Influence of artificial intelligence tool perceptions on mathematics undergraduates' academic engagement: role of attitudes and usage intentions
DOI:
https://doi.org/10.33830/ijdmde.v2i2.13064Keywords:
Artificial Intelligence, Attitude, Academic Engagement, Undergraduates, MathematicsAbstract
In African higher education, particularly among STEM students, the rapid integration of Artificial Intelligence (AI) into teaching and learning has created both opportunities for innovation and concerns about ethical use, however, there remains scarce studies about how students perceive and use these technologies in ways that influence their academic engagement and learning outcomes in Mathematics. This study, which focuses on attitudes and usage intentions, seeks to investigate the Influence of AI Tool Perceptions on the academic engagement of mathematics undergraduates from the lens of Technology Acceptance Model (TAM). The Structural Equation Modelling (SEM) approach was used to examine the perceptions of Mathematics undergraduates regarding AI usage and academic engagement. Data collected from 1,518 Mathematics undergraduates from Southwest Nigerian universities through a survey hosted online was analysed using PLS-SEM. The findings indicate that perceptions (perceived ease of use and perceived usefulness) influence attitudes towards and intentions to use AI tools while intention (β = -0.179, t = 2.426, p < 0.05), attitude towards (β = 0.216, t = 2.541, p < 0.05), and actual use of AI (β = 0.797, t = 11.904, p < 0.05) influences academic engagement, intention. According to this study, improving the mathematics students’ perceptions towards the use of AI tools could result in more engaging learning experiences. It highlights the necessity of developing positive attitudes and perceptions to foster academic engagement among undergraduate students in Mathematics programs, as well as the importance of developing supportive learning systems, and institutional regulations that support ethical and effective ways of incorporating AI.
References
Abdulmajeed, K., Joyner, D. A., & McManus, C. (2020). Challenges of online learning in Nigeria. Proceedings of the Seventh ACM Conference on Learning @ Scale, 417–420. https://doi.org/10.1145/3386527.3405953
Adelana, O. P., Ayanwale, M. A., & Sanusi, I. T. (2024). Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring—Based system. Cogent Education, 11(1), 2310976. https://doi.org/10.1080/2331186X.2024.2310976
Adewale, M. D., Azeta, A., Abayomi-Alli, A., & Sambo-Magaji, A. (2024). Impact of artificial intelligence adoption on students’ academic performance in open and distance learning: A systematic literature review. Heliyon, 10(22). https://doi.org/10.1016/j.heliyon.2024.e40025
Aieron A, B., Rowel Louie, T., Laika, B., Warren, P., & Pauline, C. (2024). The use of generative AI in learning and its influence on students’ academic engagement in Noveleta Senior High School. International Journal of Advanced Multidisciplinary Research and Studies, 4(3), 461–469. https://doi.org/10.62225/2583049X.2024.4.3.2809
Anierobi, E. I., Amjad, A. I., Agogbua, V. U., Aslam, S., Fakhrou, A., Abdullah Alanazi, A., N. Nwogbo, V., Eleje, L. I., Alshamsi, H., Khan, S., & Javaid, S. (2025). Artificial intelligence utilization: A determinant of academic self-efficacy, engagement, and satisfaction of university students. Environment and Social Psychology, 10(3). https://doi.org/10.59429/esp.v10i3.3504
Ateeq, A., Alaghbari, M., Alzoraiki, M., Milhem, M., & Beshr, B. A. H. (2024). Empowering Academic Success: Integrating AI Tools in University Teaching for Enhanced Assignment and Thesis Guidance. https://doi.org/10.1109/ICETSIS61505.2024.10459686
Ayanwale, M. A., & Molefi, R. R. (2024). Exploring intention of undergraduate students to embrace chatbots: From the vantage point of Lesotho. International Journal of Educational Technology in Higher Education, 21(1), 20-34. https://doi.org/10.1186/s41239-024-00451-8
Ayanwale, M. A., & Ndlovu, M. (2024). Investigating factors of students’ behavioral intentions to adopt chatbot technologies in higher education: Perspective from expanded diffusion theory of innovation. Computers in Human Behavior Reports, 14, 100396. https://doi.org/10.1016/j.chbr.2024.100396
Ayanwale, M. A., Sanusi, I. T., Molefi, R. R., & Otunla, A. O. (2024). A Structural Equation Approach and Modelling of Pre-service Teachers’ Perspectives of Cybersecurity Education. Education and Information Technologies, 29(3), 3699–3727. https://doi.org/10.1007/s10639-023-11973-5
Bai̇Doo-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/10.61969/jai.1337500
Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1), 60-76. https://doi.org/10.1186/s40561-023-00269-3
Chibisa, A., Sibaya, D. C., & Mutambara, D. (2022). Factors affecting pre-service teachers’ acceptance of online learning to promote social distancing. Progressio. https://doi.org/10.25159/2663-5895/12229
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publishers.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. L. Erlbaum Associates.
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
Creswell, J. W. (2014). Research Design: Qualitative, Quantitative and Mixed Methods Approaches (4th edition). SAGE Publications, Inc.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (Fifth edition). SAGE.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-Learning in Libyan Higher Education: a structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192–199. https://doi.org/10.7763/IJIET.2016.V6.683
Ellikkal, A., & Rajamohan, S. (2024). AI-enabled personalized learning: Empowering management students for improving engagement and academic performance. Vilakshan - XIMB Journal of Management, 22(1), 28–44. https://doi.org/10.1108/XJM-02-2024-0023
Etikan, I., Musa, S. A., & Alkassim, R. S. (2015). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), Article 1. https://doi.org/10.11648/j.ajtas.20160501.11
Falebita, O., & Kok, P. (2025). Undergraduate intentions of generative artificial intelligence for enhanced learning: influence of attitude and perceived behavioral control. The International Journal of Technology, Knowledge, and Society, 21(1), 169–188. https://doi.org/10.18848/1832-3669/CGP/v21i01/169-188
Falebita, O. S., & Kok, P. J. (2024). Artificial intelligence tools usage: a structural equation modeling of undergraduates’ technological readiness, self-efficacy and attitudes. Journal for STEM Education Research, 7, 1–26. https://doi.org/10.1007/s41979-024-00132-1
Falebita, O. S., & Olofin, S. O. (2020). Impact of teachers’ instructional practices on students’ achievement and self-efficacy in mathematics. Commonwealth Journal of Academic Research (CJAR.EU), 1(3), 1–14. https://doi.org/10.5281/ZENODO.3879417
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Freda, M. F., Raffaele, D. L. P., Esposito, G., Ragozini, G., & Testa, I. (2023). A new measure for the assessment of the university engagement: The SInAPSi academic engagement scale (SAES). Current Psychology, 42(12), 9674–9690. https://doi.org/10.1007/s12144-021-02189-2
Geddam, S. M. G., Dr. N. Nethravathi, Nethravathi, N., & Hussian, A. A. (2024). Understanding AI adoption: the mediating role of attitude in user acceptance. Journal of Informatics Education and Research, 4(2). https://doi.org/10.52783/jier.v4i2.975
Green, R. A., Whitburn, L. Y., Zacharias, A., Byrne, G., & Hughes, D. L. (2018). The relationship between student engagement with online content and achievement in a blended learning anatomy course. Anatomical Sciences Education, 11(5), 471–477. https://doi.org/10.1002/ase.1761
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of the structural model. In J. F. Hair Jr., G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, & S. Ray (Eds.), Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook (pp. 115–138). Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7_6
Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624
Hair, J. F., Sarstedt, M., Hopkins, L., & G., K. V. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584. https://doi.org/10.1108/EJM-10-2018-0665
Isnawan, M. G., Belbase, S., & Yanuarto, W. N. (2024). Implementation of a hybrid mathematics module to minimize students’ learning obstacles when interpreting fractions. International Journal of Didactic Mathematics in Distance Education, 1(2), 83–101. https://doi.org/10.33830/ijdmde.v1i2.9555
James, W., Oates, G., & Schonfeldt, N. (2025). Improving retention while enhancing student engagement and learning outcomes using gamified mobile technology. Accounting Education, 34(3), 366–386. https://doi.org/10.1080/09639284.2024.2326009
Kahu, E. R., & Nelson, K. (2018). Student engagement in the educational interface: Understanding the mechanisms of student success. Higher Education Research & Development, 37(1), 58–71. https://doi.org/10.1080/07294360.2017.1344197
Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1–19. https://doi.org/10.1108/IJILT-05-2020-0090
Kaswan, K. S., Dhatterwal, J. S., & Ojha, R. P. (2024). AI in personalized learning. In Advances in Technological Innovations in Higher Education. CRC Press.
Kim, D., Kim, S., Kim, S., & Lee, B. H. (2025). Generative AI characteristics, user motivations, and usage intention. Journal of Computer Information Systems. https://www.tandfonline.com/doi/abs/10.1080/08874417.2024.2442438
Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., Komis, V., & Karacapilidis, N. (2024). Determinants of humanities and social sciences students’ intentions to use artificial intelligence applications for academic purposes. Information, 15(6), 314. https://doi.org/10.3390/info15060314
Lestari, N. N., & Indrasari, S. Y. (2019). Teachers’ adoption of 1:1 iPad implementation in the classroom: The role of efficacy and perceived impact. Interactive Technology and Smart Education, 16(3), 278–299. https://doi.org/10.1108/ITSE-06-2018-0041
Lynam, S., Cachia, M., & Stock, R. (2024). An evaluation of the factors that influence academic success as defined by engaged students. Educational Review, 76(3), 586–604. https://doi.org/10.1080/00131911.2022.2052808
Molina, M. G., & Yabut, E. V. (2025). Integrating technology and non-routine problems: The design, development, and validation of a computer-aided assessment for non-routine problem-solving. International Journal of Didactic Mathematics in Distance Education, 2(1), 81–104. https://doi.org/10.33830/ijdmde.v2i1.11721
Mutambara, D., & Chibisa, A. (2022). Rural STEM Preservice Teachers’ Acceptance of Virtual Learning. International Journal of Learning, Teaching and Educational Research, 21(2), Article 2.
Nawaz, I. Y. (2020). Characteristics of Millennials and Technology Adoption in the Digital Age. In Handbook of Research on Innovations in Technology and Marketing for the Connected Consumer (pp. 241–262). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-7998-0131-3.ch012
Nuryakin, N., Rakotoarizaka, N. L. P., & Musa, H. G. (2023). The effect of perceived usefulness and perceived easy to use on student satisfaction: the mediating role of attitude to use online learning. APMBA (Asia Pacific Management and Business Application), 11(3), 323–336. https://doi.org/10.21776/ub.apmba.2023.011.03.5
Osman, Z. (2025). Attitude as a catalyst: the role of perceived ease of use, perceived usefulness, and self-efficacy in shaping student intentions to use artificial intelligence in higher education. International Journal of Academic Research in Accounting, Finance and Management Sciences, 15(1), Pages 201-215. https://doi.org/10.6007/IJARAFMS/v15-i1/24459
Osman, Z., Alwi, N.H., Jodi, K.H.M., Khan, B.N.A., Ismail, M.N., & Yusoff, Y. (2024). Optimizing Artificial Intelligence Usage among Academicians in Higher Education Institutions. https://doi.org/10.6007/ijarafms/v14-i2/20935
Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS. SmartPLS 4. Bönningstedt: SmartPLS. https://www.smartpls.com/
Rodway, P & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, 100150. https://doi.org/10.1016/j.caeai.2023.100150
Sander, T & Teh, P.L. (2014). SmartPLS for the human resources field to evaluate a model. 346–358. http://eprints.sunway.edu.my/243/
Sarstedt, M., Ringle, C. M & Hair, J. F. (2017). Partial Least Squares Structural Equation Modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of Market Research (pp. 1–40). Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-1
Tan, P. S. H., Seow, A. N., Choong, Y. O., Tan, C. H., Lam, S. Y., & Choong, C. K. (2023). University students’ perceived service quality and attitude towards hybrid learning: Ease of use and usefulness as mediators. Journal of Applied Research in Higher Education, 16(5), 1500–1514. https://doi.org/10.1108/JARHE-03-2023-0113
Toros, E., Asiksoy, G., & Sürücü, L. (2024). Refreshment students’ perceived usefulness and attitudes towards using technology: A moderated mediation model. Humanities and Social Sciences Communications, 11(1), 1–10. https://doi.org/10.1057/s41599-024-02839-3
Wang, Y., & Xue, L. (2024). Using AI-driven chatbots to foster Chinese EFL students’ academic engagement: An intervention study. Computers in Human Behavior, 159, 108353. https://doi.org/10.1016/j.chb.2024.108353
Ward, B., Bhati, D., Neha, F., & Guercio, A. (2024). Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance (No. arXiv:2412.02166). arXiv. https://doi.org/10.48550/arXiv.2412.02166
Xerri, M. J., Radford, K., & Shacklock, K. (2018). Student engagement in academic activities: A social support perspective. Higher Education, 75(4), 589–605. https://doi.org/10.1007/s10734-017-0162-9
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