Open Access Peer-reviewed Research Article

Behavioural Intention and Readiness for AI Adoption among Lecturers in Northwest Nigerian Universities

Main Article Content

Usman Abubakar corresponding author
Samuel Adenubi Onasanya

Abstract

This study investigated lecturers' behavioural intention and readiness to adopt artificial intelligence (AI) for academic engagement in federal universities across Northwest Nigeria. Anchored on the Diffusion of Innovation (DOI) theory and the Unified Theory of Acceptance and Use of Technology (UTAUT), a descriptive survey design was employed. Data were collected from 759 lecturers using the AI Technology Adoption Questionnaire (AITAQ) and analysed with descriptive statistics and Spearman's Rank Correlation. Findings revealed that lecturers demonstrated moderate levels of behavioural intention, readiness, and acceptance of AI, with an overall weighted mean of 3.43. Socioeconomic status had a weak, insignificant effect on AI adoption, whereas institutional support had a significant, though modest, positive effect, highlighting the importance of enabling environments over financial capacity. The study concludes that professional motivation, institutional structures, and perceived usefulness outweigh personal financial resources in determining AI adoption, with practical implications for policymakers to strengthen infrastructure, training, and leadership support for sustainable AI integration in higher education. These findings contribute to the growing body of knowledge on AI integration in education, providing practical information for educators and policymakers seeking to enhance academic engagement through AI innovations.

Keywords
AI technology, behavioural intention, readiness to adopt, technology acceptance, technology adoption, institutional support, socioeconomic status

Article Details

How to Cite
Abubakar, U., & Onasanya, S. A. (2025). Behavioural Intention and Readiness for AI Adoption among Lecturers in Northwest Nigerian Universities. Advances in Mobile Learning Educational Research, 6(1), 1681-1698. https://doi.org/10.25082/AMLER.2026.01.003

References

  1. Agwoje, S. E., & Okeleke, M. C. (2023). Institutional leadership and the management of change in the 21st century university education. International Journal of Institutional Leadership, Policy and Management, 5(4), 393-417.
  2. Aithal, P. S., & Aithal, S. (2023). How to Empower Educators through Digital Pedagogies and Faculty Development Strategies. International Journal of Applied Engineering and Management Letters, 139–183. Internet Archive. https://doi.org/10.47992/ijaeml.2581.7000.0198
  3. Alnasib, B. N. M. (2023). Factors Affecting Faculty Members’ Readiness to Integrate Artificial Intelligence into Their Teaching Practices: A Study from the Saudi Higher Education Context. International Journal of Learning, Teaching and Educational Research, 22(8), 465–491. https://doi.org/10.26803/ijlter.22.8.24
  4. Andrade, C. (2020). Sample Size and its Importance in Research. Indian Journal of Psychological Medicine, 42(1), 102–103. https://doi.org/10.4103/ijpsym.ijpsym_504_19
  5. Anurogo, D., Hardin La Ramba, Nabila Diyana Putri, & Ulfah Mahardika Pramono Putri. (2023). Digital Literacy 5.0 to Enhance Multicultural Education. Multicultural Islamic Education Review, 1(2), 109–179. https://doi.org/10.23917/mier.v1i2.3414
  6. Ateş, H., & Gündüzalp, C. (2025). The convergence of GETAMEL and protection motivation theory: A study on augmented reality-based gamification adoption among science teachers. Education and Information Technologies, 30(12), 17361–17403. https://doi.org/10.1007/s10639-025-13480-1
  7. Ayeni, O. O., Al Hamad, N. M., Chisom, O. N., Osawaru, B., & Adewusi, O. E. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2), 261-271. https://doi.org/10.30574/gscarr.2024.18.2.0062
  8. Cao, W., & Mai, N. (2025). Predictive Analytics for Student Success: AI-Driven Early Warning Systems and Intervention Strategies for Educational Risk Management. Educational Research and Human Development, 2(2), 36-48. https://doi.org/10.61784/erhd3042
  9. Chashechnikova, O., Odintsova, O., Hordiienko, I., Danylchuk, O., & Popova, L. (2024). Innovative technologies for the development of critical thinking in students. Revista Amazonia Investiga, 13(81), 197–213. CLOCKSS. https://doi.org/10.34069/ai/2024.81.09.16
  10. Dalsaniya, A., & Patel, K. (2022). Enhancing process automation with AI: The role of intelligent automation in business efficiency. International Journal of Science and Research Archive, 5(2), 322-337. https://doi.org/10.30574/ijsra.2022.5.2.0083
  11. Das, A., & Malaviya, S. (2025). Digital Platforms and Leveraging Technologies to Enhance Learner Engagement. Cyber‐Physical Systems for Innovating and Transforming Society 5.0, 211–231. Portico. https://doi.org/10.1002/9781394197750.ch10
  12. Deraney, P. (2022). “Because more trust now”: The Role of Peer Observation of Teaching in Building a Faculty Community of Practice. Qualitative Research in Education, 11(3), 270–297. https://doi.org/10.17583/qre.10266
  13. Du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630
  14. Edwards-Fapohunda, M. O., & Adediji, M. A. (2024). Sustainable development of distance learning in continuing adult education: The impact of artificial intelligence. IRE Journals, 8(1), 113-114..
  15. Erdmann, A., & Toro-Dupouy, L. (2025). The influence of the institutional environment on AI adoption in universities: identifying value drivers and necessary conditions. European Journal of Innovation Management, 28(9), 4365–4398. https://doi.org/10.1108/ejim-04-2024-0407
  16. Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2021). Intention to use analytical artificial intelligence (AI) in services – the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293–320. https://doi.org/10.1108/josm-10-2020-0378
  17. Granić, A. (2023). Technology adoption at individual level: toward an integrated overview. Universal Access in the Information Society, 23(2), 843–858. https://doi.org/10.1007/s10209-023-00974-3
  18. Grelle, S., & Hofmann, W. (2023). When and Why Do People Accept Public-Policy Interventions? An Integrative Public-Policy-Acceptance Framework. Perspectives on Psychological Science, 19(1), 258–279. https://doi.org/10.1177/17456916231180580
  19. Hang, N. T. (2024). The role of adoption, ease of use and teachers experience of artificial intelligence on teaching effectiveness: Moderating role of student interest. Journal of Pedagogical Research. LOCKSS. https://doi.org/10.33902/jpr.202428342
  20. Khairullah, S. A., Harris, S., Hadi, H. J., Sandhu, R. A., Ahmad, N., & Alshara, M. A. (2025). Implementing artificial intelligence in academic and administrative processes through responsible strategic leadership in the higher education institutions. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1548104
  21. Khanduri, V., & Teotia, Dr. A. (2023). Revolutionizing Learning: An Exploratory Study on The Impact of Technology-Enhanced Learning Using Digital Learning Platforms and AI Tools on The Study Habits of University Students Through Focus Group Discussions. International Journal of Research Publication and Reviews, 4(6), 663–672. https://doi.org/10.55248/gengpi.4.623.44407
  22. Lampropoulos, G., & Papadakis, S. (2025). The Educational Value of Artificial Intelligence and Social Robots. Social Robots in Education, 3–15. https://doi.org/10.1007/978-3-031-82915-4_1
  23. Lavidas, K., Papadakis, S., Manesis, D., Grigoriadou, A. S., & Gialamas, V. (2022). The Effects of Social Desirability on Students’ Self-Reports in Two Social Contexts: Lectures vs. Lectures and Lab Classes. Information, 13(10), 491. https://doi.org/10.3390/info13100491
  24. Lavidas, K., Petropoulou, A., Papadakis, S., Apostolou, Z., Komis, V., Jimoyiannis, A., & Gialamas, V. (2022). Factors Affecting Response Rates of the Web Survey with Teachers. Computers, 11(9), 127. https://doi.org/10.3390/computers11090127
  25. Lyu, T., Huang, K., & Chen, H. (2024). Exploring the Impact of Technology Readiness and Innovation Resistance on User Adoption of Autonomous Delivery Vehicles. International Journal of Human–Computer Interaction, 41(12), 7663–7683. https://doi.org/10.1080/10447318.2024.2400387
  26. Mnguni, L., Nuangchalerm, P., Zaky El Islami, R. A., Sibanda, D., Sari, I. J., & Ramulumo, M. (2024). The behavioural intentions for integrating artificial intelligence in science teaching among pre-service science teachers in South Africa and Thailand. Computers and Education: Artificial Intelligence, 7, 100334. https://doi.org/10.1016/j.caeai.2024.100334
  27. Mupaikwa, E. (2025). The Application of Artificial Intelligence in Educational Administration. AI Adoption and Diffusion in Education, 209–230. https://doi.org/10.4018/979-8-3693-7949-3.ch008
  28. Mutanga, M. B., Jugoo, V., & Adefemi, K. O. (2024). Lecturers’ Perceptions on the Integration of Artificial Intelligence Tools into Teaching Practice. Trends in Higher Education, 3(4), 1121–1133. https://doi.org/10.3390/higheredu3040066
  29. Nasni Naseri, R. N., & Abdullah, M. S. (2024). Understanding AI Technology Adoption in Educational Settings: A Review of Theoretical Frameworks and their Applications. Information Management and Business Review, 16(3(I)), 174–181. https://doi.org/10.22610/imbr.v16i3(i).3963
  30. Njeri, M., & Taym, A. (2024). Analysing the power of socioeconomic status on access to technology-enhanced learning in secondary schools. Research Studies in English Language Teaching and Learning, 2(4), 223–250. https://doi.org/10.62583/rseltl.v2i4.55
  31. Nwisagbo, A. E., Osuji, C. U., & Amachree, T. (2025). Leading Changes in Education: Strategies for Managing Resistance and Building Buy-In. International Journal of Educational Management, Rivers State University., 1(1), 387-402.
  32. Octafia, D., Supriyadi, S., & Sulhadi, S. (2021). Validity and Reliability Content of Physics Problem Solving Test Instrument Based on Local Wisdom. Journal of Educational Research and Evaluation, 9(1), 46–51. https://doi.org/10.15294/jere.v9i1.43712
  33. Okada, A., Sherborne, T., Panselinas, G., & Kolionis, G. (2025). Fostering Transversal Skills Through Open Schooling Supported by the CARE-KNOW-DO Pedagogical Model and the UNESCO AI Competencies Framework. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-025-00458-w
  34. Papadakis, S., Kiv, A. E., Kravtsov, H. M., Osadchyi, V. V., Marienko, M. V., Pinchuk, O. P., ... & Striuk, A. M. (2023). Unlocking the power of synergy: the joint force of cloud technologies and augmented reality in education.
  35. Papadakis, S., Kiv, A. E., Kravtsov, H. M., Osadchyi, V. V., Marienko, M. V., Pinchuk, O. P., Shyshkina, M. P., Sokolyuk, O. M., Mintii, I. S., Vakaliuk, T. A., Azarova, L. E., Kolgatina, L. S., Amelina, S. M., Volkova, N. P., Velychko, V. Ye., Striuk, A. M., & Semerikov, S. O. (2023). ACNS Conference on Cloud and Immersive Technologies in Education: Report. CTE Workshop Proceedings, 10, 1–44. https://doi.org/10.55056/cte.544
  36. Ragheb, M. A., Tantawi, P., Farouk, N., & Hatata, A. (2022). Investigating the acceptance of applying chat-bot (Artificial intelligence) technology among higher education students in Egypt. International Journal of Higher Education Management, 08(02). https://doi.org/10.24052/ijhem/v08n02/art-1
  37. Rogers, E. (2003). Diffusion of Innovations, 5th EDN London. UK: Free Press.
  38. Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2025). Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09897-9
  39. Salifu, I., Arthur, F., Arkorful, V., Abam Nortey, S., & Solomon Osei-Yaw, R. (2024). Economics students’ behavioural intention and usage of ChatGPT in higher education: a hybrid structural equation modelling-artificial neural network approach. Cogent Social Sciences, 10(1). https://doi.org/10.1080/23311886.2023.2300177
  40. Shaikh, I. M., Tanakinjal, G. H., Amin, H., Noordin, K., & Shaikh, J. (2024). Students’ e-learning acceptance: empirical evidence from higher learning institutions. On the Horizon: The International Journal of Learning Futures, 33(1), 1–13. https://doi.org/10.1108/oth-08-2022-0041
  41. Shonubi, O. A. (2024). Advancing organisational technology readiness and convergence of emerging digital technologies (AI, IoT, I4.0) for innovation adoption. International Journal of Technology and Globalisation, 9(1), 50–91. https://doi.org/10.1504/ijtg.2024.142621
  42. Sobaih, A. E. E., Elshaer, I. A., & Hasanein, A. M. (2024). Examining Students’ Acceptance and Use of ChatGPT in Saudi Arabian Higher Education. European Journal of Investigation in Health, Psychology and Education, 14(3), 709–721. https://doi.org/10.3390/ejihpe14030047
  43. Thelma, C. C., Sain, Z. H., Shogbesan, Y. O., Phiri, E. V., & Akpan, W. M. (2024). Digital literacy in education: Preparing students for the future workforce. International Journal of Research, 11(8), 327-343.
  44. Uzorka, A., Odebiyi, O. A., & Makumbi, D. (2025). Exploring Key Factors in Faculty Professional Development Programs for Seamless Integration of Modern Technology. Social Education Research, 112–124. https://doi.org/10.37256/ser.6120255893
  45. Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
  46. Zhang, W., & Hou, Z. (2024). College Teachers’ Behavioral Intention to Adopt Artificial Intelligence-Assisted Teaching Systems. IEEE Access, 12, 152812–152824. https://doi.org/10.1109/access.2024.3445909