Bibliometric Study and Visualization Analysis of Domestic and International Information Literacy Research in the Era of Artificial Intelligence

This study employs bibliometric and visualization analysis to investigate the landscape of information literacy research in the era of artificial intelligence (AI), comparing trends and characteristics across domestic and international contexts. Utilizing data from the Web of Science and CNKI databases, the analysis reveals a consistent upward trajectory in research output for both domains, with notable peaks reflecting technological advancements and policy initiatives. The research themes demonstrate a shift from early focuses on information retrieval and knowledge organization to more recent explorations of AI literacy, social media, and user engagement. International research highlights interdisciplinary collaboration, particularly between library science, computer science, and education. In contrast, domestic research emphasizes the integration of AI technologies within library automation, information service innovation, and smart library development. Author and institutional collaboration networks reveal a preference for university-based partnerships, with international collaborations extending across diverse disciplinary backgrounds. While both domains exhibit similar trends in collaboration patterns, the absence of certain high-publishing countries from international collaboration networks warrants further investigation. Research hotspots reflect the growing influence of AI on information literacy, with international research exploring fear appeals, social media, and deep learning, while domestic research focuses on deep learning applications, smart libraries, and bibliometrics.

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Publication Details

Journal
International Journal of Librarianship
Published
2024-09-20
DOI
https://doi.org/10.23974/ijol.2024.vol9.3.408
Citations
5
Primary Topic
Medical Research and Treatments
Type
article
Field-Weighted Citation Impact
3.58
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article

Bibliometric Study and Visualization Analysis of Domestic and International Information Literacy Research in the Era of Artificial Intelligence

Shuyi Wang, Bo Sun, Jingpeng Zhang, Changhong Wu et al.
5 citations
International Journal of Librarianship
Medical Research and Treatments
3.58
article

Bibliometric Study and Visualization Analysis of Domestic and International Information Literacy Research in the Era of Artificial Intelligence

Shuyi Wang, Bo Sun, Jingpeng Zhang, Changhong Wu, Jiatong Zou
article en
5 citations

Abstract

This study employs bibliometric and visualization analysis to investigate the landscape of information literacy research in the era of artificial intelligence (AI), comparing trends and characteristics across domestic and international contexts. Utilizing data from the Web of Science and CNKI databases, the analysis reveals a consistent upward trajectory in research output for both domains, with notable peaks reflecting technological advancements and policy initiatives. The research themes demonstrate a shift from early focuses on information retrieval and knowledge organization to more recent explorations of AI literacy, social media, and user engagement. International research highlights interdisciplinary collaboration, particularly between library science, computer science, and education. In contrast, domestic research emphasizes the integration of AI technologies within library automation, information service innovation, and smart library development. Author and institutional collaboration networks reveal a preference for university-based partnerships, with international collaborations extending across diverse disciplinary backgrounds. While both domains exhibit similar trends in collaboration patterns, the absence of certain high-publishing countries from international collaboration networks warrants further investigation. Research hotspots reflect the growing influence of AI on information literacy, with international research exploring fear appeals, social media, and deep learning, while domestic research focuses on deep learning applications, smart libraries, and bibliometrics.

International Journal of LibrarianshipVol. 9(3)
Northeast Normal University (CN)
Openalex Percentile: Top 8%
Medical Research and Treatments
3.58
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