CHALLENGES IN THE USE OF ARTIFICIAL INTELLIGENCE FOR INFORMATION MANAGEMENT AMONG LIBRARIANS IN NIGERIA
Article Sidebar
Main Article Content
Abstract
This study examines the challenges faced by librarians in a Nigerian university regarding the adoption of Artificial Intelligence (AI) for information management. It addresses how systemic and contextual factors influence perceptions of AI’s usefulness and ease of use. A qualitative case study approach was employed. Data was selected from semi-structured interviews with 12 librarians from various functional areas within the library at a mid-sized agricultural university in Nigeria. Data were collected from January to March 2025 and the study was underpinned by the Technology Acceptance Model (TAM) and the Information Systems Success Model (ISSM). Thematic analysis was applied to identify key patterns. Findings indicate that while librarians perceive AI as useful for automating routine tasks such as cataloguing, indexing, and handling frequently asked questions, significant barriers such as inadequate digital infrastructure, insufficient training, and bureaucratic procurement processes hinder effective adoption. Specifically, participants noted a steep learning curve and a reliance on donor-driven training initiatives which led to fragmented skill development. Systemic issues like unreliable electricity and high internet costs further constrained AI implementation. The systemic issues point to the relationship between technological readiness and institutional support. The results lay emphasis on the fact that perceived usefulness and ease of use are interdependent, and are influenced by systemic quality and contextual realities unique to Nigeria. The study reframes AI adoption from a purely technological challenge to a socio-technical phenomenon that is co-determined by information management practices, institutional funding priorities, and policy frameworks; and, in doing so, it offers what can be described as a correction for literature that tends to overstate the potential of AI in information management in libraries by neglecting these systemic interdependencies.
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Revista MEDIACIONES © 2024 by Corporación Universitaria Minuto de Dios - UNIMINUTO is licensed under the Creative Commons BY.
How to Cite
References
Abba, T. (2024). Use of artificial intelligence technologies in rendering library services: An empirical evidence from university libraries in Africa. African Journal of Library, Archives and Information Science, 34(1), 23-35.
Adams, W. C. (2015). Conducting semi-structured interviews. Handbook of practical program evaluation, 492-505.
Al-Hawamleh, A. (2024). Exploring the satisfaction and continuance intention to use e-learning systems: An integration of the information systems success model and the technology acceptance model. International Journal of Electrical and Computer Engineering Systems, 15(2), 201-214.
Alala, A. G., Uzoaru, G. C., & Odikwa, N. H. (2024). Advantages and challenges of implementing artificial intelligence in academic library services. Int’l Journal of Education Research and Scientific Development, 5(1), 15-15.
Annamalah, S. (2024). The value of case study research in practice: A methodological review with practical insights from organisational studies. Journal of Applied Economic Sciences (JAES), 19(4 (86)), 485-498.
Barnett, A. J., Baggio, J. A., Shin, H. C., Yu, D. J., Perez-Ibarra, I., Rubiños, C., ... & Janssen, M. A. (2016). An iterative approach to case study analysis: insights from qualitative analysis of quantitative inconsistencies. International Journal of the Commons, 10(2).
Barsha, S., & Munshi, S. A. (2024). Implementing artificial intelligence in library services: a review of current prospects and challenges of developing countries. Library Hi Tech News, 41(1), 7-10.
Barsha, S., & Munshi, S. A. (2024). Implementing artificial intelligence in library services: a review of current prospects and challenges of developing countries. Library Hi Tech News, 41(1), 7-10.
Boddy, C. R. (2016). Sample size for qualitative research. Qualitative market research: An international journal, 19(4), 426-432.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.
Braun, V., & Clarke, V. (2021). To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualitative research in sport, exercise and health, 13(2), 201-216.
Chen, X. (2023). ChatGPT and its possible impact on library reference services. Internet Reference Services Quarterly, 27(2), 121-129.
Echedom, A. U., & Okuonghae, O. (2021). Transforming academic library operations in Africa with artificial intelligence: Opportunities and challenges: A review paper. New Review of Academic Librarianship, 27(2), 243-255.
Gbadebo, A. D. (2024). Application of artificial intelligence and machine learning in academic libraries. International Journal of Social and Educational Innovation (IJSEIro), 11(22), 184-201.
Goodman, R. S., Patrinely, J. R., Stone, C. A., Zimmerman, E., Donald, R. R., Chang, S. S., ... & Johnson, D. B. (2023). Accuracy and reliability of chatbot responses to physician questions. JAMA network open, 6(10), e2336483-e2336483.
Gupta, V. (2025). From hype to strategy: Navigating the reality of experimental strategic adoption of AI technologies in libraries. Reference Services Review, 53(1), 1-14.
Huang, Y., Cox, A. M., & Cox, J. (2023). Artificial Intelligence in academic library strategy in the United Kingdom and the Mainland of China. The Journal of Academic Librarianship, 49(6), 102772.
Khan, A. U., Rafi, M., Zhang, Z., & Khan, A. (2023). Determining the impact of technological modernization and management capabilities on user satisfaction and trust in library services. Global Knowledge, Memory and Communication, 72(6/7), 593-611.
Khan, A. U., Rafi, M., Zhang, Z., & Khan, A. (2023). Determining the impact of technological modernization and management capabilities on user satisfaction and trust in library services. Global Knowledge, Memory and Communication, 72(6/7), 593-611.
Lim, W. M. (2025). What is qualitative research? An overview and guidelines. Australasian Marketing Journal, 33(2), 199-229.
Mohapatra, P. S. (2025). Artificial Intelligence and Machine Learning for Test Engineers: Concepts in Software Quality Assurance. Intelligent Assurance: Artificial Intelligence-Powered Software Testing in the Modern Development Lifecycle, 4, 17.
Nkomo, S., & Matli, W. (2025). Adoption of artificial intelligence for library services at universities in Africa: are we there yet? South African Journal of Libraries and Information Science, 91(2), 1-12.
Nookala, G. (2024). Adaptive data governance frameworks for data-driven digital transformations. Journal of Computational Innovation, 4(1).
Ogungbenro, O. D., Esse, U. C., Olowoporoku, I., & Christopher, A. (2025). Revolutionizing Library Services: The Impact of Artificial Intelligence on Cataloguing and Access to Information in Nigeria Academic Libraries. Journal of Library Metadata, 25(2), 99-118.
Okunlaya, R. O., Syed Abdullah, N., & Alias, R. A. (2022). Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education. Library Hi Tech, 40(6), 1869-1892.
Orubebe, E. D., Oloniruha, E. A., & Oladokun, B. D. (2024). Adoption and utilization of artificial intelligence in academic libraries: Challenges and opportunities in developed and developing nations. International Journal of Knowledge Content Development & Technology, 14(3).
Padilla, T. (2019). Responsible Operations: Data Science, Machine Learning, and AI in Libraries. OCLC Research Position Paper. OCLC Online Computer Library Center, Inc. 6565 Kilgour Place, Dublin, OH 43017.
Panda, S., & Chakravarty, R. (2022). Adapting intelligent information services in libraries: A case of smart AI chatbots. Library Hi Tech News, 39(1), 12-15.
Praveenraj, D., Agarwal, K., & Singh, V. (2025). Artificial Intelligence Applications in Modern Library Services. Library of Progress-Library Science, Information Technology & Computer, 45(1), 1-11.
Rahman, M. H., Ahmad, A. B., & Mohamed Sawal, M. Z. H. B. (2025). The influence of personal factors on resistance to technology adoption in university libraries in Bangladesh. Information Development, DOI: 02666669241257196.
Rahman, M. H., Ghazali, A. B. M., & Sawal, M. Z. H. B. M. (2025). Exploring organizational factors of resistance to technology adoption in university libraries in Bangladesh. Information Development, 02666669251325447.
Sheikh, A., Malik, A., & Adnan, R. (2025). Evolution of research data management in academic libraries: A review of the literature. Information Development, 41(2), 305-319.
Shivadekar, S. (2025). Cognitive Artificial Intelligence for Health and Climate: Deep Models, Interpretability, and Decision Support. Deep Science Publishing.
Sreenivasan, A., & Suresh, M. (2024). Design thinking and artificial intelligence: A systematic literature review exploring synergies. International Journal of Innovation Studies, 8(3), 297-312.
Tella, A., Akanmu Odunola, O., & WO, L. (2023). Cataloguing and classification in the era of artificial intelligence: Benefits, and challenges from the perspective of cataloguing librarians in Oyo State, Nigeria. Vjesnik bibliotekara Hrvatske, 66(1), 159-176.
Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The SAGE handbook of qualitative research in psychology, 2(17-37), 25.
Tewksbury, R. (2009). Qualitative versus quantitative methods: Understanding why qualitative methods are superior for criminology and criminal justice. Journal of theoretical and philosophical criminology, 1(1), 4.
Tight, M. (2017). Understanding case study research: Small-scale research with meaning. Sage.
Tripathi, A. (2024). The Impact of Artificial Intelligence on Library Services and Information Management. Library Philosophy & Practice. (e-journal).
Tsang, E. W. (2014). Generalizing from research findings: The merits of case studies. International Journal of Management Reviews, 16(4), 369-383.
Umar, J. T., Vijesh, P. V., & Yohannan, S. (2024). Empowering education in Africa: The future of academic libraries with artificial intelligence–A comprehensive review. Rajagiri Journal of Social Development, 16(1), 30-40.
Xu, Y. S., Yanti Idaya, A. M. K., & Kassim, M. S. S. (2025). How Do AI Technologies Augment Intelligent Information Services in Libraries? A Deep Learning-Based Topic Modeling and Temporal Analysis. Education for Information, 42(1), DOI: 01678329251374501.
Yusuf, S., & Ibrahim, M. A. (2024). Educational services in Nigerian universities: Prospect, challenges and way forward. Fuoye Journal of Educational Management, 1(1).
Zondi, N. P., Epizitone, A., Nkomo, N., Mthalane, P. P., Moyane, S., Luthuli, M., ... & Phokoye, S. (2024). A review of artificial intelligence implementation in academic library services. South African Journal of Libraries and Information Science, 90(2), 1-8.