High Skills and Giftedness in the Era of Ict: the Use of Artificial Intelligence and Big Data for Identifying and Enhancement of Talents in the Inclusive School Context
Year: 2025
Type: article
Abstract: This research addresses the application of Digital Information and Communication Technologies (ICTs), with a focus on Artificial Intelligence (AI) and Big Data, for the identification and development of students with high abilities in inclusive school environments. The growing use of these technologies to improve educational practices and identify talents in diverse educational contexts is contextualized. The problem focuses on the limitations of traditional methods in identifying and monitoring the progress of gifted students. The main objective is to investigate how ICTs can be effectively integrated into inclusive education, contributing to a more equitable and adaptive education. Methodologically, the research adopts the gifted neoperspectivist paradigm, based on the theories of Inclusion, Giftedness, Mediated Learning, AI and Big Data. The hypothetical-deductive method and a Narrative Bibliographic and Documentary Review are used, with 78 papers selected from databases such as Scopus, Web of Science and IEEE Xplore. The main findings indicate that AI and Big Data are effective in detecting and monitoring abilities, with ethical and technical limitations. Gaps related to school infrastructure and teacher training are identified. Theoretical and empirical limitations include the lack of practical data and ethical guidelines. This research contributes theoretically and empirically to the field of inclusive education, promoting a model of support for gifted students and adding value to Science and society by proposing guidelines for a more inclusive and personalized education.
Authors: Breviário, Álaze Gabriel do, Souza, Jaine Marques de, Lucena, João Batista, Rago, Logan Faedda, Gomes, Marcelo D'Ávilla Teixeira, Fróes, Deusirene Sousa da Silva
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6912059303
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