BIG DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE IN PROFESSIONAL AND TECHNOLOGICAL EDUCATION: ADVANCED STRATEGIES FOR DEVELOPING BNCC COMPETENCIES IN SECONDARY AND TECHNICAL EDUCATION

This research addresses the integration of Big Data Analytics and Artificial Intelligence (AI) in professional and technological education, highlighting their potential to meet the competencies of the National Common Curricular Base (BNCC). Contextualized in the need to modernize technical education in Brazil, the problem investigated how these technologies can be implemented to personalize learning, reduce educational inequalities, and train teachers. The general objective was to analyze the applicability of these tools in the development of technical and pedagogical competencies aligned with the BNCC. Methodologically, the research adopted the Gifted neoperspectivist paradigm and the theories of Complexity, Meaningful Learning, Big Data, and Inclusive Education. The hypothetical-deductive method guided the investigation, complemented by a Narrative Bibliographic and Documentary Review that consulted databases such as Scopus, Web of Science and SciELO, analyzing 47 selected works. The main findings include the effectiveness of technologies in personalizing teaching, promoting student engagement and reducing inequalities, although barriers such as insufficient infrastructure and limitations in teacher training were identified. The contributions cover theoretical and methodological advances, as well as practical recommendations for the application of technologies in technical education. Limitations include the absence of experimental studies and the reliance on secondary data. This research adds value by expanding the possibilities of inclusive pedagogical practices and strengthening the debate on digital transformation in education.