AI 4 RDM: Exploring AI for Research Data Management Support at KU Leuven Libraries

At KU Leuven, Research Data Management Support is organised as a collaborative network, coordinated by the RDM Competence Centre. Within this network, the library plays an important role: its staff provide training for researchers, curate comprehensive RDM webpages, assist via an RDM helpdesk, manage the institutional data repository, organise RDM-related events, review data management plans, and the library heads the RDM Competence Centre. In addition, we have been exploring how AI can enhance these RDM support services. This presentation will share our approach, progress, and key insights from the AI4RDM project. While researchers are increasingly aware of the importance of sound research data management, RDM is still often viewed as an administrative burden taking away valuable research time. It also appears that researchers often struggle to find the right support channels, resulting in only a small proportion of our 14,000 researchers and 7,000 PhDs accessing the library’s RDM support staff and materials. Recognizing AI’s potential to deliver personalized information and improve scalability, we have begun exploring how AI can be used to enhance our RDM support services since September 2024. In this presentation, we will detail four (pilot) projects, each focusing on a specific aspect of AI integration within RDM-support workflows. Our first project, in collaboration with the Research Coordination Office and the context of a master thesis in the advanced master programme of Artificial Intelligence, explored using AI to automate the review of Data Management Plans (DMPs). Rather than manually reviewing all 700 DMPs each year, we aimed to improve efficiency by automatically categorizing them and only manually reviewing those needing feedback. A key finding was the need for structured domain knowledge. This knowledge, currently distributed across various unstructured sources, was at risk of no longer being compliant with the recently updated KU Leuven RDM policy. Since manually reviewing all materials would be resource-intensive, our second project, in partnership with the master of digital humanities, uses AI to identify conflicting documentation. While the first two projects focus on improving internal RDM support workflows and documentation, the third and fourth projects are intended to benefit researchers more directly. The third project involves developing an AI agent with access to our RDM documentation and evaluating its potential as a chatbot on the KU Leuven RDM website to further strengthen researchers’ RDM self-reliance. The fourth project examines researchers’ workflows, assessing which AI tools are currently used for research data management, evaluating their compliance with KU Leuven regulations, and identifying further needs that could be addressed with tailored solutions. In summary, this presentation explores the potential use of artificial intelligence within Research Data Management support at KU Leuven. By sharing our experiences across four distinct projects, we aim to highlight the practical opportunities and challenges of integrating AI into RDM workflows. We hope our findings will contribute to ongoing discussions about AI’s role in academic support services and offer useful insights for institutions facing similar challenges.