Enhancing Social Science Research in Library Discovery: The Application of Generative AI for Contextual and Exploratory Search

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dc.audienceAudience::Social Science Libraries Section
dc.contributor.authorCruz, Luis Ezra D.
dc.date.accessioned2025-06-10T11:45:25Z
dc.date.available2025-06-10T11:45:25Z
dc.date.issued2025-06-09
dc.description.abstractEnhancing Social Science Research in Library Discovery: The Application of Generative AI for Contextual and Exploratory Search by Mr. Luis Ezra D. Cruz (Manila, Philippines) Emerging models of information retrieval are reshaping how researchers in the social sciences navigate complex and interdisciplinary literature. As scholarly output grows in volume and complexity, traditional keyword-based search methods often fall short in meeting the needs of exploratory and context-driven inquiry. This prompts libraries to adopt AI-enhanced discovery tools that support more intuitive research workflows. This paper examines the Primo Research Assistant, a generative AI-supported tool integrated into the Primo-based discovery platform of an academic library in Southeast Asia. The tool is designed to facilitate exploratory search through natural language querying and contextualized content delivery. Operating within the Primo VE discovery layer, it enables users to pose research questions in natural language. Using a Retrieval-Augmented Generation (RAG) framework, it identifies and synthesizes content drawn from indexed academic sources in the Central Discovery Index (CDI). The resulting output presents a concise, structured overview derived from article abstracts, accompanied by inline citations and links to the full records, allowing users to verify and further explore the presented content. The paper focuses on the tool’s application in research contexts, particularly its role in supporting preliminary literature scanning, clarifying unfamiliar topics, and enabling associative discovery, an information behaviour commonly observed in social sciences scholarship. Use cases include users refining research questions, identifying entry points into emerging subject areas, and surfacing relevant materials when initial keyword strategies yield limited results. The tool also proves helpful in guiding users toward adjacent topics and concepts that may not have been part of their original query formulation. Preliminary observations indicate that the tool is valued for its ability to summarize dispersed content, reduce time spent navigating search results, and provide starting points for deeper inquiry. Users describe it as particularly useful in situations involving topic selection, scoping reviews, and initial background research.
dc.identifier.urihttps://youtu.be/EOReF_OsvQE?si=OvbRhmOIPyONryaA&t=1377
dc.identifier.urihttps://repository.ifla.org/handle/20.500.14598/4056
dc.language.isoen
dc.publisherInternational Federation of Library Associations and Institutions (IFLA)
dc.rights.holderLuis Ezra D. Cruz
dc.rights.licenseCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSocial sciences
dc.subjectArtificial intelligence
dc.subjectScientific research
dc.titleEnhancing Social Science Research in Library Discovery: The Application of Generative AI for Contextual and Exploratory Search
dc.typeEvents Material
ifla.UnitSection::Social Science Libraries Section
ifla.oPubId0

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