Home⇛PAARL Research Journal⇛vol. 11 no. 1 (2025)

Application of Generative AI to Support Contextual and Exploratory Search in a Library Discovery Service

Luis Ezra D. Cruz

Discipline: library and information science

 

Abstract:

The rapid growth of scholarly output and interdisciplinary research has made it increasingly difficult for scholars to locate, evaluate, and connect with relevant materials. This often leads to information overload and the risk of overlooking important studies. Integrating artificial intelligence into library discovery services provides an opportunity to enhance query comprehension, accuracy, and overall search effectiveness. To address the limits of traditional keyword searching, a university library in the Philippines (Institution A) integrated Primo Research Assistant into its Primo VE platform in January 2025. This case study evaluated the tool’s potential to support contextual and exploratory academic searching. A heuristic evaluation was conducted by three academic librarians over a three-week period using Jakob Nielsen’s 10 Usability Heuristics. The evaluation found that the system handled straightforward queries effectively, produced academically framed summaries, and presented results in a clean, intuitive interface. Strengths included visibility of system status, clarity of presentation, and recognition-based navigation. However, recurring issues were noted in the handling of complex queries, limited support for exploratory search, vague error feedback, and the absence of user documentation. While most issues were minor, several moderate concerns may reduce efficiency and limit adoption. Overall, Primo Research Assistant showed promise as a complement to a traditional library discovery service. Improvements in query interpretation, contextual sensitivity, exploratory features, and user support are needed to strengthen its role in academic research workflows.



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