This study investigates the application of Artificial Intelligence (AI) in academic libraries through a bibliometric analysis of 354 publications indexed in Scopus from 2010 to 2023. Using Bibliometrix, VOS Viewer, and MS Excel, it examines publication trends, influential authors, sources, geographical contributions, and thematic evolution. The findings reveal a surge in AI-related research in recent years, with significant contributions from scholars like Cox AM and Stock WG. Collaborative research accounts for 75% of publications, indicating its high impact. The Journal of Academic Librarianship emerges as the leading source, while China, the USA, and India lead in publication output. Key research themes include AI integration in library services, data mining, and user personalization, with notable collaborations between Indonesia-Malaysia and Nigeria-South Africa. A thematic analysis identifies artificial intelligence, machine learning, and chatbots as pivotal areas. The study underscores the interdisciplinary nature of AI research in academic libraries and its growing influence on enhancing library operations, resource discovery, and user experiences. By identifying research gaps and future directions, the findings provide valuable insights for library professionals and policymakers, enabling more strategic AI adoption to improve efficiency, automate routine tasks, and enhance decision-making in library management.

This research embarks on a bibliometric journey to delineate the evolution, trends and thematic territories of artificial intelligence (AI) applications in library science from 2018 to 2022. Utilising Scopus as the foundational data source, we employed a suite of bibliometric instruments – citation analysis, collaboration network examination and thematic mapping – to dissect publication dynamics, authorship patterns, and thematic progressions within the domain. Our exploration reveals an ascending trajectory in AI research outputs, spotlighting pivotal advancements in information retrieval, knowledge organisation and user-centric services in libraries. The analysis underscores the imperative for interdisciplinary collaboration, spotlighting how it fuels the progression of AI in library science, with ethical considerations and the anticipation of longitudinal impacts forming crucial research vectors. We unearthed significant research clusters, identifying emergent themes that promise to shape the future discourse of AI applications in library contexts. Notably, our findings advocate for a paradigm shift towards integrating AI to navigate the challenges of digital information management, enhance user engagement, and foster innovative service delivery in libraries. This study, through its comprehensive bibliometric analysis, not only enriches the theoretical discourse surrounding AI’s role in transforming libraries but also delivers practical insights for librarians, researchers, and policymakers. It charts a strategic course for future investigations, emphasising the importance of embracing emerging AI technologies to sustain the relevance and efficiency of libraries in the digital age. This research contributes to the ongoing dialogue on the transformative potential of AI in libraries, offering a lens through which future research directions and strategic decisions can be discerned.

Purpose: This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.Design/methodology/approach: This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.Findings: The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.Originality/value: This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.

This column aims to explore the potential of big data analytics in improving library management in Indonesia and identify the challenges, opportunities, and best practices for libraries when applying big data analytics. Using of literature review of published studies, this column examines the current state of big data analytics in library management in Indonesia and provides recommendations, including a SWOT analysis, for implementing big data analytics in operations. It also highlighted several challenges such as limited resources, limited access to technology and digital literacy among librarians, and limited data security measures which could put sensitive patron data at risk.