Abstract
Background and Objectives: : This study aimed to analyze the intellectual structure and map international research on artificial intelligence in knowledge management.
Methodology: It employed a scientometric approach combining scientific performance analysis with bibliographic network analysis. Research articles published between 1989 and 2026 were systematically examined. Data were analyzed using VOSviewer, focusing on co-authorship, co-citation, and term co-occurrence networks.
Findings: The findings showed that 2026 recorded the highest number of publications, with 49 documents, while China contributed the largest share of scientific output. Examination of document titles and abstracts identified knowledge management, artificial intelligence, expert systems, generative artificial intelligence, large language models, machine learning, and knowledge sharing as frequently occurring concepts. Terms referring to generative artificial intelligence appeared in 38 documents, expert systems in 18, and large language models in 16. A temporal comparison of document content revealed an expansion from expert systems, intelligent agents, and decision support in earlier periods toward applications of generative artificial intelligence, large language models, and ChatGPT in recent years. All documents containing terms referring to generative artificial intelligence and large language models in their titles or abstracts were published between 2023 and 2026. This pattern indicates the growing prominence of generative and language-based technologies in recent knowledge management research.
Discussion: Overall, the resulting map provides an overview of the intellectual foundations, scholarly collaboration networks, and thematic directions of artificial intelligence in knowledge management and can inform the identification of research gaps and future research priorities.