The Adoption of Artificial Intelligence Technologies in Academic Libraries: Challenges and Prospects for Sustainable Information Services

Abstract

The convergence of artificial intelligence with academic library practice represents one of the most consequential technological intersections in the history of information services. Globally, AI tools, including chatbots, machine learning cataloguing systems, recommendation engines, natural language processing (NLP) interfaces, intelligent search systems, and generative AI platforms, are progressively reshaping how academic libraries acquire, organise, preserve, and deliver information to scholars, students, and researchers. Yet the pace and equity of this transformation are deeply uneven: while leading institutions in North America, Europe, and parts of the Asia-Pacific are rapidly integrating sophisticated AI capabilities into their core service workflows, academic libraries in developing regions and particularly in sub-Saharan Africa, including Nigeria, face structural barriers that limit meaningful AI adoption and risk deepening the information access divide. This study examines the adoption of artificial intelligence technologies in academic libraries, with specific attention to the challenges and prospects for achieving sustainable information services. Grounded in four theoretical frameworks such as the Technology Acceptance Model (TAM), Diffusion of Innovations Theory, Socio Technical Systems Theory, and Responsible AI Theory, and drawing on a systematic review of peer-reviewed scholarship, policy frameworks, and institutional assessments published between 2020 and 2025, the study identifies and analyses the major strengths and weaknesses of AI in academic library settings, evaluates the policy frameworks governing AI deployment, and assesses the implications of AI adoption for information service sustainability. Findings reveal that while AI offers transformative prospects, including enhanced information retrieval, personalised user services, automated cataloguing, improved accessibility, and operational efficiency, its adoption is constrained by significant challenges including inadequate digital infrastructure, funding deficits, acute AI literacy gaps among library professionals, algorithmic bias and ethical risks, data privacy concerns, workforce displacement anxieties, and the absence of institution-specific AI governance policies. The study advances eight evidence-based policy recommendations for enabling sustainable, equitable, and ethically governed AI adoption in academic libraries.