EXAMINING THE INTENTION OF UNDERGRADUATE MATHEMATICS EDUCATION STUDENTS TO USE AI IN THEIR ACADEMIC WORK: AN APPLICATION OF THE UTAUT MODEL

نویسندگان

  • Philip K. Mwei Lecturer of Mathematics Education and Educational Technology, Department of Curriculum, Instruction and Educational Media, School of Education, Moi University, P.O. Box 3900-30100, Eldoret, Kenya orcid.org/0000-0001-5116-108

DOI:

https://doi.org/10.46827/ejes.v12i11.6380

کلمات کلیدی:

Artificial Intelligence (AI), Artificial Intelligence Adoption, Mathematics Education, Undergraduate Students, Unified Theory of Acceptance and Use of Technology (UTAUT)

چکیده

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:

Hit counter

بیوگرافی نویسنده

  • Philip K. Mwei، Lecturer of Mathematics Education and Educational Technology, Department of Curriculum, Instruction and Educational Media, School of Education, Moi University, P.O. Box 3900-30100, Eldoret, Kenya orcid.org/0000-0001-5116-108

    Dr. Philip K. Mwei has a PhD in Educational Communication and Technology, and he is a lecturer of Mathematics Education and Educational Technology at Moi University, Department of Curriculum, Instruction and Educational Media. He has taught and examined mathematics education courses at undergraduate and master’s levels. He has published in peer-reviewed journals with research interests on: Mathematical problem solving, ICT integration in education (esp. in curriculum and instruction), Language of Mathematics/Mathematical language, Teaching models and skills for effective curriculum and instruction, and appropriate use of statistical methods in research. He is a member of the African Network for Internationalization of Education (ANIE).

مراجع

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

دانلودها

چاپ شده

2025-11-12

شماره

دوره 12 شماره 11 (2025)

نوع مقاله

Articles

مجوز

Authors who publish with this journal agree to the following terms:

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).