Purpose: This study aims to map the scientific landscape of the convergence between Knowledge Management and Artificial Intelligence through a comprehensive scientometric analysis. Specifically, it seeks to identify the conceptual structure, emerging research trends, key thematic clusters, and potential paradigm shift in this interdisciplinary domain. By examining the evolution of scholarly output from 2005 to 2025, the research provides an evidence-based overview of how AI technologies are reshaping traditional KM practices, while highlighting both opportunities and persistent gaps in the literature.
Methodology: Adopting a scientometric approach, this research analyzed 1,961 peer-reviewed documents (original articles and reviews in English) indexed in the Scopus database, covering the period from 2005 to 2025. Data retrieval was conducted in May 2026 using a carefully constructed search strategy in the Title-Abstract-Keywords fields: ("knowledge management") AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "transfer learning" OR "large language model" OR "federated learning" OR "explainable AI" OR "XAI" OR "generative AI" OR "knowledge graph" OR "natural language processing"). The process followed PRISMA guidelines across four stages: identification (2,742 initial records), screening by document type and language (603 removed), content-based eligibility screening of titles, abstracts, and keywords (178 removed), and final cleaning/standardization using OpenRefine. Performance analysis examined publication trends, leading countries, journals, authors, and citation patterns using descriptive statistics. Structural analysis employed VOSviewer (version 1.6.20, Full Counting method, minimum keyword occurrence of 5, Association Strength normalization) for keyword co-occurrence networks and thematic clustering. CiteSpace (version 6.2.6) was used for citation burst detection (γ = 0.6), temporal evolution, and emerging trend identification, with Pathfinder Network and pruning (1%) applied for network clarity. Quality metrics included Modularity Q (>0.7) and an average Silhouette score (approaching 1), with triangulation between the two tools to enhance validity.
Findings: The results reveal explosive growth in the field, with over 66% of publications concentrated in 2021–2025, reflecting the broader surge in AI research. Network analysis identified four major thematic clusters: (1) Decision-oriented KM processes (centered on Knowledge Management, Decision Making, AI, and Data Mining, with emerging ethics-related terms); (2) Deep Learning and Knowledge Extraction; (3) Transfer and Federated Learning; and (4) Optimization and Applications (including Reinforcement Learning and Neural Networks). "Knowledge Management" remains the central hub in the conceptual network, demonstrating deep integration with AI technologies, though technical clusters (deep learning, federated learning) dominate recent activity. Citation burst analysis highlighted Large Language Models (strength 9.1), Deep Learning (8.7), Transfer Learning (7.9), Federated Learning (7.2), Knowledge Distillation (6.8), and Explainable AI (5.9) as the most prominent emerging trends. Geographically, China, the United States, and India lead in publication volume. Leading journals include IEEE Access, Knowledge-Based Systems, and Expert Systems with Applications, indicating a strong technical orientation. While "Knowledge Management" maintains conceptual centrality, ethical and explainability-related keywords show increasing but still relatively lower presence, signaling a potential imbalance between technical advancements and human/governance considerations.
Conclusion: This scientometric study demonstrates that the convergence of Knowledge Management and Artificial Intelligence has matured into a vibrant, rapidly expanding interdisciplinary field. The results show clear evidence of exponential publication growth driven by advancements in deep learning, large language models, and federated approaches, alongside a stable central role for core KM concepts within an increasingly AI-augmented knowledge ecosystem. Four well-defined thematic clusters highlight both the continuity of traditional KM concerns (decision-making and knowledge processes) and the emergence of sophisticated technical solutions for knowledge extraction, transfer, and optimization. The identification of strong citation bursts in areas such as large language models, Explainable AI, and Federated Learning signals promising directions for future research and practice. These trends suggest that organizations can leverage AI to enhance innovation, decision-making speed, and competitive advantage, particularly through intelligent systems that support knowledge creation, sharing, and application at scale. Theoretically, the study provides an empirical foundation for advancing KM frameworks in the AI era, showing how classic models are being enriched and, in some cases, challenged by new technological capabilities. From a practical standpoint, the mapping offers strategic insights for managers, policymakers, and educators seeking to integrate AI effectively into knowledge-intensive processes. In summary, the convergence of KM and AI represents a significant evolution in how organizations create, manage, and utilize knowledge. This study delivers a robust, data-driven portrait of the field's current state and trajectory, serving as a valuable reference for both researchers and practitioners navigating this transformative intersection.