AI TRENDS IN EDUCATION: A BIBLIOMETRIC ANALYSIS OF EMERGING CONTEXTS (REMUNOM, 2026)
Artificial intelligence (AI) is emerging as a transformative force in education, driving personalized teaching and mitigating educational inequalities. This study aims to analyze, through a bibliometric approach, the main trends in the application of AI in education, with a focus on emerging contexts with limited infrastructure, and to identify advances, challenges, and opportunities. The methodology used was bibliographic research, characterized by systematic analysis of the literature. Data were collected from the Scopus and Web of Science (WoS) databases, covering the period from 2014 to 2024. After screening and applying inclusion and exclusion criteria, 634 documents were selected. Reference management was performed using Zotero, and data organization and cleaning was conducted on the Rayyan platform. Bibliometric analysis was conducted using the Bibliometrix package in RStudio software, and VOSviewer was employed for the scientific mapping and analysis of co-authorship and co-occurrence networks. The results revealed an increase in scientific production of AI in education from 2019 onwards, intensified by the COVID-19 pandemic. China, the United States, and India stood out as the countries with the most significant scientific production and academic impact in the region. Despite these advances, the study highlighted several challenges, including infrastructural barriers, algorithmic bias, a lack of teacher training, and ethical risks associated with the collection and use of sensitive data. It is concluded that the ethical, equitable, and contextualized use of AI in education requires public policies committed to digital inclusion, ongoing teacher training, and investments in accessible technologies that are sensitive to local realities.