Positioning Artificial Intelligence Research in East Asia and Latin America: A Comparative Bibliometric Analysis

Carvalho Proença J.J.; Campos Rosendo N.J.; Navas Gotopo S.V. · Information (Switzerland) (2026) · DOI: 10.3390/info17050503

Resumen

This study aims to provide a comprehensive cross-regional bibliometric analysis of artificial intelligence (AI) research in East Asia and Latin America from 2020 to 2025. By quantifying publication trends, authorship, institutional productivity, collaboration networks, and citation impact, the research seeks to identify regional leaders, thematic clusters, and disparities in visibility and impact between these two regions. Design/methodology/approach; Scopus-indexed publications containing the phrases “artificial intelligence research” or “artificial intelligence innovation” in their title, abstract, or keywords were retrieved for the period 2020–2025. Inclusion criteria required at least one author’s affiliation in any of the fourteen specified countries across East Asia or Latin America. All document types (articles, reviews, conference papers, book chapters) were considered. Metadata were manually extracted from Scopus database ranking to identify the top-cited papers, most prolific authors, leading institutions, thematic and subject-area concentrations, and crossnational collaboration patterns. Findings; this bibliometric review clarifies the dynamic trajectory of AI research in East Asia and Latin America, revealing significant disparities in productivity, visibility, and thematic focus. The findings underscore the need for targeted investments in research capacity building, strategic international partnerships, and thematic realignment particularly for Latin America to enhance global visibility and align with emerging AI trends. Originality; by contrasting two understudied regions (East Asia vs. Latin America), we capture shifts in the AI landscape—specifically, the generative AI boom across subfields and regions that no single region or pre 2022 study can. By highlighting structural disparities in productivity, citation impact, and institutional support, it offers policymakers, funding agencies, and academic leaders novel insights.

Instituciones: Universidad San Ignacio de Loyola; Universidad Tecnológica del Perú