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

Author

Listed:
  • Yuqin Yang

    (Central China Normal University)

  • Ying Zhang

    (Central China Normal University)

  • Daner Sun

    (The Education University of Hong Kong)

  • Wenmeng He

    (Wuhan University of Technology)

  • Yantao Wei

    (Central China Normal University)

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.

Suggested Citation

  • Yuqin Yang & Ying Zhang & Daner Sun & Wenmeng He & Yantao Wei, 2025. "Navigating the landscape of AI literacy education: insights from a decade of research (2014–2024)," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-04583-8
    DOI: 10.1057/s41599-025-04583-8
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