Application of artificial intelligence technologies in library services at the top 100 US universities

Abstract

Purpose Through a systematic content analysis, this paper aims to examine the current use of artificial intelligence (AI) in the services of the top 100 US university libraries and explores the key factors that influence the adoption of this technology. It seeks to provide actionable strategic recommendations for library administrators. These recommendations aim to help libraries accelerate their intelligent service transformation while maintaining their academic resource advantages and humanistic traditions, and to establish a solid theoretical and empirical foundation for future research. Design/methodology/approach The authors analyse the current status of libraries adopting AI technologies and applying them to patron services, which include intelligent retrieval services, intelligent reference services, intelligent guided tour services and intelligent ID recognition services, among others. Firstly, the authors visit the official websites of the top 100 US university libraries to collect information. Subsequently, the prevalence of AI technologies is statistically analysed, and several excellent practice cases are selected. Finally, based on the data obtained from the content analysis, the impact of university rankings on the adoption of AI technologies in US university libraries is analysed. Findings The study finds that AI technologies are widely applied in the services of US university libraries, but there are differences in the adoption rates across different services. Almost all libraries have adopted intelligent retrieval and recommendation technology (99%). In terms of intelligent reference services, more than half of the libraries have used AI technologies (53%). Intelligent guided tour technology is also widely adopted (95%). The application of intelligent ID recognition technology in libraries is equally common (98%). Furthermore, more than half of the libraries have begun to explore generative AI technologies (61%). Additionally, the study further discovers that there is a significant negative correlation between university rankings and the number of AI technologies adopted by libraries. This indicates that top-ranked universities are more likely to introduce a variety of AI technologies in their library services. Research limitations/implications The study’s implications include constructing an analytical framework to assess AI in library services and identifying key adoption factors. It explores AI’s impact on service diversity and the popularity of different services. Notably, it finds a link between university rankings and AI adoption, offering insights into the relationship between tech adoption and institutional reputation. Originality/value This paper provides the latest statistical data on the use of AI technologies in US university libraries. It aims to help librarians understand the overall application situation and best practices. With these insights, librarians can develop corresponding plans to better use AI to serve patrons.

Ähnliche Arbeiten