From resistance to readiness: AI anxiety in academic libraries through a bibliometric outlook

Abstract

Purpose AI has rapidly changed academic libraries with applications like chatbots, automated cataloging, classification and data-driven decision-making. Artificial intelligence (AI) anxiety is a new type of technostress that library services are experiencing as a result of the rapid adoption of AI technologies, even with its advantages of library. The purpose of this study was to conduct a comprehensive study of existing research on AI anxiety in academic libraries and analyze it using bibliometrics. Design/methodology/approach The study employs the bibliometrics and content analysis approach, which uses both numerical and visualization techniques to identify the extant research retrieved from the Web of Science and Scopus databases from 1996 to 2025. Author and country collaboration networks, hotspot distribution clustering and historical citation networks associated with AI anxiety were visualized by VOS viewer and R Studio. Findings A total of 128 literature were eventually included. The number of AI anxiety publications has increased drastically since 2023. Growth trends, geographical area, leading journals and authors are identified. Top documents are indicated, with main and trending topics, which provide suggestions for researchers who conduct research in this field. Numerous studies on AI anxiety in academic libraries have been published in high-impact sources. Originality/value To the best of our knowledge, this is the first comprehensive and in-depth bibliometric research of trends and developments on AI anxiety literature. This study offers useful insights for policymakers, library professionals and researchers by identifying unexplored areas and outlining a strategy for reducing AI anxiety in academic library environments.

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