Artificial Intelligence in Library and Information Science Research: Trends, Citation Patterns, and Knowledge Structure Analysis

Artificial Intelligence (AI) and Machine Learning (ML) are changing Library and Information Science (LIS) by impacting on user services, cataloguing, and information retrieval; however, the literature on how this is happening has not been systematically catalogued. To fill this gap, this study was conducted using a bibliometric analysis of the papers pertaining to AI, ML and LIS published from 2015 to 2024. The aim of the study is to analyse the growth of publication, citation trends, characteristics of authors, keyword co-occurrence networks, and international collaboration in this field to discover the intellectual structures and future trends of the field. A total of 2180 documents from 436 sources were retrieved by applying a structured keyword search strategy, after removing the duplicates and the irrelevant documents, the retrieved documents were analysed with the help of the Bibliometrix package in RStudio and VOSviewer for descriptive bibliometric analysis and network-based bibliometric analysis respectively. Results indicate an increase at an annual rate of 43.16%, with only 2024 reporting 707 publications or 32.43% of the total. The data set contained 7,623 authors, each of whom authored an average of 3.84 co-authors per document, and only 71 documents (3.26%) had a single author, showing a very collaborative research culture. The most common and most cited author keywords were “machine learning” (7,633 citations), “deep learning” (4,182), and “learning systems” (3,272). The document with the most citations garnered 351 citations, with India becoming the top among the contributing countries and the largest node in the international co-authorship networks. From these results, it can be inferred that AI/ML research in LIS is growing rapidly, is collaborative in nature and has a higher focus on machine learning and deep learning applications. Based on the study, the researcher concludes that this bibliometric mapping will be a baseline reference for researchers, librarians and policy makers to understand the evolution of AI and ML in LIS and to identify emerging research areas like generative AI, explainable AI and intelligent digital libraries.