Artificial Intelligence Adoption in Academic Libraries in Nigeria: Opportunities, Challenges and Prospects

Abstract

Background: The integration of artificial intelligence (AI) in academic libraries remains at nascent stages, particularly in developing economies. Understanding librarians' adoption patterns, institutional readiness, and perceived opportunities and challenges is crucial for strategic planning. Purpose: This study examines the level and extent of AI adoption in academic libraries, identifies key AI applications, explores perceived opportunities and challenges, and assesses librarians' readiness for AI integration. Method: A quantitative descriptive survey design was employed. The study population comprised librarians and library professionals from 15 academic libraries across Nigeria's North-East geopolitical zone (n = 127, 89.4% response rate). Data were collected using a structured questionnaire with eight sections (Likert-scale items, Section A–H) and analysed using descriptive statistics (frequency, percentage, mean, standard deviation) and inferential statistics (Pearson correlation, independent t-tests, one-way ANOVA). Instrument validity was established through expert validation and content validity index (CVI = 0.89); reliability was confirmed using Cronbach's alpha (α = 0.81). Results: Current AI adoption rates remain low (mean = 2.34/5.0), with limited AI applications in catalogue management (mean = 2.41/5.0) and reference services (mean = 1.98/5.0). Significant positive correlations emerged between librarians' AI skills and adoption willingness (r = 0.67, p < 0.001) and between institutional support and adoption rates (r = 0.58, p < 0.001). Major opportunities identified include enhanced information retrieval (mean = 4.23/5.0) and improved user experience (mean = 4.18/5.0); critical challenges included inadequate infrastructure (mean = 4.41/5.0), limited funding (mean = 4.37/5.0), and insufficient AI skills (mean = 4.29/5.0). Conclusion: Strategic institutional investment, targeted capacity-building programmes, and supportive institutional policies are essential to accelerate AI adoption. Recommendations prioritise AI literacy training for librarians, infrastructure development, institutional AI frameworks, and collaboration between academic libraries and technology partners. These findings contribute evidence-based insights for policymakers, university management, and LIS educators navigating AI integration in academic library environments.

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