International Research Journal of Library and Information Sciences

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International Research Journal of Library and Information Sciences

Article Details Page

Transforming Digital Libraries: An Analysis of AI-Driven Service Enhancement and Implementation Challenges

Authors

  • Dr. Anya Sharma School of Information Sciences, Carnegie Mellon University, Pittsburgh, USA
  • Prof. Jian Li Department of Computer Science & Artificial Intelligence, Tsinghua University, Beijing, China

DOI:

https://doi.org/10.54640/

Keywords:

Artificial Intelligence, Digital Libraries, Machine Learning

Abstract

Purpose: This paper investigates the transformative role of Artificial Intelligence (AI) in enhancing the services and operations of modern digital libraries. As digital collections expand, libraries face significant challenges in information discovery, resource management, and user engagement. This study aims to systematically review and synthesize the primary applications of AI being deployed to address these challenges.

Methodology: The study employs a systematic literature review, analyzing peer-reviewed articles, technical reports, and case studies from leading institutions. The analysis is structured around key AI technologies—including Natural Language Processing (NLP), Machine Learning (ML), and computer vision—and their impact on distinct library functions.

Findings: The review identifies several key areas of AI-driven enhancement. For users, AI is improving information discovery through semantic search [10, 16], personalized recommender systems [12], and multilingual access [5, 13]. For library operations, AI is streamlining backend processes via automated metadata generation [6, 19] and advanced Optical Character Recognition (OCR) for digitization [18]. Furthermore, AI-powered chatbots are revolutionizing user support by providing instant, 24/7 virtual reference services [2, 8].

Conclusion: The integration of AI represents a paradigm shift for digital libraries, offering a suite of powerful tools to create more intelligent, responsive, and efficient services. However, successful implementation is contingent upon addressing significant challenges, including ethical considerations of algorithmic bias and data privacy [14], as well as practical hurdles related to cost and technical expertise [15]. A strategic, ethical, and user-centered approach is therefore essential for libraries to fully harness the transformative potential of AI.

References

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Brown, T. (2020). Implementing AI-Powered Chatbots in University Libraries: A Case Study of the University of Toronto. Library Hi Tech, 38(2), 287–299.

Chowdhury, G. G. (2010). Introduction to Modern Information Retrieval (3rd ed.). Facet Publishing.

Europeana Foundation. (2021). AI and Cultural Heritage: Opportunities and Challenges. Retrieved from https://pro.europeana.eu

Ghosh, M., & Singh, R. (2021). Enhancing Access to Multilingual Digital Resources through AI: The NDLI Experience. DESIDOC Journal of Library & Information Technology, 41(3), 192–198.

Gupta, P., & Varma, V. (2014). Automatic Metadata Generation in Digital Libraries using Machine Learning. Journal of Information Science and Technology, 8(1), 44–58.

Harmon, R., & King, D. (1995). Expert Systems in Libraries: A Historical Overview. Information Today.

Liu, X., & Li, J. (2019). Virtual Reference Services Using Artificial Intelligence in Academic Libraries. The Journal of Academic Librarianship, 45(5), 102–110.

National Digital Library of India (NDLI). (2023). AI Applications in Metadata and OCR. Retrieved from https://ndl.iitkgp.ac.in/

Stanford University Libraries. (2022). Semantic Search Enhancement Using AI: Technical Report. Stanford Digital Infrastructure.

University of Toronto Libraries. (2021). ASK AI Chatbot: Development and Deployment. Retrieved from https://onesearch.library.utoronto.ca

Zhang, Y., Zhao, J., & Lu, Y. (2016). AI-Based Recommender Systems in Academic Libraries: A User-Centric Approach. Library Management, 37(4), 225–240.

World Digital Library. (2020). Machine Learning for Multilingual Resource Discovery. UNESCO Publishing.

European Commission. (2020). Ethical Guidelines for Trustworthy AI. Retrieved from https://ec.europa.eu/digital-strategy

Kumar, V., & Jain, A. (2022). AI-Driven Library Services: Opportunities and Limitations in Indian Context. International Journal of Library and Information Studies, 12(1), 65–74.

Singh, S. (2023). The Role of NLP in Enhancing Library Search Systems. Library Trends, 71(3), 203–220.

Dutta, S., & Roy, B. (2020). Smart Libraries in Smart Cities: A Roadmap with Artificial Intelligence. International Journal of Information Science, 10(2), 37–45.

Tesseract OCR Project. (2024). Open-Source OCR Engine for Digitization. Retrieved from https://github.com/tesseract-ocr

GROBID Project. (2023). Machine Learning for Bibliographic Data Extraction. Retrieved from https://github.com/kermitt2/grobid

UNESCO. (2021). AI and the Future of Digital Knowledge Systems. Retrieved from https://unesdoc.unesco.org

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Published

2025-10-01

How to Cite

Transforming Digital Libraries: An Analysis of AI-Driven Service Enhancement and Implementation Challenges. (2025). International Research Journal of Library and Information Sciences, 2(10), 1-9. https://doi.org/10.54640/

How to Cite

Transforming Digital Libraries: An Analysis of AI-Driven Service Enhancement and Implementation Challenges. (2025). International Research Journal of Library and Information Sciences, 2(10), 1-9. https://doi.org/10.54640/

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