What drives Generation Z pre-service teachers’ acceptance of ChatGPT-assisted teaching practicum? The roles of enjoyment and trust

Authors

  • Laily Rahmatika Universitas Muhammadiyah Surakarta
  • M Monjurul Islam Universiti Pendidikan Sultan Idris
  • Ghadah Al Murshidi United Arab Emirates University
  • Samsudeen Sabraz Nawaz South Eastern University of Sri Lanka
  • Eka Mustika Riantina ekolah Tinggi Agama Islam Baturaja

DOI:

https://doi.org/10.24059/olj.v30i3.5273

Keywords:

ChatGPT, Generation Z, pre-service teachers, Technology Acceptance Model, teaching practicum

Abstract

The growing presence of generative AI (GenAI) tools such as ChatGPT is reshaping teacher education, yet there is still limited empirical attention to the emotional and relational aspects that shape their willingness to adopt. This study extends the Technology Acceptance Model (TAM) by incorporating perceived enjoyment and trust to examine how Generation Z pre-service teachers (PSTs) in Indonesia accept and use ChatGPT-assisted teaching practicums. A survey involving 445 participants was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results reveal that perceived enjoyment significantly predicts both perceived usefulness and perceived ease of use, while trust primarily affects perceived usefulness. Moreover, perceived enjoyment, trust, and perceived usefulness significantly shape Generation Z PSTs’ attitudes toward ChatGPT, which subsequently predict their intention to use it. Interestingly, trust does not significantly influence perceived ease of use, and perceived ease of use shows no direct effect on attitude. The extended model explains 60.2% of the variance in perceived usefulness, 46.0% in perceived ease of use, 49.0% in attitude, and 45.6% in intention to use, indicating moderate explanatory power. These results demonstrate that affective and relational dimensions significantly shape how pre-service teachers embrace ChatGPT, offering practical insights for the design of AI-supported practicum models that encourage ethical awareness, emotional engagement, and the long-term advancement of pedagogical innovation.

Author Biographies

M Monjurul Islam, Universiti Pendidikan Sultan Idris

  1. Monjurul Islam, PhD, is a senior lecturer in TESL at the Faculty of Languages and Communication at Sultan Idris Education University, Tanjong Malim, Perak, Malaysia. Dr. Islam, a TESL PhD holder from the University of Malaya, has published in esteemed journals like English Teaching & Learning, Teaching and Teacher Education, Asia TEFL, The Qualitative Report and Smart Learning Environments. His research interests include language policy and planning, professional development, English education, applied linguistics, curriculum and instruction, materials development, and technology integration in ESL. He serves on the editorial boards/reviewers of the Journal of Educators Online, Journal of Teaching and Learning, Education and Information Technologies, Educational Research and Evaluation, Interchange and International Journal of Educational Research. He can be contacted via email at monj0603@gamil.com. His ORCID ID is 0000-0002-0036-7174.

Ghadah Al Murshidi, United Arab Emirates University

Dr. Ghadah Al Murshidi is an Associate Professor at the College of Education, United Arab Emirates University (UAEU), with expertise in language and literacy, curriculum and instruction, and applied linguistics. She has led major national and international projects, including a UAEU-funded study on Emirati students and a KSA-backed project on global citizenship education. Her awards include the Young Emirati Researcher Prize, Best Young Researcher Award, and the Emirati Women Award. Her book The Guide of Creativity and Innovation in the UAE was recognized by H.H. Sheikh Hamad bin Mohammed Al Sharqi.

Samsudeen Sabraz Nawaz, South Eastern University of Sri Lanka

SAMSUDEEN. SABRAZ NAWAZ is a Professor in Management and IT at the Department of Management and IT, South Eastern University of Sri Lanka (SEUSL). He graduated with Distinction from the Sri Lanka Institute of Information Technology (SLIIT) with an MSc. in Information Systems and from SEUSL with a BBA (Hons.) in Information Systems, where he received First Class honours. The renowned Outstanding Paper Award that Prof. Sabraz Nawaz received in 2020 from Emerald Publishing, which recognises his devotion to cutting-edge research and innovation, is evidence of his commitment to excellence. His numerous academic accomplishments are demonstrated by the over fifty research articles he has published in reputable, indexed, peer-reviewed publications. He has also written books on various topics, including management information systems and electronic commerce.

Eka Mustika Riantina, ekolah Tinggi Agama Islam Baturaja

Dr. Eka mustika Riantina. SE.MSi. is a lecturer in Islamic religious education and a lecturer in sharia economics at the College of Islamic Religion Baturaja, South Sumatra. Her research is about the religious education system, religious tolerance, about religious teachers who teach religious education according to the religion of each student, about the facilities and infrastructure available at school according to the type of religion professed by each student. Some journals that have been published and some textbooks. can be contacted via email: ekamustika101973@gmail.com   

References

Adam, W., Pratama, I. P. Y., Handika, I., & Qudratuddarsi, H. ChatGPT acceptance and use for generation z pre-service science teacher: A survey study. (2025). Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan, 6(4), 787–800. https://doi.org/10.59698/afeksi.v6i4.497

Adouani, Y., & Khenissi, M. A. (2024). Investigating computer science students‘ intentions towards the use of an online educational platform using an extended technology acceptance model (e-TAM): An empirical study at a public university in Tunisia. Education and Information Technologies, 29(12), 14621–14645. https://doi.org/10.1007/s10639-023-12437-6

Aguilos, V., & Fuchs, K. (2024). Using an extended technology acceptance model (eTAM) to determine university students’ behavioral intentions of ChatGPT: An empirical study from Thailand. QWERTY-Interdisciplinary Journal of Technology, Culture and Education, 19(2), 102–123. https://doi.org/10.30557/QW000088

Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277. https://psycnet.apa.org/doi/10.1037/h0076477

Al Shamsi, J. H., Al-Emran, M., & Shaalan, K. (2022). Understanding key drivers affecting students’ use of artificial intelligence-based voice assistants. Education and Information Technologies, 27(6), 8071–8091. https://doi.org/10.1007/s10639-022-10947-3

Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 15381–15413. https://doi.org/10.1007/s10639-023-11816-3

Albayati, H. (2024). Investigating undergraduate students‘ perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers and Education: Artificial Intelligence, 6, 100203. https://doi.org/10.1016/j.caeai.2024.100203

Alzboon, M. S., Al-Shorman, H. M., Alka’awneh, S. M. N., Saatchi, S. G., Alqaraleh, M. K. S., Samara, E. I. M., ... & Haija, A. A. A. (2025). The role of perceived trust in embracing artificial intelligence technologies: Insights from SMEs. In Intelligence-Driven Circular Economy: Regeneration Towards Sustainability and Social Responsibility—Volume 2 (pp. 1–15). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-74220-0_1

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Bae, H., Hur, J., Park, J., Choi, G. W., & Moon, J. (2024). Pre-service teachers‘ dual perspectives on generative AI: Benefits, challenges, and integration into their teaching and learning. Online Learning, 28(3), 131–156. https://doi.org/10.24059/olj.v28i3.4543

Barbieri, W., & Nguyen, N. (2025). Generative AI as a “placement buddy”: Supporting pre-service teachers in work-integrated learning, self-management and crisis resolution. Australasian Journal of Educational Technology, 41(2), 34–49. https://doi.org/10.14742/ajet.10035

Benoit, O., Marc, K., Fernand, F., Dieter, F., & Martine, H. (2009). User-centered activity management system for elderly people Empowering older people with interactive technologies to manage their activities at the retirement home. In 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare (pp. 1–4). IEEE. https://doi.org/10.4108/ICST.PERVASIVEHEALTH2009.6042

Cacho, R. (2024). Integrating generative AI in university teaching and learning: A model for balanced guidelines. Online Learning, 28(3), 55–81. https://doi.org/10.24059/olj.v28i3.4508

Cano, J. R., & Nunez, N. A. (2024). Unlocking innovation: How enjoyment drives GenAI use in higher education. Frontiers in Education, 9, 1483853. https://doi.org/10.3389/feduc.2024.1483853

Chavoshi, A., & Hamidi, H. (2019). Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran. Telematics and Informatics, 38, 133–165. https://doi.org/10.1016/j.tele.2018.09.007

Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion adoption study. Information Systems Research, 14(2), 189–217. https://doi.org/10.1287/isre.14.2.189.16018

Chong, A. Y. L., Chan, F. T. S., & Ooi, K. B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34–43. https://doi.org/10.1016/j.dss.2011.12.001

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., ... & Soomro, R. B. (2024). Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(8). https://doi.org/10.1016/j.heliyon.2024.e29317

Daud, A., Nguyen, M. H., & Chowdhury, R. (2025). Navigating identity tensions and emotional struggles: Indonesian pre-service teachers in the teaching practicum. The Qualitative Report, 30(6), 3762–3783. https://doi.org/10.46743/2160-3715/2025.7226

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

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

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, 8(2), 257–282. https://doi.org/10.1007/s41979-024-00132-1

Fitriati, A., Anggoro, S., Talib, C. A., & Toh, T. L. (2025). The intention of Generation Z to use mobile learning: The role of self-efficacy and enjoyment. Turkish Online Journal of Distance Education, 26(1), 85–100. https://doi.org/10.17718/tojde.1445234

Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447. https://doi.org/10.1108/IntR-12-2017-0515

Gamlem, S. M., McGrane, J., Brandmo, C., Moltudal, S., Sun, S. Z., & Hopfenbeck, T. N. (2025). Exploring pre-service teachers’ attitudes and experiences with generative AI: A mixed methods study in Norwegian teacher education. Educational Psychology, 1–25. https://doi.org/10.1080/01443410.2025.2528663

Geddam, S. M., Nethravathi, N., & Hussian, A. A. (2024). Understanding AI adoption: The mediating role of attitude in user acceptance.

Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101

Sanusi, I. T., Ayanwale, M. A., & Tolorunleke, A. E. (2024). Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory. Computers and Education: Artificial Intelligence, 6, 100202. https://doi.org/10.1016/j.caeai.2024.100202

Sarwanti, S., Sariasih, Y., Rahmatika, L., Islam, M. M., & Riantina, E. M. (2024). Are they literate on ChatGPT? University language students’ perceptions, benefits and challenges in higher education learning. Online Learning, 28(3), 105–130. https://doi.org/10.24059/olj.v28i3.4599

Sasongko, A. T., Ekhsan, M., & Fatchan, M. (2025). Dataset on technology acceptance in E-learning: A PLS-SEM analysis using extended TAM among undergraduate students in Indonesia. Telematics and Informatics Reports, 18, 100192. https://doi.org/10.1016/j.teler.2025.100192

Sayaf, A. M., Alamri, M. M., Alqahtani, M. A., & Alrahmi, W. M. (2022). Factors influencing university students’ adoption of digital learning technology in teaching and learning. Sustainability, 14(1), 493. https://doi.org/10.3390/su14010493

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

Sun, J., Wu, Q., Ma, Z., Zheng, W., & Hu, Y. (2025). Understanding pre-service teachers’ acceptance of generative artificial intelligence: An extended technology acceptance model approach. Educational Technology Research and Development, 1–23. https://doi.org/10.1007/s11423-025-10495-w

Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–155. https://doi.org/10.1016/0167-8116(94)00019-K

Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2024). What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education, 21(3), 333–355. https://doi.org/10.1108/ITSE-04-2023-0061

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 157–178. https://doi.org/10.2307/41410412

Wu, H., Wang, Y., & Wang, Y. (2024). “To Use or Not to Use?” A mixed-methods study on the determinants of EFL college learners’ behavioral intention to use AI in the distributed learning context. International Review of Research in Open and Distributed Learning, 25(3), 158–178. https://doi.org/10.19173/irrodl.v25i3.7708

Wu, R., & Yu, Z. (2024). Investigating users’ acceptance of the metaverse with an extended technology acceptance model. International Journal of Human–Computer Interaction, 40(19), 5810–5826. https://doi.org/10.1080/10447318.2023.2241295

Wulandari, M., & Purnamaningwulan, R. A. (2024). Exploring Indonesian EFL pre-service teachers’ experiences in AI-assisted teaching practicum: Benefits and drawbacks. LLT Journal: A Journal on Language and Language Teaching, 27(2), 878–894. https://doi.org/10.24071/llt.v27i2.8690

Zhang, C., Schießl, J., Plößl, L., 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(1), 20–49. https://doi.org/10.1186/s41239-023-00420-7

Zhang, X., Rong, Z., Islam, M. M., Rahmatika, L., & Nawaz, S. S. (2026). Understanding GenAI adoption in EFL higher education: A dual-model study using SDT and TAM. Interactive Learning Environments, 1–19. https://doi.org/10.1080/10494820.2025.2612251

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Published

2026-09-01

How to Cite

Rahmatika, L., Islam, M. M., Al Murshidi, G., Nawaz, S. S. N., & Riantina, E. M. (2026). What drives Generation Z pre-service teachers’ acceptance of ChatGPT-assisted teaching practicum? The roles of enjoyment and trust. Online Learning, 30(3), 452–481. https://doi.org/10.24059/olj.v30i3.5273