Language Models, and predictive models contribute to personalized learning, content adaptation, immediate feedback, and the strengthening of active learning methodologies. Challenges related to teacher training, technological infrastructure, and ethical issues were also identified. It is concluded that AI has significant potential to improve pedagogical planning and strengthen student-centered practices, provided that it is used critically, ethically, and in alignment with educational objectives.