Mathematics Student Teachers’ Behavioural Intention Using ChatGPT

Authors

  • Tang Minh Dung Ho Chi Minh City University of Education https://orcid.org/0000-0001-5401-1395
  • Vo Khoi Nguyen Ho Chi Minh City University of Education
  • Doan Cao Minh Tri Ho Chi Minh City University of Education
  • Phu Luong Chi Quoc Ho Chi Minh City University of Education
  • Bui Hoang Dieu Ban Ho Chi Minh City University of Education

DOI:

https://doi.org/10.21432/cjlt28665

Keywords:

artificial intelligence, behavioural intention, ChatGPT, Mathematics student teacher

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.

Author Biographies

Tang Minh Dung, Ho Chi Minh City University of Education

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, Ho Chi Minh City University of Education

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  ORCIDhttps://orcid.org/0009-0009-3738-6967

Doan Cao Minh Tri, Ho Chi Minh City University of Education

Doan Cao Minh Tri 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. Emailminhtridoancao06@gmail.com ORCID: https://orcid.org/0009-0001-7783-1393

Phu Luong Chi Quoc, Ho Chi Minh City University of Education

Phu Luong Chi Quoc 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 ORCIDhttps://orcid.org/0009-0009-4151-954X

Bui Hoang Dieu Ban, Ho Chi Minh City University of Education

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. Emailbuihoangdieuban12012003@gmail.com ORCID: https://orcid.org/0009-0008-1696-1195

References

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

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

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/

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Frost, J. (2019). Regression analysis: An intuitive guide for using and interpreting linear models. Statistics by Jim Publishing.

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

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

Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2018). Multivariate data analysis (8th ed.). Cengage.

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

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.

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

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: with applications in R (2nd ed.). Springer.

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

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

Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575

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

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

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

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

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

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

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

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

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

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

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

Nguyen, D. T. (2014). Giao trinh phuong phap nghien cuu khoa hoc trong kinh doanh [Textbook on Scientific Research Methods in Business]. Finance Publishing.

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

Pituch, K. A., & Stevens, J. P. (2015). Applied multivariate statistics for the social sciences (6th ed.). Routledge.

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

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

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

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

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

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

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

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

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

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

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

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

Young, D. S. (2017). Handbook of regression methods. Chapman and Hall/CRC. https://doi.org/10.1201/9781315154701

Published

2025-04-04

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Articles