Generative AI in Academic Libraries: A Systematic Review of AI-Mediated Reference Services, Librarian Roles, and User Trust

Abstract

Introduction: Generative Artificial Intelligence (AI), and in particular large language models such as ChatGPT, is being gradually integrated into the systems and workflows of academic libraries. Consequently, it is transforming the nature of reference services, reshaping the work of librarians, and influencing the information-seeking behaviour of users. This systematic review aimed to explore the impact of generative AI on the quality of reference services, restructuring of librarian tasks, and trust of users in academic libraries. Methodology: A systematic search of Scopus and ScienceDirect was conducted to identify relevant literature. A total of 13 major empirical studies, published between 2023 and 2026 were selected and analysed that included experimental assessments, cross-sectional surveys, qualitative interviews, mixed-methods research and quasi-experimental audit studies across different geographic locations. Results: The findings revealed a significant performance of AI-mediated reference services but generative AI can only be applied to low-complexity questions. However, it demonstrated severe limitations in responding to complex research questions, contextual questions, and citation verification, as indicated by high rates of hallucinations and bibliographic errors. However, user trust was also conditional, depending on the perceived utility, transparency, and the institutionalisation of AI services. Conclusion: Generative AI should be understood as a socio-technical system, in which the efficacy is determined by human control, governance systems, and correlation with the fundamental values, which are the foundation of academic libraries. Thus, this study emphasises that, instead of replacing librarians, generative AI restructures professional practices in favour of mediation, verification, ethical oversight, and the delivery of AI literacy education.