ARTIFICIAL INTELLIGENCE AND BIG DATA IN SCIENCE EDUCATION: COMPUTATIONAL MODELING AND DATA ANALYSIS TO PROMOTE SCIENTIFIC THINKING IN STUDENTS

This research investigates the impact of integrating Artificial Intelligence (AI) and Big Data in Science teaching, focusing on promoting more inclusive and personalized pedagogical practices. Brazilian education faces challenges related to teaching quality, and the use of these emerging technologies can offer innovative solutions to overcome such obstacles. The problem addresses the resistance to the adoption of these tools, especially due to the lack of training and infrastructure in schools. The main objective is to analyze how AI and Big Data can improve Science teaching, identifying challenges and benefits for Basic Education. The research adopts the neoperspectivist paradigm giftdeano, which promotes the coexistence of absolute and relative truths, emphasizing inclusion and diversity. Theories of technological learning, critical pedagogy and educational data models were used to guide the investigation. The hypothetical-deductive method was used to test the hypotheses related to the impacts of these technologies on education. The bibliographic and documentary narrative review involved consulting databases such as Scopus, Google Scholar and Web of Science Science, initially analyzing 120 papers and selecting 40 for further analysis. The main findings indicate that, despite resistance, AI and Big Data can provide teaching that is more adapted to students' needs, although infrastructure and teacher training are still major challenges. Gaps include the lack of studies in public school contexts. The theoretical and methodological contributions of the research offer new perspectives for the application of these technologies in Science teaching. The added value lies in the transformation of pedagogical practices and the potential social impact.