Artificial Intelligence and Knowledge Management: A Bibliometric Analysis of Global Research Trends

Abstract

In recent years, the convergence of Artificial Intelligence (AI) and Knowledge Management (KM) has gained increasing relevance due to the acceleration of digital transformation across sectors. The global pandemic, advances in machine learning, and the demand for rapid knowledge-based decision-making have driven researchers and practitioners to explore AI as a strategic enabler in knowledge-intensive environments. This study aims to map global research developments on the integration of AI in KM by conducting a comprehensive bibliometric analysis. The specific objectives are to identify (1) publication trends and the most cited works in the field, (2) prolific authors, countries, and institutions involved in AI-KM research, and (3) dominant themes and collaboration networks in scholarly discourse. The study used bibliometric analysis of 209 Scopus-indexed articles published from 2015 to 2025. Using the Biblioshiny tool in R, the study analyzed metadata such as publication year, citation metrics, co-occurrence of keywords, and factorial mapping to identify intellectual structures and emerging research domains. Results show a significant surge in publications since 2020, influenced by increased adoption of AI technologies in knowledge systems. The most prominent themes include AI for decision-making, personalized digital learning environments, and educational technologies such as gamification and virtual reality. China, the United States, and the United Kingdom emerge as key contributors with strong international collaboration networks. The study concludes that AI-driven KM research has evolved into an interdisciplinary field with expanding academic and practical relevance. This research offers a strategic overview for scholars, educators, and policymakers aiming to understand how AI is reshaping the landscape of knowledge generation, sharing, and application in digital ecosystems.