Mathematics Student Teachers’ Behavioural Intention Using ChatGPT

Minh Dung, Tang; Khoi Nguyen, Vo; Cao Minh Trí, Ðoàn; Lúóng Chí Quõc, Phú; Hoang Dieu Ban, Bui

doi:https://doi.org/10.21432/cjlt28665

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Abstract

The rapid rise of artificial intelligence (AI), exemplified by ChatGPT, has transformed education. However, few studies have examined the factors influencing its adoption in higher education, especially among Mathematics student teachers. This study investigates factors that influence the behavioural intentions of Mathematics student teachers regarding using ChatGPT. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model, data were collected through a questionnaire of 24 items across six factors on a 5-point Likert scale. Using multiple linear regression analysis with RStudio, the findings reveal that Intrinsic Motivation, Performance Expectancy, Social Influence, and Perceived Trust positively affect behavioural intentions to adopt ChatGPT. The study emphasizes implications for developers and educators to enhance AI integration in education, thereby supporting personalized and engaging learning experiences.

Keywords:

artificial intelligence,
behavioural intention,
ChatGPT,
Mathematics student teacher

Résumé

L’essor rapide de l’intelligence artificielle (IA), illustré par ChatGPT, a transformé l’éducation. Cependant, peu d’études ont examiné les facteurs influençant son adoption dans l’enseignement supérieur, en particulier parmi les stagiaires en mathématiques. Cette étude examine les facteurs qui influencent les intentions comportementales des stagiaires en mathématiques concernant l’utilisation de ChatGPT. Guidés par le modèle de la théorie unifiée de l’acceptation et de l’utilisation des technologies (UTAUT), les données ont été collectées au moyen d’un questionnaire de 24 éléments portant sur six facteurs sur une échelle de Likert à 5 points. À l’aide d’une analyse de régression linéaire multiple avec RStudio, les résultats révèlent que la motivation intrinsèque, les attentes en matière de performance, l’influence sociale et la confiance perçue affectent positivement les intentions comportementales d’adopter ChatGPT. L’étude met l’accent sur les implications pour les personnes développeuses et enseignantes d’améliorer l’intégration de l’IA dans l’éducation, soutenant ainsi des expériences d’apprentissage personnalisées et engageantes.

Mots-clés :

ChatGPT,
intelligence artificielle,
intentions comportementales,
stagiaires en mathématiques

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Biographical notes

Tang Minh Dung, PhD, earned his doctorate in Mathematics - Informatics from Université Grenoble Alpes in France. He is a lecturer in the Department of Mathematics and Informatics at Ho Chi Minh City University of Education in Vietnam. His research focuses on teacher education and technology-enhanced mathematics education. Email: dungtm@hcmue.edu.vn ORCID: https://orcid.org/0000-0001 5401-1395

Vo Khoi Nguyen is a senior mathematics education student at the Department of Mathematics and Informatics, Ho Chi Minh City University of Education in Vietnam. His research focuses on harmonic analysis and teacher education. Email: 4701101107@student.hcmue.edu.vn ORCID: https://orcid.org/0009-0009-3738-6967

Ðoàn Cao Minh Trí is a senior mathematics education student at the Department of Mathematics and Informatics, Ho Chi Minh City University of Education in Vietnam. His research focuses on representation theory, number theory, and mathematics teaching methodology. Email: minhtridoancao06@gmail.com ORCID: https://orcid.org/0009-0001-7783-1393

Phú Lúóng Chí Quõc is a senior mathematics education student at the Department of Mathematics and Informatics, Ho Chi Minh University of Education in Vietnam. His research focuses on technology-enhanced mathematics education. Email: chiquocphuluong26012002@gmail.com ORCID: https://orcid.org/0009-0009-4151-954X

Bui Hoang Dieu Ban is a senior mathematics education student at the Department of Mathematics and Informatics, Ho Chi Minh University of Education in Vietnam. Her research focuses on technology-enhanced mathematics education and mathematics teaching methodology. Email: buihoangdieuban12012003@gmail.com ORCID: https://orcid.org/0009-0008-1696-1195

Bibliography

Agyei, D. D., & Voogt, J. (2011). ICT use in the teaching of mathematics: Implications for professional development of pre-service teachers in Ghana. Education and Information Technologies, 16, 423–439. https://doi.org/10.1007/s10639-010-9141-9
10.1007/s10639-010-9141-9 Google Scholar
Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195
10.1002/hbe2.195 Google Scholar
Albion, P., Jamieson-Proctor, R., & Finger, G. (2010). Auditing the TPACK confidence of Australian pre-service teachers: The TPACK confidence survey (TCS). In D. Gibson & B. Dodge (Eds.), Society for Information Technology & Teacher Education International Conference (pp. 3772–3779). Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/33969/
Google Scholar
Alshammari, S. H., & Alshammari, M. H. (2024). Factors affecting the adoption and use of ChatGPT in higher education. International Journal of Information and Communication Technology Education, 20(1), 1–16. https://doi.org/10.4018/IJICTE.339557
10.4018/IJICTE.339557 Google Scholar
Angeli, C., & Valanides, N. (2009). Epistemological and methodological issues for the conceptualization, development, and assessment of ICT–TPCK: Advances in technological pedagogical content knowledge (TPCK). Computers & Education, 52(1), 154–168. https://doi.org/10.1016/j.compedu.2008.07.006
10.1016/j.compedu.2008.07.006 Google Scholar
Bernabei, M., Colabianchi, S., Falegnami, A., & Costantino, F. (2023). Students’ use of large language models in engineering education: A case study on technology acceptance, perceptions, efficacy, and detection chances. Computers & Education: Artificial Intelligence, 5, 100172. https://doi.org/10.1016/j.caeai.2023.100172
10.1016/j.caeai.2023.100172 Google Scholar
Cheng, Y., & Jiang, H. (2020). How do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued use. Journal of Broadcasting & Electronic Media, 64(4), 592–614. https://doi.org/10.1080/08838151.2020.1834296
10.1080/08838151.2020.1834296 Google Scholar
Cristobal, E., Flavián, C., & Guinalíu, M. (2007). Perceived e‐service quality (PeSQ): Measurement validation and effects on consumer satisfaction and web site loyalty. Managing Service Quality: An International Journal, 17(3), 317–340. https://doi.org/10.1108/09604520710744326
10.1108/09604520710744326 Google Scholar
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
10.1111/j.1559-1816.1992.tb00945.x Google Scholar
De Cosmo, L. M., Piper, L., & Di Vittorio, A. (2021). The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Italian Journal of Marketing, 2021(1–2), 83–102. https://doi.org/10.1007/s43039-021-00020-1
10.1007/s43039-021-00020-1 Google Scholar
Drent, M., & Meelissen, M. (2008). Which factors obstruct or stimulate teacher educators to use ICT innovatively? Computers & Education, 51(1), 187–199. https://doi.org/10.1016/j.compedu.2007.05.001
10.1016/j.compedu.2007.05.001 Google Scholar
Duong, C. D., Vu, T. N., & Ngo, T. V. N. (2023). Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. International Journal of Management Education, 21(3), 100883. https://doi.org/10.1016/j.ijme.2023.100883
10.1016/j.ijme.2023.100883 Google Scholar
Egara, F. O., & Mosimege, M. (2024). Exploring the integration of artificial intelligence-based ChatGPT into Mathematics instruction: Perceptions, challenges, and implications for educators. Education Sciences, 14(7), 742. https://doi.org/10.3390/educsci14070742
10.3390/educsci14070742 Google Scholar
Enochsson, A.-B. (2009). ICT in initial teacher training. Organisation for Economic Co-operation and Development. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=5dcec359fa38aa677c4beabf85b7257284ee6b18
Google Scholar
Firat, E. A., & Köksal, M. S. (2019). Effects of instruction supported by web 2.0 tools on prospective teachers’ biotechnology literacy. Computers & Education, 135, 61–74. https://doi.org/10.1016/j.compedu.2019.02.018
10.1016/j.compedu.2019.02.018 Google Scholar
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Contemporary Sociology, 6, 244. https://api.semanticscholar.org/CorpusID:144301400
Google Scholar
Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 40, 4501–4520. https://doi.org/10.1080/10447318.2023.2226495
10.1080/10447318.2023.2226495 Google Scholar
Frost, J. (2019). Regression analysis: An intuitive guide for using and interpreting linear models. Statistics by Jim Publishing.
Google Scholar
Getenet, S. (2024). Pre-service teachers and ChatGPT in multistrategy problem-solving: Implications for mathematics teaching in primary schools. International Electronic Journal of Mathematics Education, 19(1), em0766. https://doi.org/10.29333/iejme/14141
10.29333/iejme/14141 Google Scholar
Green, S. B. (1991). How many subjects does it take to do a regression analysis. Multivariate Behavioral Research, 26(3), 499–510. https://doi.org/10.1207/s15327906mbr2603_7
10.1207/s15327906mbr2603_7 Google Scholar
Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2018). Multivariate data analysis (8th ed.). Cengage.
Google Scholar
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New challenges to international marketing (Vol. 20, pp. 277–319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
10.1108/S1474-7979(2009)0000020014 Google Scholar
Hoang, T., & Chu Nguyen, M. N. (2008). Phan tich du lieu nghien cuu voi SPSS 2008 (tap 1) [Research data analysis with SPSS 2008 (Volume 1)]. Hong Duc Publisher.
Google Scholar
Hsu, H. T., & Lin, C. C. (2021). Extending the technology acceptance model of college learners’ mobile‐assisted language learning by incorporating psychological constructs. British Journal of Educational Technology, 53(2), 286–306. https://doi.org/10.1111/bjet.13165
10.1111/bjet.13165 Google Scholar
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: with applications in R (2nd ed.). Springer.
Google Scholar
Jo, H. (2023). Understanding AI tool engagement: A study of ChatGPT usage and word-of-mouth among university students and office workers. Telematics and Informatics, 85, 102067. https://doi.org/10.1016/j.tele.2023.102067
10.1016/j.tele.2023.102067 Google Scholar
Kabudi, T. M. (2022, May). Artificial intelligence for quality education: Successes and challenges for AI in meeting SDG4. In Y. Zheng, P. Abbott, & J. A. Robles-Flores (Eds.), International conference on social implications of computers in developing countries (pp. 347–362). Springer. https://doi.org/10.1007/978-3-031-19429-0_21
10.1007/978-3-031-19429-0_21 Google Scholar
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575
10.1007/BF02291575 Google Scholar
Kale, U. (2018). Technology valued? Observation and review activities to enhance future teachers’ utility value toward technology integration. Computers & Education, 117, 160–174. https://doi.org/10.1016/j.compedu.2017.10.007
10.1016/j.compedu.2017.10.007 Google Scholar
Keengwe, J., & Bhargava, M. (2013). Mobile learning and integration of mobile technologies in education. Education and Information Technologies, 19, 737–746. https://doi.org/10.1007/s10639-013-9250-3
10.1007/s10639-013-9250-3 Google Scholar
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers & Education: Artificial Intelligence, 5, 100178. https://doi.org/10.1016/j.caeai.2023.100178
10.1016/j.caeai.2023.100178 Google Scholar
Li, C., & Yanagisawa, H. (2021). Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions. Plos One, 16(4), e0250326. https://doi.org/10.1371/journal.pone.0250326
10.1371/journal.pone.0250326 Google Scholar
Lindeman, R. H., Merenda, P. F., & Gold, R. Z. (1980). Introduction to bivariate and multivariate analysis. Scott, Foresman and Company. https://doi.org/10.2307/2287559
10.2307/2287559 Google Scholar
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
10.3390/educsci13040410 Google Scholar
Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, 102362. https://doi.org/10.1016/j.techsoc.2023.102362
10.1016/j.techsoc.2023.102362 Google Scholar
Marshall, G., & Cox, M. J. (2008). Research methods: Their design, applicability and reliability. In J. Voogt & G. Knezed (Eds.), International handbook of information technology in primary and secondary education (pp. 983–1002). Springer. https://doi.org/10.1007/978-0-387-73315-9_62
10.1007/978-0-387-73315-9_62 Google Scholar
Menon, D., & Shilpa, K. (2023). “Chatting with ChatGPT”: Analyzing the factors influencing users’ intention to Use the Open AI’s ChatGPT using the UTAUT model. Heliyon, 9(11), e20962. https://doi.org/10.1016/j.heliyon.2023.e20962
10.1016/j.heliyon.2023.e20962 Google Scholar
Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023). Impact of the implementation of ChatGPT in education: A systematic review. Computers, 12(8), 153. https://doi.org/10.3390/computers12080153
10.3390/computers12080153 Google Scholar
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2, 100041. https://doi.org/10.1016/j.caeo.2021.100041
10.1016/j.caeo.2021.100041 Google Scholar
Nguyen, D. T. (2014). Giao trinh phuong phap nghien cuu khoa hoc trong kinh doanh [Textbook on Scientific Research Methods in Business]. Finance Publishing.
Google Scholar
Pham, V. L. P., Vu, D. A., Hoang, N. M., Do, X. L., & Luu, A. T. (2024). ChatGPT as a math questioner? Evaluating ChatGPT on generating pre-university math questions. In J. Hong, J. W. Park, & A. Przybylek (Eds.), Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (pp. 65–73). Association for Computing Machinery. https://doi.org/10.1145/3605098.363603
Google Scholar
Pituch, K. A., & Stevens, J. P. (2015). Applied multivariate statistics for the social sciences (6th ed.). Routledge.
Google Scholar
Rahim, N. I. M., Iahad, N. A., Yusof, A. F., & Al-Sharafi, M. A. (2022). AI-based chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-neural network modelling approach. Sustainability, 14(19), 12726. https://doi.org/10.3390/su141912726
10.3390/su141912726 Google Scholar
Singh, N., Sinha, N., & Liébana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191–205. https://doi.org/10.1016/j.ijinfomgt.2019.05.022
10.1016/j.ijinfomgt.2019.05.022 Google Scholar
Strzelecki, A. (2023). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49, 223–245. https://doi.org/10.1007/s10755-023-09686-1
10.1007/s10755-023-09686-1 Google Scholar
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 1–10. https://doi.org/10.37074/jalt.2023.6.1.17
10.37074/jalt.2023.6.1.17 Google Scholar
Szymkowiak, A., Melović, B., Dabić, M., Jeganathan, K., & Kundi, G. S. (2021). Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65, 101565. https://doi.org/10.1016/j.techsoc.2021.101565
10.1016/j.techsoc.2021.101565 Google Scholar
Talan, T., & Kalınkara, Y. (2023). The role of artificial intelligence in higher education: ChatGPT assessment for anatomy course. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi [International Journal of Management Information Systems and Computer Science], 7(1), 33–40. https://doi.org/10.33461/uybisbbd.1244777
10.33461/uybisbbd.1244777 Google Scholar
Teo, T., & Milutinovic, V. (2015). Modelling the intention to use technology for teaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 31(4), 363–380. https://doi.org/10.14742/ajet.1668
10.14742/ajet.1668 Google Scholar
Terblanche, N., & Kidd, M. (2022). Adoption factors and moderating effects of age and gender that influence the intention to use a non-directive reflective coaching chatbot. Sage Open, 12(2). https://doi.org/10.1177/21582440221096136
10.1177/21582440221096136 Google Scholar
Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024). AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models. Frontiers in Psychology, 15, 1–16. https://doi.org/10.3389/fpsyg.2024.1268549
10.3389/fpsyg.2024.1268549 Google Scholar
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
10.2307/30036540 Google Scholar
Voogt, J., Fisser, P., Pareja Roblin, N., Tondeur, J., & van Braak, J. (2012). Technological pedagogical content knowledge–a review of the literature. Journal of Computer Assisted Learning, 29(2), 109–121. https://doi.org/10.1111/j.1365-2729.2012.00487.x
10.1111/j.1365-2729.2012.00487.x Google Scholar
Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272
10.29333/ejmste/13272 Google Scholar
Young, D. S. (2017). Handbook of regression methods. Chapman and Hall/CRC. https://doi.org/10.1201/9781315154701
10.1201/9781315154701 Google Scholar

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