IGeLU 2025

Conference

Understanding user search behavior over time is essential for optimizing library discovery services. This session presents findings from a year-long study analyzing user interactions with Primo VE, our library’s discovery system. Using generative AI tools, we examined a representative sample of search queries, categorizing them by subject classification, query type (e.g. author, title, subject/concept), academic discipline, and search intent. By applying AI-driven analysis, we transformed raw search data into actionable insights, automating classification, uncovering hidden trends, and generating data-driven recommendations to enhance user experience. Our findings reveal insights into user needs, offering opportunities to refine discovery services and guide collection development. By sharing our methodology and key findings, this session will demonstrate the transformative potential of AI in academic libraries and showcase how these technologies can be used to better understand and meet user needs.

User Experience / Discovery

Analytics / Mixpanel
Primo / Primo VE