Modeling pre-service EFL teachers’ attitudes toward Artificial Intelligence in language education

Arezoo Ashoori Tootkaboni; Mojtaba Maghsoudi

doi:10.6018/reifop.680741

Authors

Arezoo Ashoori Tootkaboni Farhangian University
Mojtaba Maghsoudi https://orcid.org/0000-0001-8780-5888

DOI: https://doi.org/10.6018/reifop.680741

Keywords: Artificial intelligence, language education, teacher education, technology integration

Supporting Agencies

This work has financial support of Farhangian University (Contract No. 50000/16288/120)

Abstract

Although there has been a lot of interest in the use of artificial intelligence (AI) in language instruction, yet research on pre-service English as a Foreign Language

teachers' (PSEFLTs) attitude toward AI remains limited. This study investigates the key determinants influencing PSEFLTs' attitudes toward AI integration in language education, focusing on perceived usefulness, perceived ease of use, AI confidence, perceived benefits of AI in teaching, and subjective norms. Utilizing a structural equation modeling (SEM) approach, data were collected from 128 PSEFLTs across three teacher education universities in Iran. The findings reveal that perceived usefulness, perceived ease of use, subjective norms, and perceived benefits of AI in teaching significantly shape PSEFLTs' attitudes toward AI, while AI confidence did not have a statistically significant effect. These findings highlight how crucial it is to implement focused teacher training programs in order to improve future educators' confidence and proficiency with AI integration. With its practical recommendations for policymakers and teacher educators in creating AI-focused curricula that meet pedagogical objectives, the study adds to the continuing conversation on AI in education.

Downloads

Download data is not yet available.

Metrics

Views/Downloads

Abstract
0
pdf EN (Español (España))
0

References

Adelana, O. P., Ayanwale, M. A., & Sanusi, I. T. (2024). Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring-based system. Cogent Education, 11(1), 2310976. https://doi.org/10.1080/2331186X.2024.2310976

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers' perspective. Computers and Education: Artificial Intelligence, 4, 100132. https://doi.org/10.1016/j.caeai.2023.100132

Alshorman, S. M. (2024). The readiness to use AI in teaching science: Science teachers’ perspective. Journal of Baltic Science Education, 23(3), 432-448.

Aptyka, H., & Großschedl, J. (2022). Analyzing pre-service biology teachers’ intention to teach evolution using the theory of planned behavior. Evolution: Education and Outreach, 15(1), 16. https://doi.org/10.1186/s12052-022-00175-1

Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099.

https://doi.org/10.1016/j.caeai.2022.100099

Ayanwale M. A., Ntshangase, S. D., Adelana O. P., Afolabi K. W., Adam U. A., & Olatunbosun, S. O. (2024). Navigating the future: Exploring in-service teachers’ preparedness for artificial intelligence integration into South African schools. Computers and Education: Artificial Intelligence, 7, 100330.https://doi.org/10.1016/j.caeai.2024.100330

Baddar, A., & Khan, M. A. (2023). Teachers' intention to use digital resources in classroom teaching: The role of teacher competence, peer influence, and perceived image. Higher Learning Research Communications, 13(2), 26-41.

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

Bakhadirov, M., & Alasgarova, R. (2024). Factors influencing teachers' use of artificial intelligence for instructional purposes. IAFOR Journal of Education, 12(2), 9-32.

Bandura, A., & Wessels, S. (1997). Self-efficacy (pp. 4-6). Cambridge: Cambridge University Press.

Bergdahl, N., & Sjöberg, J. (2025). Attitudes, perceptions and AI self-efficacy in K-12 education. Computers and Education: Artificial Intelligence, 8, 100358.

https://doi.org/10.1016/j.caeai.2024.100358

Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for modelling Chinese secondary school students’ intention to learn artificial intelligence. Mathematics, 8(11), 1–18. https://doi.org/10.3390/math8112089

Chai, C. S., Lin, P., Jong, M. S., Dai, Y., Chiu, T. K. F., & Qin, J. (2021). Perceptions of and behavioral intention towards learning artificial Iitelligence in primary school students. Educational Technology & Society, 24(3), 89–101.

https://www.jstor.org/stable/27032858

Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054-1064. https://doi.org/10.1016/j.compedu.2012.04.015

Chiu, T. K., Lim, C. P., & Wang, X. (2021). Adoption of AI in education: Examining the factors influencing teachers’ intentions and usage. Computers & Education, 173, 104289.

Conner, M., & Armitage, C. J. (1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28(15), 1429-1464.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.

Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1), 12. https://doi.org/10.1186/s40561-023-00231-3

Du, H., Sun, Y., Jiang, H., Islam, A. Y. M., & Gu, X. (2024). Exploring the effects of AI literacy in teacher learning: An empirical study. Humanities and social sciences communications, 11(1), 1-10. https://doi.org/10.1057/s41599-024-03101-6

El Shazly, R. (2021). Effects of artificial intelligence on English speaking anxiety and speaking performance: A case study. Expert Systems, 38(3), e12667.

https://doi.org/10.1111/exsy.12667

Fathi, J., Rahimi, M., & Derakhshan, A. (2024). Improving EFL learners’ speaking skills and willingness to communicate via artificial intelligence-mediated interactions. System, 121, 103254. https://doi.org/10.1016/j.system.2024.103254

Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The Reasoned action approach. London, UK: Psychology Press.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobserved variables and measurement error. Journal of Marketing Research, 18, 39–50.

Har, F., & Ma, B. W. L. (2023). The future of education utilizing an artificial intelligence robot in the centre for independent language learning: Teacher perceptions of the robot as a service. In Applied degree education and the shape of things to come (pp. 49-64). Singapore: Springer Nature Singapore.

Hapsari, I. P., & Wu, T. T. (2022). AI chatbots learning model in English speaking skill: Alleviating speaking anxiety, boosting enjoyment, and fostering critical thinking. In International conference on innovative technologies and learning (pp. 444-453). Cham: Springer International Publishing.

Hawanti, S., & Zubaydulloevna, K. M. (2023). AI chatbot-based learning: alleviating students' anxiety in English writing classroom. Bulletin of Social Informatics Theory and Application, 7(2), 182-192.

Hopcan, S., Polat, E., Ozturk, M. E., & Ozturk, L. (2023). Artificial intelligence in special education: A systematic review. Interactive Learning Environments, 31(10), 7335-7353. https://doi.org/10.1080/10494820.2022.2067186

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204.

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles, and research issues of AI in education. Computers & Education: Artificial Intelligence, 1, 100001.

Jeon, J. (2024). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language Learning, 37(1-2), 1-26. https://doi.org/10.1080/09588221.2021.2021241

Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies, 28(12), 15873-15892. https://doi.org/10.1007/s10639-023-11834-1

Junaidi, J. (2020). Artificial intelligence in EFL context: rising students’ speaking performance with Lyra virtual assistance. International Journal of Advanced Science and Technology Rehabilitation, 29(5), 6735-6741.

Kallunki, V., Kinnunen, P., Pyörälä, E., Haarala-Muhonen, A., Katajavuori, N., & Myyry, L. (2024). Navigating the evolving landscape of teaching and learning: University faculty and staff perceptions of the artificial intelligence-altered terrain. Education Sciences, 14(7), 727. https://doi.org/10.3390/educsci14070727

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E. and Krusche, S. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.35542/osf.io/5er8f

Kim, H. J., Hong, A. J., & Song, H. D. (2019). The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments. International Journal of Educational Technology in Higher Education, 16(1), 1-18. https://doi.org/10.1186/s41239-019-0152-3

Knauder, H., & Koschmieder, C. (2019). Individualized student support in primary school teaching: A review of influencing factors using the Theory of Planned Behavior (TPB). Teaching and Teacher Education, 77, 66-76.

https://doi.org/10.1016/j.tate.2018.09.012

Liu, M., Ren, Y., Nyagoga, L. M., Stonier, F., Wu, Z., & Yu, L. (2023). Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools. Future in Educational Research, 1(1), 72-101. https://doi.org/10.1002/fer3.10

Liu, G., & Ma, C. (2024). Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 18(2), 125-138. https://doi.org/10.1080/17501229.2023.2240316

Luckin, R. (2017). Machine learning and human intelligence: The future of education for the 21st century. UCL Press.

Ma, S., & Lei, L. (2024). The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education, 44(1), 94-111. https://doi.org/10.1080/02188791.2024.2305155

Maheshwari, G. (2024). Factors influencing students' intention to adopt and use ChatGPT in higher education: A study in the Vietnamese context. Education and Information Technologies, 29(10), 12167-12195. https://doi.org/10.1007/s10639-023-12333-z

Marzuki, Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10(2), 2236469.

https://doi.org/10.1080/2331186X.2023.2236469

Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., & Umalili, B. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10(1), 42. https://doi.org/10.1186/s40561-023-00261-x

Nunnally, J. C. Psychometric theory (2nd ed.). New York: McGraw-Hill, 1978.

Rajapakse, C., Ariyarathna, W., & Selvakan, S. (2024). A Self-Efficacy theory-based study on the teachers’ readiness to teach artificial intelligence in public schools in Sri Lanka. ACM Transactions on Computing Education, 24(4), 1-25.

https://dl.acm.org/doi/abs/10.1145/3691354

Ramnarain, U., Ogegbo, A. A., Penn, M., Ojetunde, S., & Mdlalose, N. (2024). Pre-service science teachers’ intention to use generative artificial intelligence in inquiry-based teaching. Journal of Science Education and Technology, 1-14.

https://doi.org/10.1007/s10956-024-10159-z

Rane, N., Choudhary, S., & Rane, J. (2024). Contribution of ChatGPT and similar generative Artificial Intelligence for enhanced climate change mitigation strategies. Available at SSRN 4681720. http://dx.doi.org/10.2139/ssrn.4681720

Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023). Developing middle school students’ understanding of machine learning in an African school. Computers and Education: Artificial Intelligence, 5, 100155.

https://doi.org/10.1016/j.caeai.2023.100155

Seufert, S., Guggemos, J., & Sailer, M. (2021). Technology-related knowledge, skills, and attitudes of pre-and in-service teachers: The current situation and emerging trends. Computers in Human Behavior, 115, 106552.

https://doi.org/10.1016/j.chb.2020.106552

Sadaf, A., & Johnson, B. L. (2017). Teachers' beliefs about integrating digital literacy into classroom practice: An investigation based on the theory of planned behavior. Journal of Digital Learning in Teacher Education, 33(4), 129-137.

https://www.researchgate.net/publication/319258692

Sanusi, I. T., Ayanwale, M. A., & Chiu, T. K. (2024). Investigating the moderating effects of social good and confidence on teachers' intention to prepare school students for artificial intelligence education. Education and information technologies, 29(1), 273-295. https://doi.org/10.1007/s10639-023-12250-1

Song, C., & Song, Y. (2023). Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology, 14, 1260843.

Tai, T. Y., & Chen, H. H. J. (2024). Improving elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions. Computers & Education, 220, 105112. https://doi.org/10.1016/j.compedu.2024.105112

Tatar, C., Jiang, S., Rosé, C. P., & Chao, J. (2024). Exploring teachers’ views and confidence in the integration of an artificial intelligence curriculum into their classrooms: a case study of curricular co-design program. International Journal of Artificial Intelligence in Education, 1-34. https://doi.org/10.1007/s40593-024-00404-2

Tenenhaus, M., Amato, S., & Esposito Vinzi, V. (2004). A global goodness-of-fit index for PLS structural equation modelling. In Proceedings of the XLII SIS scientific meeting, 1(2), 739-742.

Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024). Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human–Computer Interaction, 1-23. https://doi.org/10.1080/10447318.2024.2383033

Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34, 25–33.

Wijnen, F., van der Walma, J., & Voogt, J. (2021). Primary school teachers’ attitudes toward technology use and stimulating higher-order thinking in students: a review of the literature. https://doi.org/10.1080/15391523.2021.1991864

Xiao, Y., Zhang, T., & He, J. (2024). A review of promises and challenges of AI-based chatbots in language education through the lens of learner emotions. Heliyon. 10(18), e37238. https://doi.org/10.1016/j.heliyon.2024.e37238

Xu L. (2025). Factors influence readiness to use Artificial Intelligence (AI) applications in teaching and learning among college faculty members of China: A case study of Guangdong Medical University. The Educational Review, USA, 9(1), 117-132. http://dx.doi.org/10.26855/er.2025.01.017

Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 59. https://doi.org/10.1186/s40594-022-00377-5

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2021). Systematic review of research on artificial intelligence in higher education: Current trends and future directions. Journal of Computing in Higher Education, 33(1), 36-48.

https://doi.org/10.1186/s41239-019-0171-0

Zhang, B., Zhu, Y., Deng, J., Zheng, W., Liu, Y., Wang, C., & Zeng, R. (2023). “I am here to assist your tourism”: predicting continuance intention to use ai-based chatbots for tourism. does gender really matter?. International Journal of Human–Computer Interaction, 39(9), 1887-1903. https://doi.org/10.1080/10447318.2022.2124345

Zou, B., Du, Y., Wang, Z., Chen, J., & Zhang, W. (2023). An investigation into artificial intelligence speech evaluation programs with automatic feedback for developing EFL learners’ speaking skills. Sage Open, 13(3), 21582440231193818.

https://doi.org/10.1177/21582440231193818