From Data Entry to Discovery: AI-Driven Transformation in Research Information Management

Author(s)
Digital Science (United Kingdom)
Keywords

research information ...

automated metadata pr...

artificial intelligen...

Symplectic

responsible AI

Issue Date
May 20, 2026
Publisher
euroCRIS
Type
Conference Paper
Abstract
The landscape of Research Information Management (RIM) is undergoing a signifi cant paradigm shift driven by the integration of Artifi cial Intelligence (AI). Institutions and national consortia face persistent challenges in maintaining complete researcher profi les, reducing administrative burden, and eff ectively communicating research impact to external audiences. This presentation explores how AI technologies are being deployed within Current Research Information Systems (CRIS), specifi cally Symplectic Elements, to address the "manual entry barrier" and the accessibility of complex research outputs.
We will examine the evolution of workfl ow automation, from the current capability of using Large Language Models (LLMs) to extract structured metadata from free-form text snippets, such as CV entries, award letters, and pre-publication manuscripts. Furthermore, we will analyse best practices for intelligent metadata enrichment, demonstrating how AI-generated plain-language summaries can maximize the discoverability of research by non-specialists. Finally, the session will outline a rigorous framework for "Responsible AI," focusing on human-in-the-loop governance, duplicate detection, and resource management.
Description
22 slides.-- Presentation delivered within CRIS2026 session "AI for Metadata, Workflows, and Operational Research Information Management".-- Includes extended abstract
URI
https://dspacecris.eurocris.org/handle/11366/10293
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