EVALUATION ON THE ROLE OF ARTIFICIAL INTELLIGENCE IN KNOWLEDGE DISCOVERY AND INFORMATION RETRIEVAL IN ACADEMIC LIBRARIES.

1.1 Background to the Study Across the globe, academic libraries are undergoing a transformative shift catalyzed by the proliferation of digital technologies and artificial intelligence (AI). Traditionally perceived as static repositories of books and journals, academic libraries are now increasingly dynamic spaces where technology intersects with scholarship to facilitate learning, research, and innovation. The integration of AI into library systems reflects this paradigm shift, offering sophisticated tools for automating information retrieval, curating knowledge repositories, and supporting personalized learning pathways (Tenopir, Sandusky, Allard & Birch, 2020). In high-income countries, AI-driven platforms such as semantic search engines, recommender systems, and machine learning algorithms are redefining how users access and interact with information. These innovations enhance efficiency, reduce cognitive load, and enable deeper engagement with scholarly content. In the context of developing nations like Nigeria, the deployment of AI in academic libraries remains relatively emergent. However, the imperative for modernization is no less pressing. Nigeria’s tertiary institutions are under increasing pressure to meet global academic standards, improve information access, and align with contemporary research trends. According to Okike and Adetoro (2022), the traditional cataloguing and retrieval systems used in many Nigerian academic libraries often fall short in terms of speed, accuracy, and user satisfaction. These constraints limit students’ and researchers’ ability to explore relevant literature effectively, thereby affecting the quality of academic output. AI applications in libraries—including natural language processing (NLP), automated indexing, and intelligent retrieval—hold immense potential to overcome these limitations by enhancing information discovery and user experience. Information retrieval is the cornerstone of academic research, and the role of AI in refining this process is particularly significant. Through algorithms that mimic human cognitive patterns, AI facilitates intelligent searching, enabling users to locate relevant resources without relying solely on keywords or Boolean logic. Recommender systems such as those embedded in academic databases like Scopus, Web of Science, or Google Scholar employ AI to suggest related literature based on user behavior, citation patterns, and search history. These tools enhance scholarly exploration and provide pathways for interdisciplinary research, a feature especially critical in knowledge-intensive fields (Khan, 2021). Thus, AI is not only optimizing access to knowledge but also redefining its discovery process by making it more intuitive and personalized. Moreover, the application of AI extends beyond retrieval to knowledge discovery—a process that involves extracting implicit patterns, relationships, and insights from vast data corpora. Academic libraries, as custodians of institutional knowledge, can leverage AI to analyze user queries, track research trends, and generate visual insights that guide academic inquiry. For example, through machine learning and data mining, AI tools can identify emerging research themes, map citation networks, and detect knowledge gaps within disciplines. According to Nwosu and Usoro (2023), such capabilities are instrumental in supporting strategic research planning and academic forecasting in universities. The combination of retrieval efficiency and discovery depth positions AI as a strategic asset in the evolving mission of academic libraries. Despite these promises, implementation in Nigeria remains patchy. Challenges such as inadequate digital infrastructure, limited technical expertise, and low awareness of AI capabilities continue to impede widespread adoption in university libraries. While some private institutions have piloted AI-assisted library systems, public universities often lag due to funding constraints and bureaucratic inertia. As Olalekan and Egbokhare (2021) observe, this uneven technological uptake risks widening the digital divide within the academic community and weakening the role of libraries in scholarly development. Consequently, there is a pressing need for empirical studies that assess the actual impact of AI in academic library environments, particularly in the Nigerian context. This study, therefore, seeks to evaluate the role of AI in enhancing information retrieval and knowledge discovery in academic libraries, with a specific focus on its implementation, benefits, challenges, and implications for academic excellence. By situating the inquiry within Nigerian university libraries, the research aims to contribute to the discourse on digital transformation in education and propose evidence-based strategies for effective AI integration in the library sector. 1.2 Statement of the Problem While academic libraries in advanced economies have harnessed AI to streamline services, improve access to scholarly materials, and enhance knowledge discovery, many Nigerian university libraries still operate with manual or semi-digital systems that hinder optimal information delivery. The inefficiency in traditional retrieval mechanisms—marked by poor indexing, outdated catalogues, and user frustration—continues to impede the timely acquisition of relevant academic materials (Adeniran & Ajayi, 2021). Students and faculty often face difficulties navigating fragmented search systems, leading to underutilization of library resources and compromised research quality. Several studies have explored the impact of ICT on educational delivery, student performance, and digital literacy (Chukwu & Obaro, 2020; Lawal & Okwuonu, 2022), yet there remains a gap in empirical literature that directly investigates the influence of AI applications in library operations, particularly in the domain of job creation, service efficiency, and cognitive development. Moreover, existing research tends to focus narrowly on infrastructure readiness or user perception, without a holistic examination of AI’s role in knowledge structuring, discovery, and personalized retrieval. This gap becomes more evident in contexts like Nigeria where resource allocation and digital policy frameworks are still evolving. Against this backdrop, this study is motivated by the need to assess the current state and future potential of AI integration in academic libraries within Nigeria. The investigation will explore the nature of AI tools available, their usage patterns, user outcomes, and institutional readiness. The findings are expected to inform not only academic practice and policy but also contribute to a broader understanding of how AI can foster inclusive, efficient, and innovation-driven knowledge ecosystems in African universities. 1.3 Objectives of the Study 1. To examine the types of artificial intelligence technologies currently deployed in academic libraries in Nigeria. 2. To investigate how AI enhances information retrieval efficiency among students and academic staff. 3. To assess the role of AI in supporting knowledge discovery and research productivity. 4. To identify the challenges limiting the adoption of AI technologies in academic library environments. 1.4 Research Questions 1. What AI technologies are currently used in academic libraries in Nigeria? 2. In what ways does AI improve information retrieval among library users? 3. How does AI contribute to knowledge discovery in academic research? 4. What are the institutional challenges associated with AI adoption in academic libraries? 1.5 Research Hypotheses H₀₁: There is no significant relationship between the use of AI and the efficiency of information retrieval in academic libraries. H₀₂: The application of AI does not significantly influence knowledge discovery among academic library users. 1.6 Significance of the Study This study offers timely insights for library science professionals seeking to modernize services and enhance academic support. By evaluating the use and impact of AI, the research contributes to professional discourse on digital innovation and best practices in library management. For academic librarians, the findings may serve as a guide for integrating AI solutions that align with institutional goals and user needs. Policymakers and education administrators also stand to benefit. In an era where national development is tightly linked to digital competencies, understanding how AI can be harnessed for knowledge delivery is crucial. The study provides empirical evidence that can inform digital policy, budget prioritization, and infrastructure planning for tertiary institutions. At the societal level, the research underscores the relevance of AI in promoting equitable access to knowledge, bridging information gaps, and fostering academic excellence. By highlighting opportunities for scalable AI solutions in libraries, it contributes to the discourse on technological inclusion and intellectual empowerment. 1.7 Scope of the Study The study is geographically situated within selected academic libraries in Nigerian public and private universities, with an emphasis on Lagos State due to its relative digital advancement. The population includes library personnel, academic staff, and students who actively engage with library technologies. The research is conceptually delimited to three key variables: artificial intelligence applications (independent), information retrieval efficiency (dependent), and knowledge discovery (dependent). The scope does not extend to commercial or public libraries. Instead, it focuses on the academic context where the impact on research and instruction is most direct. 1.8 Operational Definition of Terms Artificial Intelligence (AI): AI refers to computer systems capable of performing tasks that typically require human intelligence, including machine learning, language processing, and decision-making in academic information systems. Information Retrieval: The process through which users locate, access, and utilize relevant academic resources from digital or physical library collections, often enhanced by search algorithms or indexing systems. Knowledge Discovery: The identification and extraction of meaningful patterns, themes, and insights from academic data and literature, typically facilitated by AI techniques such as data mining and recommendation engines. Academic Library: A specialized information center within an institution of higher learning, responsible for acquiring, organizing, and disseminating scholarly materials for teaching, learning, and research purposes.