Evaluating AI in Academic Libraries: A Systematic Literature Review on User Perceptions, Acceptance Factors and Service Effectiveness

This study presents a systematic literature review examining the use of artificial intelligence (AI) in academic libraries, focusing on three main dimensions: user perception, factors influencing technology acceptance, and service effectiveness. Following the PRISMA guidelines for systematic reviews, a thorough search was conducted in four major academic databases: Web of Science, Scopus, Emerald Insight, and Science Direct, using the search string "Artificial Intelligence" AND "Academic Library" AND "Effectiveness" AND "User Perception" AND "Acceptance." The initial search yielded 1,435 articles, which were then filtered based on predefined inclusion and exclusion criteria, resulting in a final analysis of 30 peer-reviewed studies published between 2024 and 2026. Quality assessment was carried out using criteria set for systematic literature reviews. The study's findings show that users' perceptions of AI in academic libraries are marked by excitement about increased efficiency in academic activities, along with concerns about accuracy, transparency, and reduced human interaction. Key factors influencing AI adoption include perceived usefulness, ease of use, trust in AI technology, algorithm literacy, and compatibility with existing library processes. Transformational leadership and institutional support emerge as critical organizational enablers for successful AI adoption. Service effectiveness analysis shows improvements in information retrieval, user support, and overall service functions, although challenges remain with complex queries and system integration. This review also identifies major barriers in terms of organizational, financial, and technical aspects in implementing AI, including inadequate infrastructure, high costs, insufficient training programs, and a lack of clear institutional policies. Ethical considerations, especially intellectual property rights, data privacy, algorithmic bias, and AI hallucinations, remain significant challenges. This review provides a comprehensive framework for understanding AI usage in academic libraries and offers actionable recommendations for library administrators, policymakers, and researchers.

Artificial intelligence, academic libraries, user perceptions, technology acceptance, service effectiveness

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