Behavioural intentions to use ChatGPT: A case of pre-service teachers in colleges of education in Ghana

Kassim File Dangor; Jane Odurowaa Edjah

doi:10.51867/AQSSR.3.4.78

Auteurs-es

Kassim File Dangor Tumu College of Education Auteur-e https://orcid.org/0009-0000-2499-4933
Jane Odurowaa Edjah University of Cape Auteur-e https://orcid.org/0000-0002-7031-0050

DOI :

https://doi.org/10.51867/AQSSR.3.4.78

Mots-clés :

Artificial Intelligence (AI), Colleges of Education, ChatGPT, Pre-Service Teachers, Teacher Education

Résumé

Even though research on integration of Artificial Intelligence (AI) in higher education is promising, it appears pre-service teachers in Ghanaian colleges of education are not well represented. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study sought to explore ChatGPT acceptance and use among pre-service teachers in colleges of education in Ghana. The study adopted the descriptive cross-sectional design. Data were collected from a sample of 462 respondents using an online questionnaire. The data were analysed using SPSS and SmartPLS-SEM. Results of the study show no statistically significant differences in pre-service teachers’ views on performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and social influence (SI) based on sex. The results however show statistically significant sex difference in their behavioural intention to use ChatGPT in favour of males. Also, the study found that EE, FC, and SI predicted behavioural intention to use ChatGPT whilst PE did not. The study recommends to colleges of education to develop AI policies, modify curricular, and eliminate systemic barriers to ensure ethical integration and use of ChatGPT as a pedagogical tool.

Biographie de l'auteur-e

Jane Odurowaa Edjah, University of Cape

Counselling Centre, University of Cape Coast

Références

Abdul-Gafaar, S., Nyaaba, R. A., Anechana, R., & Anhwere, D. K. (2024). Exploring Cognitive Dissonance among Undergraduate Students at the Uni versity of Cape Coast: Investigating Resistance and Acceptance in the Adoption of Technol ogy. Creative Education, 15, 2499-2520. https://doi.org/10.4236/ce.2024.1512152

Adam, W., pratama, I., P. Y., Handika, I., & Qudratuddarsi, H. (2025). Chatgpt acceptance and use for generation z pre-service science teacher: A survey study. Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan, 6(4), 1-14. https://afeksi.id/jurnal/index.php/afeksi

https://doi.org/10.59698/afeksi.v6i4.497

Agormedah, E. K. (2025). Modelling pre-service business teachers' motivational beliefs and use of artificial intelligence in lesson planning-preparation during teaching practicum. In Adarkwah, M. A., Amponsah, S., Huang, R., Thomas, M. (eds) Artificial intelligence and human agency in education, 2. Future Education and Learning Spaces. Springer. https://doi.org/10.1007/978-981-969251-4_10

Amponsah, K. D., Adu-Boahen, J., Commey-Mintah, P., Kumassah, E. K., Ayittey, R. F., Nketsiah, J. (2026). Perceptions and acceptance of artificial intelligence in science education programmes: Voices of pre-service science teachers. Education Quarterly Reviews, 9(3), 51-65. https://doi.org/10.31014/aior.1993.09.03.724

Apata, O. E., Oyewole, P. O., Ajose, S. T., Adisa, I. O., Olaitan, G. I., Ajamobe, J. O., Mohammed, T., Awoyemi, I. D., Oladejo, P. O., & Oladipo, E. T. (2026). Generative AI in African higher education: A systematic review of opportunities, etical challenges, and institutional readiness. Journal of University Teaching and Learning Practice, 2393). https://doi.org/10.53761/c682ye61

Aravantinos, S., Lavidas, K., Komis, V., Karalis, T., & Papadakis, S. (2026). Artificial intelligence in K-12 education: A systematic review of teachers' professional development needs for AI integration. Computers, 15, 49, 1-30. https://doi.org/10.3390/computers15010049

Arthur, F., Gyedu, F. O., Quayson, E., Quaye, S. A., Boateng, E., Inkoom, M., Nortey, S. A., & Frempong, D. (2026). What drives preservice teachers' use of generative AI as instructional media? A structural and configurational analysis. Frontiers in Artificial Intelligence, 9, 1853130. https://doi.org/10.3389/frai.2026.1853130

Atuahene, E., & Boateng, O. (2026). Mathematics teachers' awareness, perceptions, and challenges in using ChatGPT. Discover Education, 5, 88. https://doi.org/10.1007/s44217-025-01083-4

Bahroun, Z., Anane, A., Ahmed, V., & Zacca, A. (2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sutainability, 15, 12983. https://doi.org/10.3390/su151712983

Beckman, K., Apps, T., Howard, S. K., Rogerson, C., Rogerson, A., & Tondeur, J. (2025). The GenAI divide among university students: A call for action research. The Internet and Higher Education, 67, 101036. https://doi.org/10/1016/j.iheduc.2025.101036

Chen, P., Lee, H., Huang, Y., & Wu, T. (2026). Unpacking the heterogeneity of pre-service teachers' ChatGPT acceptance: A latent profile analysis across STEM and non-STEM disciplines. International Journal of STEM Education, 13, 33. https://doi.org/10.1186/s40594-026-00634-x

Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th edn.). Taylor and Francis.

https://doi.org/10.4324/9780203029053

Costache, O., Verhoeff, R. P., & Gorissen, P. (2026). Teacher-AI collaboration and the professionalization of teachers in the age of automation: A systematic review. Education Sciences, 16, I938. https://doi.org/10.3390/educsci16060938

Dangor, K. F. & Osman, S. (2026). Teacher trainees' self-efficacy in fostering critical thinking and problem solving, creativity and innovation, and digital literacy as 21st century core skills in Ghana. Eastern African Journal of Humanities and Social Sciences, 5(1), 147-158. https://doi.org/10.58721/eajhss.v5i1.1646

Demir, B. (2026). Pre-service mathematics teachers' use of artificial intelligence: an extended technology acceptance model with self-efficacy (the case of ChatGPT). Front. Psychol. 17:1826980. https://doi.org/10.3389/fpsyg.2026.1826980

Devanadera, A. C. (2025). AI integration in education: A correlational study on attitudes, perceptions and anxiety among pre-service teachers. LatIA., 3, 249. https://doi.org/10.62486/latia2025249

Ebrahimi, Z., Kotamjani, S. S., Qosimov, A., & Xodabande, I. (2025). Mapping the emerging research landscape on applications of ChatGPT in Language teacher education: A systematic narrative literature review. Discover Artificial Intelligence, 5, 349. https://doi.org/10.1007/s44163-025-00631-z

Egara, F.O., & Mosimege, M. (2024). Exploring the integration of artificial intelligence-based ChatGPT into mathematics instruction: Perceptions, challenges, and implications for educators. Educ. Sci., 14, 742. https://doi.org/10.3390/educsci14070742

Eleje, L. I., Ezeugo, N. C. Esomonu, N. P. M., Metu, I. C., Anierobi, E. I., Mbelede, N. G., Nwosu, K. C., Ezeonwumelu, V. U., Ufearo, F. N., & Eleje, G. U. (2025). Artificial intelligence adoption in higher education in Nigeria. Discover Artificial Intelligence, 5, 335. https://doi.org/10.1007/s44163-025-00452-0

Hair, J. F., Hult, G. T. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd edn.). Sage Publications, Inc.

Iddrisu, H. M., & Iddrisu, S. A. (2025). Teacher readiness to use AI tools in the classroom. International Journal of Research in Education and Science (IJRES), 11(3), 494-510. https://doi.org/10.46328/ijres.1314

Indasari, N. L. (2026). Modelling acceptance of artificial intelligence technology among pre-service teachers in islamic higher education. At-Tadzkir: Islamic Education Journal, 5,

https://doi.org/10.59373/attadzkir.v5i1.241

Kaur, D. (2026). Artificial intelligence in education: In-service teachers' experiences of using ChatGPT for generating lesson plans. International Journal of Research in Education and Science (IJRES), 12(3), 763-774. https://doi.org/10.46328/ijres.7071

Kohnke, L., Zou, D., Ou, A. W., & Gu., M. M. (2025). Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration. Computers and Education: Artificial Intelligence, 8, 100398. https://doi.org/10.1016/j.caeai.2025.100398

Köse, N., & Kudratovna, K. M. (2025). Predictors of ChatGPT acceptance in language education: A study of pre-service ELT students in Uzbekistan. Research Square, 1, 1-21. https://doi.org/10.21203/rs.3.rs-7655776/v1

Köse, N., & Kudratovna, K. M. (2026). Determinants of ChatGPT acceptance among pre-service English language teaching students in Uzbekistan. Discover Education, 5, 315. https://doi.org/10.1007/s44217-026-01169-7

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610.

https://doi.org/10.1177/001316447003000308

Manzi, W. I., & Modise, M. A. (2026). Accounting teachers' voices on the integration of ChatGPT in Accounting Education: A case of South Africa. Journal of Education and Learning Technology (JELT), 7(2), 321-335. https://doi.org/10.38159/jelt.20267221

Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., Ukah, J. U., Eyo, E. O., Anari, M. I., & Cormelius-Ukpepi, B. U. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10, 42. https://doi.org/10.1186/s40561-023-00261-x

Nyaaba, M., & Zhai, X. (2024). Generative AI professional development needs for teacher educators. Journal of AI, 8(1), 1-13. https://doi.org/10.61969/jai.1385915

Nyaaba, M., Kyeremeh, P., Majialuwe, E. K., Owusu-Fordjour, C., Asebiga, E., & A-ingkoge, B. (2024). Generative ai in academic research: A descriptive study on awareness, gender usage, and views among pre-service teachers. Journal AI, 8(1), 45-60.

https://doi.org/10.61969/jai.1400867

OECD. (2024). Education policy outlook 2024: Reshaping teaching into a thriving profession from ABCs to AI. OECD

Örnek, F., Issah, M., Al-Mutawah, M. A., Abdel-Hameed, F. S. M., Alaam, S., & Örnek, B., N. (2026). Pre-service mathematics and science teacher's perceptions of ChatGPT as future educators. Discover Education, 5, 534. https://doi.org/10.1007/s44217-026-01567-x

Partey, P. A., Quayson, E., Adjei, S. K., & Antwi, J. (2026). Determinants of generative AI adoption in teacher education: A TTF-TPB model tested with PLS-SEM. Journal of Educational Technology Development and Exchange, 19(2), 86-123. https://doi.org/10.18785/jetde.1902.05

Ponera, J. M., & Madila, S. S. (2024). Harnessing the use of artificial intelligence among higher education institutions in Tanzania: Challenges and prospects. Edukasiana: Jurnal Inovasi Pendidikan, 393), 269-279. https://doi.org?10.56916/ejip.v3i3.714

Pu, S., Ahmed, N. A., Khambari, M. N., Yap, N. K., 7 Ahrari, S. (2021). Improvement of pre-service teachers' practical knowledge and motivation about artificial intelligence through a service-learning-based module in Guizhou, China: A quasi-experimental study. Asian Journal of University Education (AJUE), 17(3), 203-219.

https://doi.org/10.24191/ajue.v17i3.14499

Soko, J., Pete, J., & Mureithi, G. (2025). Assessing awareness and usage of AI tools among faculty and learners in higher education institutions in sub-saharan Africa. Asian Journal of Distance Education, 20(2), 63-75. http://www.asianjde.com/

Venkatesh, V., Morris, M. G., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://www.jstor.org/stable/30036540

Wainaina, P. K., & Sun, Y. (2025). Educators' perceptions and willingness to integrate generative artificial intelligence in teaching and research: evidence from Kenyan higher education. Discover Education, 4, 347. https://doi.org/10.1007/s44217-025-00820-z

Zhang, C., Schießl, J., Hofmann, F., & Gläser Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20, 49. https://doi.org/10.1186/s41239-023-00420-7