Navigating the landscape of AI literacy education: insights from a decade of research (2014–2024)

As artificial intelligence (AI) becomes increasingly integrated into various fields, the need to enhance learners’ AI literacy is more urgent than ever. Despite its growing importance, a comprehensive review of AI literacy education research has been lacking. This study addresses that gap by mapping the current landscape, tracing its evolution, and identifying key themes through a bibliometric analysis of research from 2014 to 2024, utilizing CiteSpace for data visualization and analysis. This study systematically selected 335 relevant articles from databases, including Web of Science Core Collection, Scopus, and Science Direct, following PRISMA guidelines. Our methodology involved keyword co-occurrence mapping to trace the development paths and thematic evolution within the field. By examining publication trends and thematic clusters, we provide insights into the progression and focal points of AI literacy education research over the past decade. The study reveals three key insights. First, AI literacy education research has shifted from an exploratory phase to rapid growth, with a marked increase in publications. Second, four distinct developmental trajectories have emerged, emphasizing the interdisciplinary nature of the field and its connections to information, digital, and algorithmic literacy. Third, nine prominent research themes have been identified, with data literacy, machine learning, AI literacy, the technology acceptance model, and computational thinking as focal points. These themes highlight AI’s evolving role, particularly in education, and shifts in research priorities. This review provides a comprehensive understanding of AI literacy education’s evolution and its implications for education, ethics, and society. As AI continues to influence modern education and industry, this analysis serves as a valuable resource for researchers, educators, and policymakers navigating the complex intersection of AI and literacy.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2025-03-16
DOI
https://doi.org/10.1057/s41599-025-04583-8
Citations
39
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
66.05
Controls
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article

Navigating the landscape of AI literacy education: insights from a decade of research (2014–2024)

Daner Sun, Yantao Wei, Wenmeng He, Yuqin Yang et al.
39 citations
Humanities and Social Sciences Communications
Online Learning and Analytics
66.05
article

Navigating the landscape of AI literacy education: insights from a decade of research (2014–2024)

Daner Sun, Yantao Wei, Wenmeng He, Yuqin Yang, Ying Zhang
article en
39 citations

Abstract

As artificial intelligence (AI) becomes increasingly integrated into various fields, the need to enhance learners’ AI literacy is more urgent than ever. Despite its growing importance, a comprehensive review of AI literacy education research has been lacking. This study addresses that gap by mapping the current landscape, tracing its evolution, and identifying key themes through a bibliometric analysis of research from 2014 to 2024, utilizing CiteSpace for data visualization and analysis. This study systematically selected 335 relevant articles from databases, including Web of Science Core Collection, Scopus, and Science Direct, following PRISMA guidelines. Our methodology involved keyword co-occurrence mapping to trace the development paths and thematic evolution within the field. By examining publication trends and thematic clusters, we provide insights into the progression and focal points of AI literacy education research over the past decade. The study reveals three key insights. First, AI literacy education research has shifted from an exploratory phase to rapid growth, with a marked increase in publications. Second, four distinct developmental trajectories have emerged, emphasizing the interdisciplinary nature of the field and its connections to information, digital, and algorithmic literacy. Third, nine prominent research themes have been identified, with data literacy, machine learning, AI literacy, the technology acceptance model, and computational thinking as focal points. These themes highlight AI’s evolving role, particularly in education, and shifts in research priorities. This review provides a comprehensive understanding of AI literacy education’s evolution and its implications for education, ethics, and society. As AI continues to influence modern education and industry, this analysis serves as a valuable resource for researchers, educators, and policymakers navigating the complex intersection of AI and literacy.

Humanities and Social Sciences CommunicationsVol. 12(1)
Wuhan University of Technology (CN), Central China Normal University (CN), Education University of Hong Kong (HK), Hubei University of Technology (CN)
Quality Education
Openalex Percentile: Top 0%
Online Learning and Analytics
66.05
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