Decoding AI Literacy: A Bibliometric Analysis of Global Research Trends, Thematic Evolution, and Future Directions

This study presents a comprehensive bibliometric analysis of AI literacy research to examine current trends, theoretical frameworks, and scholarly contributions in this emerging field. Data were collected from the SCOPUS database using "AI Literacy" as the search term, focusing on English-language journal articles published between January 2019 and February 2025. The analysis identified 436 scholarly articles across 248 journals, demonstrating an annual growth rate of 82.95%. The temporal analysis revealed that 2024 was the most productive year with 278 publications (63.76%), while 2025 maintained this momentum with 75 articles in just two months. Citation analysis indicated that articles from 2020 achieved the highest average citations per article (127.67) and per year (21.28), while recent publications from 2024-2025 showed lower citation metrics due to insufficient time for citation accumulation. Journal analysis identified three primary sources: Computers and Education: Artificial Intelligence (34 articles, H-index 16), Education and Information Technologies (23 articles), and Computers and Education Open (11 articles). Bradford's Law analysis confirmed 18 core journals in the field. The most productive researcher was SU J. with 11 publications, while NG DTK's 2021 article in Computers and Education: Artificial Intelligence received the highest citations (470). Co-word analysis revealed three thematic clusters: (1) Blue cluster - focusing on artificial intelligence, students, ChatGPT, and educational technology; (2) Red cluster - emphasizing AI literacy, curriculum, and generative AI; and (3) Green cluster - centered on human-related aspects including demographic factors. Thematic mapping positioned artificial intelligence, human, and literacy as motor themes, indicating high density and centrality within the research landscape. These findings suggest that AI literacy research represents an expanding field with substantial future potential, though competency-based perspectives remain underexplored and require immediate scholarly attention.