Artificial Intelligence and Data Science in Academic Libraries: Framework, Competencies, and Adoption Challenges among Library Professionals

The accelerating integration of Artificial Intelligence (AI) and data science into academic knowledge systems has created an urgent imperative for library professionals to reassess their competency frameworks, service models, and institutional readiness. Drawing conceptually on the International Federation of Library Associations and Institutions (IFLA) Big Data Special Interest Group framework and synthesising evidence from Q1-ranked, Elsevier, and Scopus-indexed publications (2018-2025), this paper examines the theoretical foundations and practical dimensions of AI and data science adoption in academic libraries. Three research questions guided the study: the level of AI and data science awareness among library professionals; the nature of data science services currently offered or feasibly deployable in academic libraries; and the key barriers to adopting AI-driven services. A conceptual-analytical research design was employed, integrating systematic literature synthesis with a structured competency mapping exercise. Findings reveal that while foundational data science awareness is growing, significant skills gaps persist- particularly in library analytics, text and data mining, and AI-driven patron services. A hypothesis is advanced that academic librarians' digital literacy competency levels are significantly and positively associated with their institutional readiness to adopt AI and data science frameworks. The paper concludes with a multi-dimensional adoption framework organised around three pillars: Skills, Operations, and Services, and proposes evidence-based recommendations for embedding data science into the professional DNA of librarianship.