EXAMINING THE INTENTION OF UNDERGRADUATE MATHEMATICS EDUCATION STUDENTS TO USE AI IN THEIR ACADEMIC WORK: AN APPLICATION OF THE UTAUT MODEL
Abstract
The emergence of generative Artificial Intelligence (AI) is disrupting every sector of the global economy and the information society. The education industry, similarly, has both educators and students exploring ways to utilize AI. This study investigates the adoption of AI in higher education among undergraduate mathematics education students using the four constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research focuses on Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, and their influence on students’ Behavioural Intention to adopt AI tools. Gender was included as a moderating variable in the relationships between these constructs and behavioural intention. A sample of 142 undergraduate mathematics education students participated in an online survey to measure their perceptions and intentions of AI adoption in their academic work. The findings revealed that all four independent variables and gender had significant direct effects on Behavioural Intention to use AI. This indicates that students perceive AI tools as valuable for enhancing academic performance, easy to use, influenced by social factors such as peers, and supported by adequate facilitating conditions such as technical infrastructure. However, gender did not emerge as a significant moderator in any of the relationships between the UTAUT constructs and Behavioural Intention. This suggests that male and female students exhibit similar adoption patterns toward AI technologies in this context. These results contribute to the growing body of literature on technology adoption in education by confirming the applicability of the UTAUT model within a mathematics education-focused cohort of students while highlighting that gender differences may not play a critical role in shaping intentions toward AI adoption. Future research could explore additional moderating variables or extend this analysis across other disciplines for broader generalizability.
Article visualizations:
Keywords
Full Text:
PDFReferences
Acosta-Enriquez, B.G., Farronan, E.V.R., Zapata, L.I.V., Garcia, F.S.M., Rabanal-Leon, H.C., Angaspilco, J.F.M., Bocanegra, J.C.S. (2024). Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory. Heliyon. https://doi.org/10.1016/j.heliyon.2024.e38315
Adigun, O. T., Tijani, F. A., Haihambo, C. K., & Enock, S. L. (2025). Understanding pre-service teachers’ intention to adopt and use artificial intelligence in Nigerian inclusive classrooms. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1519472
Aldreabi1, H., Dahdoul, N.K.S., Alhur, M., Alzboun, N., & Alsalhi, N.R. (2025). Determinants of student adoption of generative AI in Higher Education. Electronic Journal of e-Learning, 23(1), 15-33, https://doi.org/10.34190/ejel.23.1.3599
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2020). Technology acceptance model in M-learning context: A systematic review. Sustainability, 12(9). https://doi.org/10.3390/su12093654
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2022). Exploring the critical challenges and factors influencing the e-learning system usage during COVID-19 pandemic. Education and Information Technologies, 27, 1051–1078. https://doi.org/10.1007/s10639-021-10475-y
Alyoussef, I.Y. (2021). Factors influencing students’ acceptance of M-Learning in Higher Education: An application and extension of the UTAUT model. Electronics, 10(3171). https://doi.org/10.3390/electronics10243171
Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1-3), 139-159. https://doi.10.1016/0004-3702(91)90053-m
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of Artificial Intelligence for teachers: a systematic review of research. TechTrends, 1-15. https://doi.org/10.1007/s11528-022-00715-y
Chandra, Y.W., & Suyanto, S. (2019). Indonesian chatbot of university admission using a question answering system based on sequence-to-sequence model. Procedia Computer Science, 157, 367-374. https://doi.org/10.1016/j.procs.2019.08.179
Crawford, J., Cowling, M., & Allen, K. A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching & Learning Practice, 20(3), 2. https://doi.org/10.53761/1.20.3.02
Crawford, J., Cowling, M., Ashton-Hay, S., Kelder, J-A., Middleton, B., & Wilson, G. (2023). Artificial intelligence and authorship editor policy: ChatGPT, Jasper, Bing, and beyond. Journal of University Teaching and Learning Practice, 20(5), https://doi.org/10.53761/1.20.5.01
Dwivedi, Y. K., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Ellis, R., Bliuc, A.-M., Han, F. (2021). Challenges in assessing the nature of effective collaboration in blended university courses. Australasian Journal of Educational Technology, 37(1), 1-14. https://doi.org/10.14742/ajet.5576
Faraon, M., Rönkkö, K., Milrad, M., & Tsui, E. (2025). International perspectives on artificial intelligence in higher education: An explorative study of students’ intention to use ChatGPT across the Nordic countries and the USA. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13492-x
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press. Retrieved from https://mitpress.mit.edu/9780262035613/deep-learning/
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S. & Yang, G-Z. (2019). XAI: Explainable artificial intelligence. Science Robotics, 4(37). https://doi.org/10.1126/scirobotics.aay7120
Hamzat, L.F. (2024). Influence of technology-enhanced learning tools on collaborative learning in higher education: A critical review. Journal of Education and Practice, 15(13), 20-27. https://doi.org/10.7176/JEP/15-13-03
Klos, M.C., Escoredo, M., Joerin, A., Lemos, V.N., Rauws, M., & Bunge, E.L. (2021). Artificial intelligence–based Chatbot for anxiety and depression in university students: Pilot randomized controlled trial. JMIR Formative Research, 5(8), 20678. https://doi.org/10.2196/20678
Kumar, V.S., & Boulanger, D. (2021). Automated essay scoring and the deep learning black box: How are rubric scores determined? International Journal of Artificial Intelligence in Education, 31(3): 538-584. https://doi.org/10.1007/s40593-020-00211-5
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
Mailizar, Burg, D., & Maulina, S. (2021). Student acceptance of e-learning during the COVID-19 pandemic: An application of the UTAUT model. Education and Information Technologies, 26, 1093–1112. https://doi.org/10.1007/s10639-020-10320-x
Major, L. & Francis, G. A. (2020). Technology-supported personalised learning: Rapid evidence review. EdTechHub. https://doi.org/10.5281/zenodo.3948175
Manyika, J., Chui, M., Bughin, J., Brown, B., Dobbs, R., Roxburgh, C., & Sarrazin, H. (2017). Artificial intelligence: The next digital frontier? [McKinsey Global Institute]. Retrieved from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/artificial-intelligence-the-next-digital-frontier
Mutisya, D. N., & Makokha, G. L. (2016). Challenges affecting adoption of e-learning in public universities in Kenya. International Review of Research in Open and Distributed Learning, 17(3), 120–141. https://doi.org/10.19173/irrodl.v17i3.2224
Nilsson, N. J. (1980). Principles of artificial intelligence. Palo Alto, CA: Morgan Kaufmann Publishers. Retrieved from https://books.google.ro/books/about/Principles_of_Artificial_Intelligence.html?id=4JwN5DTpcqkC&redir_esc=y
Nizam, N., Wahab, S. N., & Rahim, A. (2021). Adoption of artificial intelligence in higher education: The UTAUT model approach. International Journal of Emerging Technologies in Learning (iJET), 16(12), 25–34.
O'Dea, X., & O'Dea, M. (2023). Is artificial intelligence really the next big thing in learning and teaching in Higher Education? A conceptual paper. Journal of University Teaching & Learning Practice, 20(5). https://doi.org/10.53761/1.20.5.05
Park, M-J, & Lee, J-K (2021). Investigation of college students’ intention to accept online education services: An application of the UTAUT model in Korea. Journal of Asian Finance, Economics and Business, 8 (6), 327–336. https://doi:10.13106/jafeb.2021.vol8.no6.0327
Park, Y. B., & Park, H. J. (2017). Testing for use and acceptance of internet banking based on UTAUT model. Journal of the Korea Industrial Information Systems Research, 22(1), 11–21. https://doi.org/10.9723/jksiis.2017.22.1.011
Patterson, A, Frydenberg, M., & Basma, L (2024). Examining generative artificial intelligence adoption in academia: a UTAUT perspective. Issues in Information Systems, 25(3), 238-251. https://doi.org/10.48009/3_iis_2024_119
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd Ed.). Harlow: Pearson Education. Retrieved from http://repo.darmajaya.ac.id/5272/1/Artificial%20Intelligence-A%20Modern%20Approach%20(3rd%20Edition)%20(%20PDFDrive%20).pdf
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117. http://dx.doi.org/10.1016/j.neunet.2014.09.003
Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025). Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2. Contemporary Educational Technology, 17(2), ep571. https://doi.org/10.30935/cedtech/16039
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2272–2280. https://doi.org/10.1016/j.compedu.2011.06.004
Uto, M., Xie, Y., & Ueno, M. (2020). Neural automated essay scoring incorporating handcrafted features. In Proceedings of the 28th International Conference on Computational Linguistics, 6077-6088. Retrieved from https://aclanthology.org/2020.coling-main.535/
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. Retrieved from https://doi.org/10.2307/41410412
Williams, M. D., Dwivedi, Y. K., Lal, B., & Schwarz, A. (2009). Contemporary trends and issues in IT adoption and diffusion research. Journal of Information Technology, 24, 1–10. https://doi:10.1057/jit.2008.30
Williams, M.D., Rana, N.P., & Dwivedi, Y.K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, 28(3), 443-488. Retrieved from https://www.jstor.org/stable/41410412
DOI: http://dx.doi.org/10.46827/ejes.v12i11.6380
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Philip K. Mwei
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright © 2015-2026. European Journal of Education Studies (ISSN 2501 - 1111) is a registered trademark of Open Access Publishing Group. All rights reserved.
This journal is a serial publication uniquely identified by an International Standard Serial Number (ISSN) serial number certificate issued by Romanian National Library (Biblioteca Nationala a Romaniei). All the research works are uniquely identified by a CrossRef DOI digital object identifier supplied by indexing and repository platforms. All authors who send their manuscripts to this journal and whose articles are published on this journal retain full copyright of their articles. All the research works published on this journal are meeting the Open Access Publishing requirements and can be freely accessed, shared, modified, distributed and used in educational, commercial and non-commercial purposes under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
