This presentation showcases a next-generation, AI-powered enhancement layer for DSpace repositories, focused on two core areas: intelligent search and improved accessibility. Moving beyond traditional keyword-based discovery, the solution introduces AI-driven retrieval based on Retrieval-Augmented Generation (RAG), combining structured metadata, full-text content, and vector search to deliver accurate, context-aware answers grounded strictly in repository holdings. This approach improves relevance, supports multilingual and cross-language discovery, and scales to very large collections without compromising trustworthiness. In parallel, the presentation demonstrates AI-powered PDF processing designed to improve accessibility and reuse of repository content. Advanced OCR and document conversion pipelines transform complex or scanned PDFs into fully searchable, screen-reader-friendly, WCAG-aligned formats, significantly broadening access for users with disabilities and enabling downstream AI processing. Through short live demonstrations, architectural overviews, and real-world use cases, this presentation illustrates how DSpace repositories can evolve from passive storage systems into active, inclusive research discovery platforms – supporting FAIR principles, lowering barriers for new users, and responding pragmatically to the opportunities of emerging AI technologies.
Paszkowska et al. (Wed,) studied this question.