AI-Enabled digital content management in academic libraries: A systematic literature review of practices, policies, and challenges

The digital content boom in university libraries, together with growing reliance on artificial intelligence, has introduced new functionalities and governance risks. This study aims to provide evidence on artificial intelligence-enabled digital content management in academic libraries. Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 served as the reporting framework; reporting items unsupported by the archived methodological record are disclosed as limitations. A set of 41 peer-reviewed publications, published from 2010 to 2025, was identified in key academic databases and reviewed to explore the evolution of the research agenda, policy environment, thematic concerns, and critical issues surrounding artificial intelligence-based digital content governance. The results demonstrate that the volume of research has grown tremendously since 2021, with a heavy concentration in developing regions, especially Africa and Asia. The literature outlines significant thematic focus areas, such as artificial intelligence-generated metadata, digital preservation, smart libraries, institutional repositories, legal preparedness, and artificial intelligence governance. Nevertheless, there were also some ongoing problems, including ineffective governance systems, inadequate funding, infrastructural constraints, skills gaps, ethical and privacy concerns, metadata quality issues, and legal uncertainties. It is interesting to note that extensive artificial intelligence-related policies and integrated governance models are still underrepresented in institutional settings. The present systematic review pulls together and presents the scattered academic evidence and illuminates the important gaps in governance, policy formulation, ethical principles, and institutional preparedness in artificial intelligence-enabled digital content management. The results highlight the necessity of policy-related, context-relevant, and evidence-based models to support the sustainable, ethical, and legally appropriate application of artificial intelligence in academic libraries.