The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction

In this journal we examined how Artificial Intelligence (AI) is used to transform the use of libraries in the academic institutions in Nairobi. As the need to run efficient library services rises, AI technologies, including automated cataloging systems, chatbots based on AIs, and intelligent recommendation engines are slowly introduced to managing libraries. The research question is to ascertain the level of AI penetration at the libraries in Nairobi, analyze how to overcome existing challenges of operations, and determine the ethical issues of integrating AI. Desktop research was carried out with the help of which secondary data contained in peer-reviewed articles, institution reports, and the case studies of 2022-2025 were studied. Central conclusions affirm that, although AI can improve the way libraries operate both in terms of efficiency and user interaction, issues like limited infrastructure, data security concern, and lack of skills can still be the impediments to realizing the widespread implementation. Also, with AI, user satisfaction has been shown to jump up dramatically under personalized user interactions. The research can be used to add value to the AI adoption literature in developing economies, and it gives information on the ethical and practical needs libraries incur. The recommendations that can be made to library practitioners, policymakers, and researchers involve training initiatives, standards of ethics, and policy formulation, which help in adopting AI and making its adoption a success in the academic libraries of Nairobi.

Artificial Intelligence, Library Services, Adoption, AI, Personalization, User-Interaction, Data-Privacy, Ethical-Implications, Nairobi-Libraries.

1. Kasprzik, A. (2023). Automating subject indexing at ZBW: Making research results stick in practice. LIBER Quarterly, 33(1), 1–25. https://doi.org/10.53377/lq.13579 [Google Scholar] [Crossref]

2. Poley, C., Güntsch, A., Hoppe, M., Junger, U., Kahl, M., Kempf, J., Löffler, J., Meissner, T., & Voß, J. (2025). Automatic subject cataloguing at the German National Library. LIBER Quarterly, 34(1), 1–28. https://doi.org/10.53377/lq.19422 [Google Scholar] [Crossref]

3. Molaudzi, A. I., & Ngulube, P. (2025). Use of artificial intelligence innovations in public academic libraries in South Africa. IFLA Journal. Advance online publication. https://doi.org/10.1177/03400352241301780 [Google Scholar] [Crossref]

4. Idhalama, O. U., Anyaoku, E. N., & Echezona, R. I. (2025). Assessing the application of artificial intelligence and machine learning in library services in Africa. Library Management. Advance online publication. https://doi.org/10.1108/LM-07-2024-0078 [Google Scholar] [Crossref]

5. Masinde, J. M., Wambiri, D. M., & Mugambi, F. (2024). Exploring the current landscape of AI adoption in Kenyan academic libraries. In Proceedings of the 17th International Conference on Theory and Practice of Electronic Governance (ICEGOV 2024) (pp. 1–8). ACM. https://doi.org/10.1145/3680127.3680156 [Google Scholar] [Crossref]

6. Sang, L. J. (2024). Adopting artificial intelligence in Kenyan academic libraries: Analyzing through the technology–organization–environment framework. Library Management. Advance online publication. https://doi.org/10.1108/LM-03-2024-0029 [Google Scholar] [Crossref]

7. Olawore, K., McTear, M., & Bi, Y. (2025). Development and evaluation of a university chatbot using deep learning: A RAG-based approach. In C. Følstad, T. Araujo, E. Luger, & P. Papadopoulos (Eds.), Chatbots and Human-Centered AI. CONVERSATIONS 2024. Lecture Notes in Computer Science (Vol. 15545, pp. 88–102). Springer. https://doi.org/10.1007/978-3-031-88045-2_7 [Google Scholar] [Crossref]

8. Chilufya, E. M., Arvola, M., Severinsson, S., Martín Bylund, A., Stenliden, L., Mortazavi, A., Lindh, J., & Ziemke, T. (2025). The BookBot project: Conceptual design of a social robot facilitating reading motivation. In C. Følstad, T. Araujo, E. Luger, & P. Papadopoulos (Eds.), Chatbots and Human-Centered AI. CONVERSATIONS 2024. Lecture Notes in Computer Science (Vol. 15545, pp. 123–140). Springer. https://doi.org/10.1007/978-3-031-88045-2_9 [Google Scholar] [Crossref]

9. Modiba, M. (2024). Application of conversational GPT for improvement of information services in academic libraries. South African Journal of Libraries and Information Science, 90(1), 1–12. https://doi.org/10.7553/90-1-2384 [Google Scholar] [Crossref]

10. Rabatseta, P. C., Modiba, M., & Ngulube, P. (2024). Utilisation of AI for the provision of information services at the University of Limpopo libraries. South African Journal of Libraries and Information Science, 90(2), 1–15. https://doi.org/10.7553/90-2-2394 [Google Scholar] [Crossref]

11. Litsalia, A. S. (2025). Data literacy: An output-focused model to guide Kenyan academic libraries. International Information & Library Review, 57(2), 137–150. https://doi.org/10.1080/10572317.2024.2444829 [Google Scholar] [Crossref]

12. Walela, P., & Ngila, J. (2024). Investigating HEMIS pre-implementation factors in Kenyan universities. South African Journal of Information Management, 26(1), e1898. https://doi.org/10.4102/sajim.v26i1.1898 [Google Scholar] [Crossref]

13. Huang, Y., Cox, A. M., & Cox, J. (2023). Artificial intelligence in academic library strategy in the United Kingdom and Mainland China. The Journal of Academic Librarianship, 49(6), 102772. https://doi.org/10.1016/j.acalib.2023.102772 [Google Scholar] [Crossref]

14. Dover, A. (2025). Artificial intelligence through the lens of the Cataloguing Code of Ethics. Cataloging & Classification Quarterly. Advance online publication. https://doi.org/10.1080/01639374.2025.2544137 [Google Scholar] [Crossref]

15. Zondi, N. P., Epizitone, A., Nkomo, N., Mthalane, P. P., Moyane, S., Luthuli, M., Khumalo, M., & Phokoye, S. (2024). A review of artificial intelligence implementation in academic library services. South African Journal of Libraries and Information Science, 90(2). https://doi.org/10.7553/90-2-2399 [Google Scholar] [Crossref]

16. Monyela, M., & Tella, A. (2024). Leveraging artificial intelligence for sustainable knowledge organisation in academic libraries. South African Journal of Libraries and Information Science, 90(2). https://doi.org/10.7553/90-2-2396 [Google Scholar] [Crossref]

17. Otike, F., Hajdu Barát, Á., & Kiszl, P. (2024). The puzzle of innovation and innovation strategies in Kenyan academic libraries. Digital Library Perspectives, 40(3), 453–483. https://doi.org/10.1108/DLP-01-2024-0013 [Google Scholar] [Crossref]

18. Ajwang, S. O., & Ikoha, A. P. (2024). Publish or perish in the era of artificial intelligence: Which way for the Kenyan research community? Library Hi Tech News, 41(6), 7–11. https://doi.org/10.1108/LHTN-04-2024-0065 [Google Scholar] [Crossref]

19. Al-Sobhi, A., Al-Kahtani, N., & Al-Dosary, A. (2022). Evaluating e-government implementation and Task-Technology Fit in Saudi Arabia. Government Information Quarterly, 39(2), 101-113. https://doi.org/10.1016/j.giq.2022.101412 [Google Scholar] [Crossref]

20. Baker, M., & Patel, K. (2022). Exploring library self-service technology adoption through Task-Technology Fit theory. Library & Information Science Research, 44(3), 177-185. https://doi.org/10.1016/j.lisr.2022.101439 [Google Scholar] [Crossref]

21. Chen, R., & Lin, T. (2022). Examining personalized e-learning systems with Expectation–Confirmation Model of IS Continuance. Computers in Human Behavior, 124, 106913. https://doi.org/10.1016/j.chb.2021.106913 [Google Scholar] [Crossref]

22. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 [Google Scholar] [Crossref]

23. Hussain, I., Bano, Z., & Khan, F. (2023). Task–Technology Fit for healthcare AI: Evaluating performance outcomes in clinical settings. Journal of Healthcare Engineering, 2023, 1–13. https://doi.org/10.1155/2023/7109321 [Google Scholar] [Crossref]

24. Jiang, L., Wang, F., & Sun, X. (2023). The evolution of Task–Technology Fit theory in the digital age. International Journal of Information Management, 68, 101-109. https://doi.org/10.1016/j.ijinfomgt.2023.102211 [Google Scholar] [Crossref]

25. Johnson, S., & Lee, R. (2022). Improving user experience in library systems through the IS Success Model: A case study on intelligent search engines. Library Management, 43(8), 755–768. https://doi.org/10.1108/LM-05-2022-0153 [Google Scholar] [Crossref]

26. Khan, F., Ali, S., & Abdullah, A. (2022). Subjective dimensions of system use: A critique of the IS Success Model in the context of AI systems. Information Systems, 96, 19-33. https://doi.org/10.1016/j.is.2022.102149 [Google Scholar] [Crossref]

27. Liu, Y., Zhang, Y., & Wang, X. (2023). Expectation–Confirmation Model of IS Continuance: Application to AI-driven chatbots in e-learning. Journal of Educational Computing Research, 61(3), 461–477. https://doi.org/10.1177/07356331221118984 [Google Scholar] [Crossref]

28. Nguyen, H. P., & Le, D. H. (2022). Implementation of AI-driven systems in academic libraries: Benefits and challenges. Library Management, 43(3), 212–226. https://doi.org/10.1108/LM-01-2022-0156 [Google Scholar] [Crossref]

29. Nayak, S., Das, A., & Saha, R. (2022). Post-adoption behavior of AI users in library systems: A critical review of ECM-IS applications. Library Management, 43(9), 119–130. https://doi.org/10.1108/LM-03-2022-0208 [Google Scholar] [Crossref]

30. Sakib, N., & Reza, A. (2022). Knowledge management and AI in decision-making: A review of recent trends in information systems. International Journal of Information Management, 60, 56–68. https://doi.org/10.1016/j.ijinfomgt.2022.102353 [Google Scholar] [Crossref]

31. Smith, G., Turner, M., & Williams, H. (2023).Enhancing metadata quality in digital libraries through artificial intelligence. Information Processing & Management, 60(4), 101-111. https://doi.org/10.1016/j.ipm.2023.102315 [Google Scholar] [Crossref]

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