“Artificial Intelligence Applications in Academic Libraries: Transforming Information Services and User Engagement in the Digital Era”
DOI:
https://doi.org/10.63300/arjst0503062026.03Keywords:
Artificial Intelligence, Academic Libraries, Library and Information Science, Machine Learning, Natural Language Processing, Chatbots, Virtual Assistants, Expert Systems, Recommendation SystemsAbstract
The twenty-first century technological revolution has accelerated the transformation of academic libraries from traditional information repositories into dynamic, intelligent knowledge ecosystems. This study examines the conceptual framework, evolutionary trajectory, and major applications of Artificial Intelligence (AI) within Library and Information Science (LIS), while critically evaluating its impact on information services, operational efficiency, and user engagement. Utilizing a comprehensive review of literature spanning from early mechanical automation to contemporary smart environments, the paper traces how AI acts as a pivotal driver of institutional innovation.
Core AI technologies transforming modern library infrastructure are analyzed, including Machine Learning (ML) for automated metadata generation, Natural Language Processing (NLP) for semantic search, AI-powered chatbots for round-the-clock reference assistance, expert systems, personalized recommendation engines, and robotics for physical inventory automation. The findings demonstrate that while these integrated technologies significantly enhance information discovery, deliver context-aware user experiences, and facilitate data-driven collection development, their implementation faces critical moderating challenges. These include steep financial constraints, technological infrastructure deficits, algorithmic bias, data privacy hazards, and an urgent demand for advanced digital competencies among library professionals. Ultimately, this study offers strategic recommendations for navigating these ethical and structural barriers, emphasizing that the sustainable future of LIS depends on deploying responsible, human-centered AI frameworks that remain deeply aligned with the core tenets of knowledge preservation and equitable information access.
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