EDUCATIONAL TRANSFORMATION WITH ADVANCED TDICS: INTEGRATION OF ARTIFICIAL INTELLIGENCE AND BIG DATA FOR THE DEVELOPMENT OF BNCC SKILLS IN THE INITIAL AND FINAL YEARS OF ELEMENTARY EDUCATION

This research investigates the use of advanced Digital Information and Communication Technologies (ICTs), artificial intelligence (AI) and Big Data in the personalization of elementary education, in alignment with the competencies of the National Common Curricular Base (BNCC). Inserted in a context of growing technological transformation in education, the research addresses the issue of how these tools can be effectively integrated into teaching, respecting inclusion and diversity, and overcoming challenges such as inequality of access and insufficient teacher training. The main objective was to analyze how these technologies can personalize teaching and develop specific BNCC competencies. The research adopted the Giftedian neoperspectivist paradigm, based on the theories of Constructivism, Cognitivism, Adaptive Learning and Computational Complexity. It used the hypothetical-deductive method and conducted a Bibliographic and Documentary Narrative Review, analyzing 87 works selected from databases such as Scopus, Web of Science and Scielo. The main findings indicate that tools such as Python, R and Google Colab personalize pedagogical practices and promote inclusion, but teacher training and unequal infrastructure are critical challenges. Among the gaps, the absence of longitudinal studies and clear regulations for the ethical use of educational data stand out. Limitations include the dependence on secondary data and the lack of practical validation in the field. The research contributes theoretically, methodologically and empirically to the area of educational technologies, adding value to science and society by proposing innovative and inclusive practices that enhance learning and reduce inequalities.