Mapping Research Directions in AI and Records Management: A Bibliometric Study on Thematic Evolution and Strategic Trends (2001-2025) - Yuliansah; Herman Setyawan; Ahmad Sukri Bin Haji Abdul Kadir (D3 Perpustakaan Fakultas Vokasi Universitas Airlangga, 2026)
Background of the study: The emergence of Artificial Intelligence (AI) has changed the social landscape of organizations. However, despite its promised potential, its application in records management still faces significant challenges, highlighting the importance of bibliometric mapping in identifying knowledge gaps. Purpose: This study provides a comprehensive knowledge map of AI-records management integration by 1) tracing publication trends; 2) identifying key contributing authors, institutions, and countries; 3) mapping research themes and gaps; and 4) proposing future research directions. Method: A bibliometric study was conducted using R Studio and Biblioshiny. Data were collected from Scopus covering 2001–September 2025 (n = 32 2 documents) and analyzed with thematic mapping, callons density, MCA, co-citation/co-authorship network analysis, and Alluvial diagram. Findings: 1) Publication trends increased sharply from 2017 to September 2025; 2) China and the USA dominate authorship and publications; 3) key motor themes are information retrieval, machine learning and temporal analysis reveals healthcare and digital preservation as growing trends; 4) future research directions including algorithmic bias, AI integration in health records management, and AI ethics and fairness. Conclusion: This study differs from previous bibliometric studies by covering nearly 25 years of publications and interpreting the findings through the records life cycle framework. Practically, the findings can be used as a reference for practitioners and policymakers in developing more responsible AI implementation strategies in records management.