Abstract
Over the past decade, academic libraries around the world have focused on developing new services to support data-intensive research and open science. This shift has been driven by funder and publisher mandates emphasizing transparency, reproducibility, and FAIR (Findable, Accessible, Interoperable, Reusable) research l data. Just as these services are maturing, the rapid rise of generative AI tools and their application in research presents a disruptive l force, but also a transformative opportunity for library data services. This paper addresses the evolving impact of generative AI and library data services, contextualized by the experiences of researchers captured in a series of interviews with faculty at the University of Victoria. Researchers interviewed spanned disciplines and l career stages, and held a range of perspectives on campus data services and the introduction of AI in their fields. The application of generative AI in academic research is often criticized for opacity, hallucinations, and biases, which challenges the principles of transparency and rigor underpinning open science and FAIR data management practices. Thus, some researchers see AI as a bridge to digital skills development, while others worry about its impact on foundational research competencies. Nonetheless, the shift to increasingly data l intensive research is only growing. Applied appropriately, generative AI offers the potential to l add capacity to library data services and improve data management practices of researchers. Opportunities include augmenting metadata creation, automating routine data management tasks, and supporting software documentation — critical and time-consuming components of sustainable and reproducible research. Ultimately, the growing prominence of AI in research requires greater data governance. Libraries will need to respond by accelerating staff development, and balancing innovation with respect for academic integrity.
Keywords
libraries, data services, generative AI
Date of this Version
11-2025
Recommended Citation
Shahira Khair,
"Generative AI as Disruptor and Driver for Library Data Services."
Proceedings of the IATUL Conferences.
Paper 5.
https://docs.lib.purdue.edu/iatul/2025/breakout/5
Paper
Included in
Generative AI as Disruptor and Driver for Library Data Services
Over the past decade, academic libraries around the world have focused on developing new services to support data-intensive research and open science. This shift has been driven by funder and publisher mandates emphasizing transparency, reproducibility, and FAIR (Findable, Accessible, Interoperable, Reusable) research l data. Just as these services are maturing, the rapid rise of generative AI tools and their application in research presents a disruptive l force, but also a transformative opportunity for library data services. This paper addresses the evolving impact of generative AI and library data services, contextualized by the experiences of researchers captured in a series of interviews with faculty at the University of Victoria. Researchers interviewed spanned disciplines and l career stages, and held a range of perspectives on campus data services and the introduction of AI in their fields. The application of generative AI in academic research is often criticized for opacity, hallucinations, and biases, which challenges the principles of transparency and rigor underpinning open science and FAIR data management practices. Thus, some researchers see AI as a bridge to digital skills development, while others worry about its impact on foundational research competencies. Nonetheless, the shift to increasingly data l intensive research is only growing. Applied appropriately, generative AI offers the potential to l add capacity to library data services and improve data management practices of researchers. Opportunities include augmenting metadata creation, automating routine data management tasks, and supporting software documentation — critical and time-consuming components of sustainable and reproducible research. Ultimately, the growing prominence of AI in research requires greater data governance. Libraries will need to respond by accelerating staff development, and balancing innovation with respect for academic integrity.