The Relationship Between Undergraduate Students’ Satisfaction and Artificial Intelligence (AI) Subscriptions for Learning Practices in Oyo State
DOI:
https://doi.org/10.0001/4xn17676Keywords:
satisfaction, Subscription types, Learning practices, FreemiumAbstract
This study investigated undergraduate students’ satisfaction with Artificial Intelligence (AI) tools for learning and the relationship with subscription type in Oyo State, Nigeria. Anchored in the Expectation–Confirmation Model, it examined whether satisfaction levels differ across subscription types (Free, Freemium, Paid, and Institution-provided). A descriptive cross-sectional survey design was employed; 50 undergraduates from one state university completed a validated questionnaire (Cronbach’s α = 0.842). Data were analysed using descriptive statistics and Tukey HSD post-hoc comparisons. Results showed an overall satisfaction mean of 3.10/4.0, indicating approval of AI tools. However, satisfaction varied significantly by subscription type: Freemium users reported the highest satisfaction, significantly exceeding Free (Δ = 0.583, p = .012), Paid (Δ = 1.000, p = .009), and Institution-provided users (Δ = 1.000, p = .004); no significant differences existed among the latter three groups. Thus, the null hypothesis of no relationship between satisfaction and subscription continuation was rejected. The findings extend technology acceptance theory by demonstrating that perceived value and confirmed expectations, rather than payment tier alone, drive satisfaction and continuance intention. Universities and policymakers should adopt Freemium-oriented access strategies, strengthen AI literacy programmes, and institute ethical guidelines to maximise pedagogical value and ensure responsible AI integration
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References
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.1234/jai.2023.0070052
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kumar, V., & Sharma, R. (2025). Undergraduate students’ perceptions and satisfaction with AI-based learning tools: A cross-sectional study. Smart Learning Environments, 12, 1–17. https://doi.org/10.1186/s40561-025-00312-z
Müller, L., & Mayer, P. (2024). “It helps, but I need to double-check”: Student satisfaction and performance with ChatGPT in an introductory programming course. International Journal of Educational Technology in Higher Education, 21, Article 45. https://doi.org/10.1186/s41239-024-00445-9
Pinto, M., & Leite, C. (2023). AI course assistants in online higher education: Effects on engagement, perceived support, and satisfaction. Open Praxis, 15(4), 401–418. https://doi.org/10.5944/openpraxis.15.4.3734
Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921https://www.jstor.org/stable/3250921
Sae-Tae, K., Ling, J., & Wang, Q. (2024). The impact of user addiction on continuance intention to use streaming platforms: incorporating expectation confirmation model and personality traits. Frontiers in Communication, 9. https://doi.org/10.3389/fcomm.2024.1410975
Al-Adwan, A. S., Nofal, M., Akram, H., Albelbisi, N. A., & Al-Okaily, M. (2022). Towards a Sustainable adoption of E-Learning Systems: The role of Self-Directed Learning. Journal of Information Technology Education Research, 21, 245–267. https://doi.org/10.28945/4980
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Alshammari, S. H., & Babu, E. (2025). The mediating role of satisfaction in the relationship between perceived usefulness, perceived ease of use and students’ behavioural intention to use ChatGPT. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91634-4
Carissa, N. E., Erlangga, M., Evik, C. S., & Handayani, P. W. (2023). The Influence of Perceived Usefulness, Satisfaction, and Personalization on Subscription Video on Demand Continuance Intentions. CommIT (Communication and Information Technology) Journal, 17(2), 169–184. https://doi.org/10.21512/commit.v17i2.8446
Kim, M., Kim, J., & Park, S. (2017). Understanding IPTV churning behaviors: focus on users in South Korea. Asia Pacific Journal of Innovation and Entrepreneurship, 11(2), 190–213. https://doi.org/10.1108/apjie-08-2017-026
Nagubandi, N. K. (2024). Leveraging AI to revolutionize subscription business models. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(5), 649–660. https://doi.org/10.32628/cseit241051052
Adediran, E., Sakpere, W., & Ogunyinka, T. (2025). Artificial Intelligence in Nigeria: Challenges and Opportunities. https://doi.org/10.2139/ssrn.5269530
Bali, B., Garba, E. J., Ahmadu, A. S., Takwate, K. T., & Malgwi, Y. M. (2024). Analysis of emerging trends in artificial intelligence for education in Nigeria. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00163-y
Inuwa-Dutse, I. (2025, February 27). NaijaNLP: A Survey of Nigerian Low-Resource Languages. arXiv.org. https://arxiv.org/abs/2502.19784?utm_source=chatgpt.com
Nwile, C. B., Amaewhule, E. C., & Ezeugo, C. R. (2025). Utilization of artificial intelligence tools for improving educational outcomes of students in public universities in Rivers State, Nigeria. Deleted Journal, 19(1), 195–209. https://doi.org/10.62154/ajhcer.2025.019.01021
S, Y. A., M, K. S., & M, A. (2025, August 9). Utilization Level of Artificial Intelligence Tools in Research Writing among Undergraduate Biology Students in Zaria, Nigeria. S. | ATBU Journal of Science, Technology and Education. https://www.atbuftejoste.com.ng/index.php/joste/article/view/2524?utm
Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in the higher education: A systematic literature review and research challenges. International Journal of Educational Research, 127, 102411. https://doi.org/10.1016/j.ijer.2024.102411
Yu, C., Yan, J., & Cai, N. (2024). ChatGPT in higher education: factors influencing ChatGPT user satisfaction and continued use intention. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1354929
Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in the higher education: A systematic literature review and research challenges. International Journal of Educational Research, 127, 102411. https://doi.org/10.1016/j.ijer.2024.102411
Wang, J., Fan, W. The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanit Soc Sci Commun 12, 621 (2025). https://doi.org/10.1057/s41599-025-04787-y
Survey: 86% of Students Already Use AI in Their Studies -- Campus Technology. (2024). Campus Technology. https://campustechnology.com/articles/2024/08/28/survey-86-of-students-already-use-ai-in-their-studies.aspx
Digital Education Council Global AI Student Survey 2024. (2024). Digitaleducationcouncil.com. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024
Wu, B., Guo, G., & Luo, P. (2024). The effect of subscriptions on customer engagement. Journal of Business Research, 178, 114638–114638. https://doi.org/10.1016/j.jbusres.2024.114638
Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2025). Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education, 1–16. https://doi.org/10.1080/02602938.2025.2502582
Luo, J., Zheng, C., Yin, J., & Teo, H. H. (2025). Design and assessment of AI-based learning tools in higher education: a systematic review. International Journal of Educational Technology in Higher Education, 22(1). https://doi.org/10.1186/s41239-025-00540-2
Yu, C., Yan, J., & Cai, N. (2024). ChatGPT in higher education: factors influencing ChatGPT user satisfaction and continued use intention. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1354929
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