Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education

Halit Karalar; Sedat Altıntaş; Bilge Aslan Altan

doi:10.17275/per.26.82.13.6

Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education

Abstract

Generative artificial intelligence tools, particularly applications such as ChatGPT, hold significant potential to reshape the landscape of teacher education by offering opportunities for personalized feedback and adaptive learning. These tools also play important roles in teachers’ pedagogical decisions while designing their teachings in present learning environments. However, the underlying mechanisms driving pre-service teachers’ adoption of such particular tools remain under-explored. This study addresses this gap by validating an extended technology acceptance model incorporating constructivist teaching beliefs, subjective norms, facilitating conditions, ChatGPT self-efficacy, and perceived enjoyment. Data collected from 488 senior pre-service teachers in Türkiye were analyzed using structural equation modeling. Results show that ChatGPT self-efficacy and perceived enjoyment significantly predict perceived ease of use, whereas facilitating conditions do not exert a significant influence. Perceived usefulness is significantly associated with perceived ease of use, subjective norms, and pedagogical beliefs. Behavioral intention is significantly associated with perceived usefulness, subjective norms, and ChatGPT self-efficacy, with the framework explaining 54% of the variance. Given the cross-sectional nature of the data, these results offer tentative implications for teacher education. Practically, the findings highlight the potential value of scaffolded micro-teaching and institutional verification procedures for supporting responsible and equitable generative AI use in teacher education. Further recommendations are discussed in the light of teacher education procedures and policies.

Keywords

ChatGPT acceptance, generative artificial intelligence (GenAI), technology acceptance model (TAM), constructivist teaching beliefs, pre-service teachers, structural equation modeling (SEM)

Ethical Statement

Author/s declare presence of Ethics Statements that needed for ethical conduct of research using human subjects.

References

Acosta-Enriquez, B. G., Ballesteros, M. A. A., de los Angeles Guzman Valle, M., Angaspilco, J. E. M., Blanco-García, L. E., Ventura, G. C., Requejo, J. D. C., & Torre, M. C. (2025). Determinants of AI use in university teachers: The role of leadership, teaching concerns, and constructivist pedagogical beliefs. Human Behavior and Emerging Technologies, 2025(1). https://doi.org/10.1155/hbe2/4834893
Acquah, B. Y. S., Arthur, F., Salifu, I., Quayson, E., & Nortey, S. A. (2024). Preservice teachers’ behavioural intention to use artificial intelligence in lesson planning: A dual-staged PLS-SEM-ANN approach. Computers and Education: Artificial Intelligence, 7, 100307. https://doi.org/10.1016/j.caeai.2024.100307
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
Ajzen, I. (1991). The theory of planned behavior. Handbook of Theories of Social Psychology, 1, 179–211. https://doi.org/10.4135/9781446249215.n22
Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024). ChatGPT in teaching and learning: A systematic review. Education Sciences, 14(6), 643. https://doi.org/10.3390/educsci14060643
Alrishan, A. M. H. (2023). Determinants of intention to use ChatGPT for professional development among Omani EFL pre-service teachers. International Journal of Learning, Teaching and Educational Research, 22(12), 187–209. https://doi.org/10.26803/ijlter.22.12.10
Aypay, A., Coskun-Celik, H., Aypay, A., & Sever, M. (2012). Technology acceptance in education: A study of pre-service teachers in Turkey. Turkish Online Journal of Educational Technology-TOJET, 11(4), 264-272. Retrieved January 5, 2026, from https://www.tojet.net/articles/v11i4/11426.pdf
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
Buchholz, K. (2023, July 7). Threads shoots past one million user mark at lightning speed. Statista Daily Data. Retrieved May 30, 2024, from https://www.statista.com/chart/29174/time-to-one-million-users/
Cai, H., Han, B., Sun, J., Li, X., & Wong, L. H. (2025). Harnessing AI for teacher education to promote inclusive education: Investigating the effects of ChatGPT-supported lesson plan critiques on the development of pre-service teachers’ lesson planning skills. Internet and Higher Education, 67(May), 101022. https://doi.org/10.1016/j.iheduc.2025.101022

Cambra-Fierro, J. J., Blasco, M. F., López-Pérez, M. E., & Trifu, A. (2024). ChatGPT adoption and its influence on faculty well-being: An empirical research in higher education. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12871-0
Chang, H., Liu, B., Zhao, Y., Li, Y., & He, F. (2024). Research on the acceptance of ChatGPT among different college student groups based on latent class analysis. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2024.2331646
Chen, C., Wang, C., Shan, X., Zhan, L., & Chen, S. (2024). An empirical investigation of reasons influencing pre-service teachers’ acceptance and rejection of immersive virtual reality usage. Teaching and Teacher Education, 137, 104391. https://doi.org/10.1016/j.tate.2023.104391
Crompton, H., & Burke, D. (2024). The educational affordances and challenges of ChatGPT: State of the field. TechTrends, 68(2), 380–392. https://doi.org/10.1007/s11528-024-00939-0
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B., & Shutaleva, A. (2024). Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Education and Information Technologies, 29, 18695–18744. https://doi.org/10.1007/s10639-024-12599-x
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
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human Computer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040
Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1206936
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2022). How to design and evaluate research in education (11th ed.). McGraw Hill.
Ertmer, P. A. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–61. https://doi.org/10.1007/BF02299597
Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25–39. https://doi.org/10.1007/BF02504683
Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Gana, K., & Broc, G. (2019). Structural equation modeling with lavaan. John Wiley & Sons.
Gao, M., Zhang, H., Dong, Y., & Li, J. (2025). Embracing generative AI in education: an experiential study on preservice teachers’ acceptance and attitudes. Educational Studies, 1–20. https://doi.org/10.1080/03055698.2025.2483831
Gökçearslan, Ş., Yildiz Durak, H., & Atman Uslu, N. (2024). Acceptance of educational use of the Internet of Things (IoT) in the context of individual innovativeness and ICT competency of pre-service teachers. Interactive Learning Environments, 32(2), 557–571. https://doi.org/10.1080/10494820.2022.2091612
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Gurer, M. D. (2021). Examining technology acceptance of pre-service mathematics teachers in Turkey: A structural equation modeling approach. Education and Information Technologies, 26(4), 4709-4729. https://doi.org/10.1007/s10639-021-10493-4
Hair, J. F., Blacks, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Haque, M. A., & Li, S. (2025). Exploring ChatGPT and its impact on society. AI and Ethics, 5(2), 791–803. https://doi.org/10.1007/s43681-024-00435-4
Heung, Y. M. E., & Chiu, T. K. F. (2025). How ChatGPT impacts student engagement from a systematic review and meta-analysis study. Computers and Education: Artificial Intelligence, 8, 100361. https://doi.org/10.1016/j.caeai.2025.100361
Hew, K. F., & Brush, T. (2006). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research & Development, 55(3), 223–252. https://doi.org/10.1007/s11423-006-9022-5
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
Hirschfeld, G., & Von Brachel, R. (2014). Multiple-group confirmatory factor analysis in R - A tutorial in measurement invariance with continuous and ordinal indicators. Practical Assessment, Research and Evaluation, 19(7), 1–12. Retrieved December 12, 2025, from https://files.eric.ed.gov/fulltext/EJ1038101.pdf
Hojeij, Z., Kuhail, M. A., & ElSayary, A. (2024). Investigating in-service teachers’ views on ChatGPT integration. Interactive Technology and Smart Education, 22(4), 548–573. https://doi.org/10.1108/itse-04-2024-0094
Hu, Y. H. (2021). Effects and acceptance of precision education in an AI-supported smart learning environment. Education and Information Technologies, 27(2), 2013–2037. https://doi.org/10.1007/s10639-021-10664-3
Huang, F., Sánchez-Prieto, J. C., Teo, T., García-Peñalvo, F. J., Olmos-Migueláñez, S., & Zhao, C. (2021). A cross-cultural study on the influence of cultural values and teacher beliefs on university teachers’ information and communications technology acceptance. Educational Technology Research and Development, 69(2), 1271–1297. https://doi.org/10.1007/s11423-021-09941-2
Huang, F., & Teo, T. (2021). Examining the role of technology-related policy and constructivist teaching belief on English teachers’ technology acceptance: A study in Chinese universities. British Journal of Educational Technology, 52(1), 441–460. https://doi.org/10.1111/bjet.13027
Islamoglu, H., Yurdakul, I. K., & Ursavas, O. F. (2021). Pre-service teachers’ acceptance of mobile-technology-supported learning activities. Educational Technology Research and Development, 69(2), 1025–1054. https://doi.org/10.1007/s11423-021-09973-8
Jing, Y., Wang, H., Chen, X., & Wang, C. (2024). What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanities and Social Sciences Communications, 11(1), 1-12. https://doi.org/10.1057/s41599-024-02751-w
Khlaisang, J., Teo, T., & Huang, F. (2021) Acceptance of a flipped smart application for learning: a study among Thai university students. Interactive Learning Environments, 29(5), 772-789. https://doi.org/10.1080/10494820.2019.1612447
Khlaisang, J., Huang, F., Koraneekij, P., & Teo, T. (2023). Using mobile technologies to teach 21st century learning skills: a study of teachers’ acceptance in Thai secondary schools. International Journal of Mobile Learning and Organisation, 17(1–2), 254–279. https://doi.org/10.1504/ijmlo.2023.128343
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
Korucu-Kış, S. (2024). Zone of proximal creativity: An empirical study on EFL teachers’ use of ChatGPT for enhanced practice. Thinking Skills and Creativity, 54, 101639. https://doi.org/10.1016/j.tsc.2024.101639
Labadze, L., Grigolia, M. & Machaidze, L. (2023). Role of AI chatbots in education: systematic literature review. International Journal of Educational Technology in Higher Education, 20(56). https://doi.org/10.1186/s41239-023-00426-1
Lee, S., & Kim, S. (2024). Preservice teachers’ learning by design through space construction in the metaverse. British Journal of Educational Technology, 56, 208–230. https://doi.org/10.1111/bjet.13493
Lim, C.W., Liew, K.Y., Lim, S.H. et al. (2024). Student acceptance towards AsepticTech VR: a teaching and learning tool for cell and tissue culture aseptic techniques. International Journal of Educational Technology in Higher Education, 21(36). https://doi.org/10.1186/s41239-024-00472-3
Mei, B., Brown, G. T. L., & Teo, T. (2018). Toward an understanding of preservice English as a foreign language teachers’ acceptance of computer-assisted language learning 2.0 in the people’s Republic of China. Journal of Educational Computing Research, 56(1), 74–104. https://doi.org/10.1177/0735633117700144
Mejía-Mancilla, J., & Mejía-Trejo, J. (2024). Technology acceptance model for smartphone use in higher education. Scientia Et PRAXIS, 4(7), 113–158. https://doi.org/10.55965/setp.4.07.a5
Milmo, D. (2023). ChatGPT reaches 100 million users two months after launch. The Guardian. Retrieved May 30, 2024, from https://www.theguardian.com/technology/2023/feb/02/chatgpt-100-million-users-open-ai-fastest-growing-app.
Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, López-Pernas, S., & Saqr, M. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda. Smart Learning Environments, 11(59). https://doi.org/10.1186/s40561-024-00350-5
Ramnarain, U., Ogegbo, A. A., Penn, M., Ojetunde, S., & Mdlalose, N. (2025). Pre-service science teachers’ intention to use generative artificial intelligence in inquiry-based teaching. Journal of Science Education and Technology, 34, 1272–1285. https://doi.org/10.1007/s10956-024-10159-z
Rogers, E. M. (1983). Diffusion of innovations. Free Press.
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
Sánchez-Prieto, J. C., Hernández-García, Á., García-Peñalvo, F. J., Chaparro-Peláez, J., & Olmos-Migueláñez, S. (2019). Break the walls! Second-order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158-167. https://doi.org/10.1016/j.chb.2019.01.019
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Scherer, R., & Teo, T. (2019). Unpacking teachers’ intentions to integrate technology: A meta-analysis. Educational Research Review, 27, 90–109. https://doi.org/10.1016/j.edurev.2019.03.001
Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21(46). https://doi.org/10.1186/s41239-024-00478-x
Sharples, M. (2023). Towards social generative AI for education: theory, practices and ethics. Learning: Research and Practice, 9(2), 159–167. https://doi.org/10.1080/23735082.2023.2261131
Şimşek, N. (2025). Integration of ChatGPT in mathematical story-focused 5E lesson planning: Teachers and pre-service teachers’ interactions with ChatGPT. Education and Information Technologies, 30(8), 11391–11462. https://doi.org/10.1007/s10639-024-13258-x
Sing, C. C., Teo, T., Huang, F., Chiu, T. K., & Xing Wei, W. (2022). Secondary school students’ intentions to learn AI: Testing moderation effects of readiness, social good and optimism. Educational technology research and development, 70(3), 765-782. https://doi.org/10.1007/s11423-022-10111-1
Soper, D. S. (2021). A-priori sample size calculator for structural equation models. Retrieved January 2, 2026, from https://www.danielsoper.com/statcalc/references.aspx?id=89
Sukirman, S., Ibharim, L. F. M., Said, C. S., & Murtiyasa, B. (2024). Development and usability testing of a virtual reality game for learning computational thinking. International Journal of Serious Games, 11(3), 19–43. https://doi.org/10.17083/ijsg.v11i3.670
Strzelecki, A. (2024). 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
Strzelecki, A., Cicha, K., Rizun, M., & Rutecka, P. (2024). Acceptance and use of ChatGPT in the academic community. Education and Information Technologies, 29, 22943–22968. https://doi.org/10.1007/s10639-024-12765-1
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, 73, 1975–1997. https://doi.org/10.1007/s11423-025-10495-w
Teo, T. (2015). Comparing preservice and in-service teachers’ acceptance of technology: Assessment of measurement invariance and latent mean differences. Computers and Education, 83, 22–31. https://doi.org/10.1016/j.compedu.2014.11.015
Teo, T., Chai, C. S., Hung, D., & Lee, C. B. (2008). Beliefs about teaching and uses of technology among pre‐service teachers. Asia-Pacific Journal of Teacher Education, 36(2), 163–174. https://doi.org/10.1080/13598660801971641
Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education, 53(3), 1000–1009. https://doi.org/10.1016/j.compedu.2009.05.017
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). https://doi.org/10.14742/ajet.1668
Teo, T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & education, 57(2), 1645-1653. https://doi.org/10.1016/j.compedu.2011.03.002
Teo, T., & Van Schaik, P. (2012). Understanding the intention to use technology by preservice teachers: An empirical test of competing theoretical models. International Journal of Human-Computer Interaction, 28(3), 178-188. https://doi.org/10.1080/10447318.2011.581892
Teo, T., & Zhou, M. (2017). The influence of teachers’ conceptions of teaching and learning on their technology acceptance. Interactive Learning Environments, 25(4), 513–527. https://doi.org/10.1080/10494820.2016.1143844
Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475. https://doi.org/10.1080/10494820.2017.1341940
Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating preservice teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4), 530–546. https://doi.org/10.1080/10494820.2018.1489290
UNESCO (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Retrieved May 30, 2024, from https://unesdoc.unesco.org/ark:/48223/pf0000366994
UNESCO. (2024). AI competency framework for teachers. Retrieved January 10, 2025, from https://www.cedefop.europa.eu/files/unesco_ai_competency_framework_for_teachers.pdf
Ursavaş, Ö. F., Yalçın, Y., & Bakır, E. (2019). The effect of subjective norms on preservice and in‐service teachers’ behavioural intentions to use technology: A multigroup multimodel study. British Journal of Educational Technology, 50(5), 2501–2519. https://doi.org/10.1111/bjet.12834
Ursavaş, Ö. F., Yalçın, Y., İslamoğlu, H., Bakır-Yalçın, E., & Cukurova, M. (2025). Rethinking the importance of social norms in generative AI adoption: Investigating the acceptance and use of generative AI among higher education students. International Journal of Educational Technology in Higher Education, 22(38). https://doi.org/10.1186/s41239-025-00535-z
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
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., 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
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 36(1), 157–178. https://doi.org/10.2307/41410412
Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(621). https://doi.org/10.1057/s41599-025-04787-y
Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/10.2307/27032860
Wang, K., Ruan, Q., Zhang, X., Fu, C., & Duan, B. (2024). Pre-service teachers’ GenAI anxiety, technology self-Efficacy, and TPACK: Their structural relations with behavioral intention to design GenAI-assisted teaching. Behavioral Sciences, 14(5), 373. https://doi.org/10.3390/bs14050373
Yadav, B., & Sharma, K. (2024). The impact of artificial intelligence on the education system. Journal of Communication and Management, 3(02), 121–127. https://doi.org/10.58966/JCM2024326
Yang, T., & Chen, J. (2022). Pre-service teachers’ perceptions and intentions regarding the use of chatbots through statistical and lag sequential analysis. Computers and Education Artificial Intelligence, 4, 100119. https://doi.org/10.1016/j.caeai.2022.100119
Zaragoza, A., Seidel, T., & Santagata, R. (2023). Lesson analysis and plan template: scaffolding preservice teachers’ application of professional knowledge to lesson planning. Journal of Curriculum Studies, 55(2), 138–152. https://doi.org/10.1080/00220272.2023.2182650
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(49). https://doi.org/10.1186/s41239-023-00420-7
Zhang, P., & Tur, G. (2024). A systematic review of ChatGPT use in K-12 education. European Journal of Education, 59, e12599. https://doi.org/10.1111/ejed.12599
Zhong, Y., Lian, J., Huang, H., & Deng, H. (2025). Affordances, constraints, and implications of ChatGPT in education from a social-ecological perspective: A data mining approach. Education and Information Technologies, 30(12), 16407–16440. https://doi.org/10.1007/s10639-024-13237-2

Details

Primary Language

English

Subjects

Instructional Technologies

Journal Section

Research Article

Authors

Halit Karalar ^*
0000-0001-9344-9672
Türkiye

Sedat Altıntaş
0000-0003-4637-561X
Türkiye

Bilge Aslan Altan
0000-0002-3309-933X
Türkiye

Publication Date

November 1, 2026

Submission Date

April 2, 2026

Acceptance Date

August 5, 2026

Published in Issue

Year 2026 Volume: 13 Number: 6

DOI

https://doi.org/10.17275/per.26.82.13.6

IZ

https://izlik.org/JA79ZS82XE

APA

Karalar, H., Altıntaş, S., & Aslan Altan, B. (2026). Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education. Participatory Educational Research, 13(6), 145-167. https://doi.org/10.17275/per.26.82.13.6

AMA

1.Karalar H, Altıntaş S, Aslan Altan B. Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education. PER. 2026;13(6):145-167. doi:10.17275/per.26.82.13.6

Chicago

Karalar, Halit, Sedat Altıntaş, and Bilge Aslan Altan. 2026. “Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education”. Participatory Educational Research 13 (6): 145-67. https://doi.org/10.17275/per.26.82.13.6.

EndNote

Karalar H, Altıntaş S, Aslan Altan B (November 1, 2026) Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education. Participatory Educational Research 13 6 145–167.

IEEE

[1]H. Karalar, S. Altıntaş, and B. Aslan Altan, “Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education”, PER, vol. 13, no. 6, pp. 145–167, Nov. 2026, doi: 10.17275/per.26.82.13.6.

ISNAD

Karalar, Halit - Altıntaş, Sedat - Aslan Altan, Bilge. “Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education”. Participatory Educational Research 13/6 (November 1, 2026): 145-167. https://doi.org/10.17275/per.26.82.13.6.

JAMA

1.Karalar H, Altıntaş S, Aslan Altan B. Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education. PER. 2026;13:145–167.

MLA

Karalar, Halit, et al. “Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education”. Participatory Educational Research, vol. 13, no. 6, Nov. 2026, pp. 145-67, doi:10.17275/per.26.82.13.6.

Vancouver

1.Halit Karalar, Sedat Altıntaş, Bilge Aslan Altan. Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education. PER. 2026 Nov. 1;13(6):145-67. doi:10.17275/per.26.82.13.6

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Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education

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