THE EVOLUTION OF ARTIFICIAL INTELLIGENCE LITERACY RESEARCH: A BIBLIOMETRIC ANALYSIS
TL;DR
The analysis reveals an exponential growth in scholarly output, peaking in 2025 with 292 documents, with rapidly emerging themes including prompt engineering, academic integrity, large language models, AI ethics and teacher education.
Abstract
The rapid adoption of artificial intelligence and growing concerns regarding workplace disruption and misinformation, traditional digital literacy frameworks lack updated, systematic mapping to account for the disruptions brought by generative AI. To address this gap, this study identify annual research trends, identifies dominant topics, and evaluates the intellectual structure of AI literacy literature published between 2020 and 2025. Literature was retrieved from the Scopus database following PRISMA systematic screening guidelines and analyzed using VOSviewer for science mapping, Harzing’s Publish or Perish for citation metrics, and Microsoft Excel. A final dataset of 443 records was selected for bibliometric analysis. The analysis reveals an exponential growth in scholarly output, peaking in 2025 with 292 documents (65.91%). The United States (85 publications) and China (80 publications) are the leading research contributors, S.C. Kong is identified as the most prolific author (17 publications), and Computers and Education: Artificial Intelligence serves as the top publication source (25 publications). Analysis also demonstrates a central focus on educational technology and digital competency, with rapidly emerging themes including prompt engineering, academic integrity, large language models, AI ethics and teacher education. This study contributes to the literature by providing a longitudinal evaluation of the post-ChatGPT AI literacy landscape, offering an empirical foundation to inform future policy development and responsible AI integration frameworks.