Artificial Intelligence in Latin America: A Bibliometric Approach to Sustainable Development and Educational Innovation

Abstract

In this paper, the rise of AI research in Latin America is considered, as well as the primary challenges to implementing AI, including a lack of infrastructure, funding, and AI professionals. The research is based on a mixed-method approach, integrating qualitative content analysis and quantitative bibliometric data from Scopus and Web of Science to examine AI publications in Latin America from 2015 to 2024. It is found that there is a 150 per cent growth in AI publications, with Brazil, Mexico, and Argentina topping the list. The primary research topics found are Machine Learning (32%), Natural Language Processing (25%), and AI to Sustainable Development (20%). Nevertheless, issues like inadequate infrastructure, inadequate funding, and skills gap still hinder the full potential of AI in the region. The paper highlights the importance of interdisciplinary work between academia, industry, and government, and policy interventions that facilitate AI research, education, and innovation. Suggestions are to invest more in AI infrastructure, create AI-specific funding, and incorporate AI training into educational systems to overcome the talent gap. Lastly, future studies are needed to consider the long-term effects of AI policies in the region and to make sure that the integration of AI is consistent with the Sustainable Development Goals (SDGs) and regional development priorities.