BIG DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE IN PROFESSIONAL AND TECHNOLOGICAL EDUCATION: ADVANCED STRATEGIES FOR DEVELOPING BNCC COMPETENCIES IN SECONDARY AND TECHNICAL EDUCATION
Álaze Gabriel do Breviário
Master in Theology. University of São
Paulo. Email: alaze_p7sd8sin5@yahoo.com.br. Lattes URL:
http://lattes.cnpq.br/9973998907456283.
Valdimeire Silvestre Lopes
Master's student in Education Sciences and
Christian Ethics, Ivy Christian University, mere_silvestre@hotmail.com. Lattes
URL:
Deusirene Souza da Silva Fróes
PhD student in Education Sciences and
Christian Ethics, Ivy Enber Christian University,
deusirenesouzasilvafroes@gmail.com. Lattes URL:
https://lattes.cnpq.br/0218139923264576.
Flávia Adriana Santos Rebello
Master in Administration, Must University,
frebello.mentoriatextual@gmail.com, Lattes URL:
http://lattes.cnpq.br/3406211444097827.
João Batista Lucena
Master's student in Education, Federal
Institute of Education, Science and Technology of Rio Grande do Norte,
joao.batista.lucena@gmail.com. Lattes URL:
http://lattes.cnpq.br/2822567703207399.
Logan Faedda Rago
Master's student in Education Sciences and
Christian Ethics, Ivy Enber Christian University, loganfaedda@hotmail.com.
Lattes URL: https://lattes.cnpq.br/2516880221903287.
Leliane Aparecida Castro Rocha
PhD in Education, Methodist University of
São Paulo (UMESP), prof.lelianerocha@gmail.com. Lattes URL:
http://lattes.cnpq.br/6176059915115617.
Ayla Limeira da Silva
Bachelor's degree in Special Education, Federal University of São Carlos (UFSCar). Email: aylasilva250@gmail.com. Lattes URL: https://wwws.cnpq.br/cvlattesweb/PKG_MENU.menu?f_cod=700C515E26C0EC79366115D7A29098A7#.
ABSTRACT
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.
Keywords: Educational Personalization. Technological Inclusion. Pedagogical Competencies. Digital Transformation. Teacher Training.