Artificial Intelligence and Machine Learning in Libraries

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Authors: Rajashree R. Patil
Cited by: 0

Abstract

Abstract: The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) is transforming various sectors. Library services impacted a lot from modern technologies like Ai & ML. Technologies offer innovative solutions to long-standing challenges in libraries. AI, with its ability to emulate human cognition, and ML, which allows systems to learn and improve from data, are reshaping how libraries operate and engage with users. AI-driven systems are automation routine tasks like cataloguing, metadata generation, and resource classification, reducing the workload of library staff and increasing efficiency. ML algorithm further personalizes the user experience by recommending resources. Artificial intelligence is 0ne of the emerging technologies of this phase. AI is an extensively used technology in library services that can transform the best services in the era of information technology in library services that can transform the best services in the era of information technology. This. papers aims to highlight the impact of Ai on library services. Several researches have been undertaken on this subject, but they only address a few applications. Ai and libraries have a substantial nexus, nevertheless, the user awareness and impact on academic scholars of AI in library services are still creating question marks addressing in this paper. This study will help the policy stakeholders, librarians, and scholars in the field to address the issues before the deployment of Ai in library services. The integration of these technologies also raises ethical concerns, particularly regarding data privacy, algorithmic bias, and potential job displacement. Libraries must navigate these challenges carefully to ensure that AI and ML systems are implemented transparently and responsibly.

Fields of Study & Subjects

Library Science and Information Web and Library Services Artificial Intelligence Applications

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
28 February 2026
Language
EN