Metadata Enrichment and Interoperability: Scoping Review of Artificial Intelligence Contexts in Metadata in Academic Libraries Experiences Across the Globe

Artificial Intelligence (AI) offers significant opportunities but requires librarian upskilling, responsible implementation, and ongoing evaluation of its impact on metadata ecosystems.This review (2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025) analyzes how AI is reshaping metadata creation, enrichment, interoperability, and access in academic libraries.Early research (2015-2019) focused on AI in digital library search engines and metadata extraction, while recent studies emphasize automation, GPT-based cataloguing, and interoperability frameworks.Key themes include automated metadata generation), metadata enrichment and structured archival description, and AI-driven discovery tools.Benefits cited are efficiency, scalability, and improved accuracy, though limitations include opacity of black-box models and inconsistency in outputs.Beyond technology, studies stress librarian readiness, metadata literacy, and AI competencies.Ethical and policy challenges-bias, transparency, and preservation standards (remain underexplored)..