An assessment of the use of AI-powered machine learning algorithms for library classification in Bauchi State University Library, Bauchi State
- Project Research
- 1-5 Chapters
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- Table of Content: Available
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- NGN 5000
Background of the study
Library classification—organizing materials into subject categories—is foundational for resource discovery and retrieval. Traditional classification systems rely on human catalogers applying standardized schemes such as Dewey Decimal or Library of Congress classifications (Borgman, 2023). AI‑powered machine learning algorithms, including supervised classifiers and deep learning models, can automate subject assignment by learning from labeled training data, potentially increasing consistency and throughput (Nguyen, 2024). Pilot implementations at universities in Europe have achieved classification accuracy rates above 90%, reducing cataloging time by 40% (Perez, 2024). In Nigerian academic libraries, however, the complexity of local research topics and limited labeled datasets present challenges for algorithm training and performance (Eze, 2025). At Bauchi State University Library, an AI classification prototype trained on existing catalog records has been trialed, yet its impact on catalog quality, error rates, and staff acceptance remains unquantified. Key considerations include algorithm transparency, handling of interdisciplinary materials, and integration with the library management system (Smith, 2023). This study evaluates the performance of AI classifiers on a representative corpus of library holdings, compares machine‑assigned classifications to expert cataloging, and examines librarians’ perceptions of reliability and workflow implications (Okoro, 2025). Results will guide the adoption of machine learning solutions that enhance classification efficiency while preserving subject accuracy.
Statement of the problem
Despite the promise of AI classification, Bauchi State University Library lacks empirical evidence on algorithm accuracy, error patterns, and librarian trust. Manual cataloging remains resource‑intensive, and without assessment, the library cannot justify shifting to AI‑driven classification.
Objectives of the study
To measure the accuracy of AI‑powered classification against expert‑assigned subjects.
To analyze error types and sources in machine classifications.
To assess librarian perceptions of AI classification reliability and usability.
Research questions
What is the accuracy rate of AI‑based classification compared to human cataloging?
Which subject areas or material types exhibit the highest classification errors?
How do librarians perceive the benefits and risks of AI classification?
Significance of the study
Findings will inform strategic decisions on automating cataloging workflows, optimizing training data sets, and developing guidelines that balance AI efficiency with subject expertise, ultimately improving resource discoverability at Bauchi State University Library.
Scope and limitations of the study
This assessment focuses on AI classification of monographs and journal articles within Bauchi State University Library’s collection. It excludes special collections and multimedia materials.
Definitions of terms
Supervised classifier: Machine learning model trained on labeled examples to predict categories.
Cataloging workflow: Sequence of steps librarians follow to create and maintain bibliographic records.
Interdisciplinary materials: Resources that span multiple subject areas, complicating classification.
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