Exploring EFL Teachers’ Motivation and Influential Factors in Adopting AI for Language Teaching

Authors
1 Department of English Language, College of Arts, Jouf University, Saudi Arabia
2 Department of English Language and Literature, Allameh Tabataba’i University, Tehran, Iran
3 Department of English Language and Literature, Hakim Sabzevari University, Sabzevar, Iran
Abstract
The integration of AI in educational contexts has been a heated debate among scholars. Despite the important role of Artificial Intelligence (AI) in foreign language education, exploring English as a foreign language (EFL) teachers’ voices regarding their motivation to use AI in foreign language education has received scant attention. Therefore, this study explores the motivational factors influencing the adoption of AI in foreign language teaching through the lens of self-determination theory (SDT). Qualitative data were collected through semi-structured interviews from six EFL teachers in the context of Saudi Arabia. The study identified several key sub-themes within the broader motivational factors of relatedness, autonomy, and competence from the SDT framework. Regarding competence, participants emphasized the role of AI in enhancing their ability to deliver personalized instruction and manage their classrooms more efficiently. For relatedness, the sub-theme of mutual support and community building emerged as crucial, highlighting the importance of collaboration between teachers and students in AI adoption. In the context of autonomy, self-initiated professional development was prominent, reflecting teachers’ active efforts to stay updated on AI technologies. Teachers felt that mastering AI tools not only improved their competence but also enabled them to adapt AI-based solutions to meet students' individual needs. However, they also noted the challenge of ensuring that AI tools complement rather than replace critical pedagogical skills, highlighting the need to maintain a balance between using AI for efficiency and preserving essential human elements like creativity and critical thinking.

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Alam, A. (2023). Harnessing the power of AI to create intelligent tutoring systems for enhanced classroom experience and improved learning outcomes. In G. Rajakumar, K-L Du, & Á. Rocha (Eds.), Lecture notes on data engineering and communications technologies (pp. 571-591). https://doi.org/10.1007/978-981-99-1767-9_42
Bhutoria, A. (2022). Personalized education and AI in the United States, China, and India: A systematic review using a Human-In-The-Loop model. Computers and Education Artificial Intelligence, 3, Article 100068. https://doi.org/10.1016/j.caeai.2022.100068
Chen, Y. (2024). Effects of technology-enhanced language learning on reducing EFL learners’ public speaking anxiety. Computer Assisted Language Learning, 37(4), 789-813. https://doi.org/10.1080/09588221.2022.2055083
Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th ed). Routledge.
Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with AI (AI) based chatbot. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2023.2172044
Collie, R. J., & Martin, A. J. (2024). Teachers’ motivation and engagement to harness Generative AI for teaching and learning: The role of contextual, occupational, and background factors. Computers and Education: Artificial Intelligence, 6, Article 100224. https://doi.org/10.1016/j.caeai.2024.100224
Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches. Sage.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum.
Deci, E. L., & Ryan, R. M. (2012). Motivation, personality, and development within em-
bedded social contexts: An overview of self-determination theory. In: Ryan, R.M.
(Ed.), The Oxford handbook of human motivation (pp. 85-103). Oxford University Press.
Derakhshan, A., Kruk, M., Mehdizadeh, M., & Pawlak, M. (2021). Boredom in online classes in the Iranian EFL context: Sources and solutions. System, 101, Article 102556. https://doi.org/10.1016/j.system.2021.102556
Derakhshan, A., & Azari Noughabi, M. (2024). A self-determination perspective on the relationships between EFL learners’ foreign language peace of mind, foreign language enjoyment, psychological capital, and academic engagement. Learning and Motivation, 87, Article 102025. https://doi.org/10.1016/j.lmot.2024.102025
Derakhshan, A., & Ghiasvand, F. (2024). Is ChatGPT an evil or an angel for second language education and research? A phenomenographic study of research‐active EFL teachers’ perceptions. International Journal of Applied Linguistics. Advance online publication. https://doi.org/10.1111/ijal.12561
Derakhshan, A., & Shakki, F. (2024). How innovative are innovative research approaches in the psychology of the language teachers and learners: A state-of-the-art review. Language Related Research, 15(5), 1-34. http://doi.org/10.29252/LRR.15.5.1
Derakhshan, A., Teo, T., & Khazaie, S. (2024). Is game-based language learning general or specific-oriented? Exploring the applicability of mobile virtual realities to medical English education in the middle east. Computers & Education, 213, Article 105013. https://doi.org/10.1016/j.compedu.2024.105013
Derakhshan, A., Teo, T., Saeedy Robat, E., Janebi Enayat, M., & Jahanbakhsh, A. A. (2024). Robot-Assisted Language Learning: A Meta-Analysis. Review of Educational Research. Advance online publication. https://doi.org/10.3102/00346543241247227.
Derakhshan, A., & Zhang, L. J. (2024). Applications of psycho-emotional traits in technology-based language education (TBLE): An introduction to the special issue. The Asia-Pacific Education Researcher, 33, 741-745. https://doi.org/10.1007/s40299-024-00881-y
Ebadi, S., & Amini, A. (2022). Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using AI technology: A case of CSIEC chatbot. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2022.2096638
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59-82. https://doi.org/10.1177/1525822X05279903
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542-570. https://doi.org/10.1111/ejed.12533
Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, Article 104684. https://doi.org/10.1016/j.compedu.2022.104684
Kim, J. (2023). Leading teachers’ perspective on teacher-AI collaboration in education. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-023-12109-5
Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069-6104. https://doi.org/10.1007/s10639-021-10831-6
Mackey, A., & Gass, S. M. (2015). Second language research: Methodology and design. Routledge.
McEown, M. S., & Oga-Baldwin, W. Q. (2019). Self-determination for all language learners: New applications for formal language education. System, 86, Article 102124. https://doi.org/10.1016/j.system.2019.102124
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. sage.
Mohamed, A. M. (2024). Exploring the potential of an AI-based Chatbot (ChatGPT) in enhancing English as a Foreign Language (EFL) teaching: Perceptions of EFL faculty members. Education and Information Technologies, 29(3), 3195-3217. https://doi.org/10.1007/s10639-023-11917-z
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137-161. https://doi.org/10.1007/s11423-023-10203-6
Niu, W., Zhang, W., Zhang, C., & Chen, X. (2024). The role of artificial intelligence autonomy in higher education: A uses and gratification perspective. Sustainability, 16(3), Article 1276. https://doi.org/10.3390/su16031276
Núñez, J. L., Fernández, C., León, J., & Grijalvo, F. (2014). The relationship between teacher’s autonomy support and students’ autonomy and vitality. Teachers and Teaching, 21(2), 191-202. https://doi.org/10.1080/13540602.2014.928127
Oga-Baldwin, W. Q., Nakata, Y., Parker, P., & Ryan, R. M. (2017). Motivating young language learners: A longitudinal model of self-determined motivation in elementary school foreign language classes. Contemporary Educational Psychology, 49, 140-150. https://doi.org/10.1016/j.cedpsych.2017.01.010
Reeve, J. (2012). A SDT perspective on student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 149-172). Springer. https://doi.org/10.1007/978-1-4614-2018-7_7
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. https://doi.org/10.1006/ceps.1999.1020
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford publications.
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, Article 1260843. https://doi.org/10.3389/fpsyg.2023.1260843
Strauss, A., & Corbin, J. (1990). Basics of qualitative research. Sage publications.
Yurt, E., & Kasarci, I. (2024). A questionnaire of AI use motives: A contribution to investigating the connection between AI and motivation. International Journal of Technology in Education, 7(2), 308-325. https://doi.org/10.46328/ijte.725
Vallerand, R., & Ratelle, C. (2002). Intrinsic and extrinsic motivation: A hierarchical
model. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination (pp. 37-58). The University of Rochester Press.
Van Den Berg, G., & Du Plessis, E. (2023). ChatGPT and generative AI: Possibilities for its contribution to lesson planning, critical thinking and openness in teacher education. Education Sciences, 13(10), Article 998. https://doi.org/10.3390/educsci13100998
Walter, Y. (2024). Embracing the future of AI in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00448-3
Wei, L. (2023). AI in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14, Article 1261955. https://doi.org/10.3389/fpsyg.2023.1261955
Yu, H. (2023). Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 14, Article 1181712. https://doi.org/10.3389/fpsyg.2023.1181712
Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education Artificial Intelligence, 2, Article 100025. https://doi.org/10.1016/j.caeai.2021.100025
Zheng, Y., Wang, Y., Liu, K. S. X., & et al. (2024). Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learning. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-024-12763-3