Volume 17 (2025) Download Cover Page

Between Documentation and Pedagogy: ESL/EFL Teacher Burnout and Perceptions of AI's Potential for Workload Relief

Article Number: e2025309  |  Available Online: July 2025  |  DOI: 10.22521/edupij.2025.17.309

Wael Alharbi

Abstract

Background/purpose. ESL/EFL teachers face unique burnout challenges due to quality assurance (QA) requirements, increasing class sizes, and intensive feedback demands. This research investigates how these factors contribute to teacher burnout in Saudi higher education and explores the potential of AI as a workload solution. Grounded in the Job Demands-Resources model and Technology Acceptance Model, the study examines burnout drivers, feedback practices, class size effects, AI perceptions, and demographic variation.

Materials/methods. This mixed-methods study collected quantitative data from 258 ESL/EFL teachers and qualitative insights from 15 interviews and 5 focus groups. Analysis employed structural equation modeling, LASSO regression, and mediation analysis to examine burnout mechanisms and potential technological interventions.

Results. Findings reveal that QA standards significantly increase workload while reducing teachers' ability to provide individualized feedback, with workload acting as a powerful mediator of teacher wellbeing. While educators recognize AI's potential to support administrative tasks, significant adoption gaps persist—particularly among highly qualified and experienced staff who express concerns about professional identity and pedagogical displacement.

Conclusion. This study uniquely frames AI as a classroom assistant rather than a replacement for pedagogical judgment, offering empirical evidence that teacher-centered AI integration could alleviate workload stress while preserving professional autonomy. Recommendations include recalibrating QA implementation and developing AI systems that complement rather than replace teacher expertise in ESL/EFL contexts.

Keywords: Teacher burnout, ESL/EFL, quality assurance, artificial intelligence, Job Demands-Resources model, Saudi higher education, feedback workload

References

Agyapong, B., Obuobi-Donkor, G., Burback, L., & Wei, Y. (2023). Burnout and Associated Psychological Issues Among Teachers: A Scoping Review. European Psychiatry, 66, S948 - S949. https://doi.org/10.1192/j.eurpsy.2023.2010

Alghamdy, R. Z. (2023). Pedagogical and ethical implications of artificial intelligence in EFL context: A review study. English Language Teaching16(10), 87-98. https://doi.org/10.5539/elt.v16n10p87 

Almossa, S. Y., & Alzahrani, S. M. (2022). Transnational English Language Teachers' Assessment Practices in Higher Education TESOL Landscape. In Transnational English Language Assessment Practices in the Age of Metrics (pp. 93-106). Routledge. http://doi.org/10.4324/9781003252382-9

Alsalem, M. S. (2024). EFL teachers’ perceptions of the use of an AI grading tool (CoGrader) in English writing assessment at Saudi universities: an Activity Theory Perspective. Cogent Education11(1). https://doi.org/10.1080/2331186X.2024.2430865

Alshahrani, A., & Storch, N. (2025). Investigating the effectiveness of scaffolded feedback on EFL Saudi students' writing accuracy: A longitudinal classroom-based study. Assessing Writing63, 100910. https://doi.org/10.1016/j.asw.2024.100910

Alutaybi, M. M., & Alfares, N. S. (2024). EFL teachers’ perspective of teaching large online classes: Issues and challenges. Journal of Language Teaching and Research, 15(1), 255-262. https://doi.org/10.17507/jltr.1501.28

An, S., & Tao, S. (2024). English as a foreign language teachers' burnout: The predicator powers of self-efficacy and well-being. Acta psychologica, 245, 104226 . https://doi.org/10.1016/j.actpsy.2024.104226.

Bakker A. B., Demerouti E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056

Barrot, J. S. (2023). Using automated written corrective feedback in the writing classrooms: Effects on L2 writing accuracy. Computer Assisted Language Learning, 36(4), 584–607. https://doi.org/10.1080/09588221.2021.1936071

Bodenheimer, G., & Shuster, S. M. (2019). Emotional labour, teaching and burnout: Investigating complex relationships. Educational Research62(1), 63–76. https://doi.org/10.1080/00131881.2019.1705868

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa 

Chang, H. (2022). Stress and burnout in EFL teachers: The mediator role of self-efficacy. Frontiers in psychology13, 880281. https://doi.org/10.3389/fpsyg.2022.880281.

Chen, F., Wang, X., & Gao, Y. (2024). EFL teachers' burnout in technology-enhanced instruction settings: The role of personality traits and psychological capital. Acta Psychologica249, 104461. https://doi.org/10.1016/j.actpsy.2024.104461.

Chen, J., Lin, C., & Lin, F. (2024). The Interplay among EFL Teachers' Emotional Intelligence, Self-Efficacy, and Burnout Acta Psychologica, 248, 104364. https://doi.org/10.1016/j.actpsy.2024.104364.

Chen, X., Wang, X., & Qu, Y. (2023). Constructing ethical AI based on the “Human-in-the-Loop” system. Systems, 11(11), Article 548. https://doi.org/10.3390/systems11110548

Colonna, L. (2023). Teachers in the loop? An analysis of automatic assessment systems under Article 22 GDPR. International Data Privacy Law, 14(1), 3–18. https://doi.org/10.1093/idpl/ipad024

Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage Publications.

Dakakni, D., & Safa, N. (2023). Artificial intelligence in the L2 classroom: Implications and challenges on ethics and equity in higher education: A 21st century Pandora’s box. Computers and Education: Artificial Intelligence, 5, 100179. https://doi.org/10.1016/j.caeai.2023.100179

Davis, F. (1989). Technology Acceptance Model: Origins. Working Papers on Information Systems, 35-59. https://doi.org/10.4018/978-1-4666-8156-9.ch013

Derakhshan, A., Eslami, Z. R., Curle, S., & Zhaleh, K. (2022). Exploring the validity of immediacy and burnout scales in an EFL context: The predictive role of teacher-student interpersonal variables in university students’ experience of academic burnout. Studies in Second Language Learning and Teaching, 12(1), 87–115. https://doi.org/10.14746/ssllt.2022.12.1.5

Ding, L. (2024). Exploring the causes, consequences, and solutions of Chinese EFL teachers' psychological ill‐being: A qualitative investigation. European Journal of Education59(4), e12739. https://doi.org/10.1111/ejed.12739.

Droogenbroeck, F., Spruyt, B., & Vanroelen, C. (2014). Burnout among senior teachers: Investigating the role of workload and interpersonal relationships at work. Teaching and Teacher Education, 43, 99-109. https://doi.org/10.1016/J.TATE.2014.07.005.

Ferris, D. R. (2022). Feedback on L2 student writing: Current trends and future directions. In Handbook of practical second language teaching and learning (pp. 344-356). Routledge. https://doi.org/10.4324/9781003106609-28

Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs—principles and practices. Health Services Research, 48(6pt2), 2134–2156. https://doi.org/10.1111/1475-6773.12117

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.

Flom, P. L., & Cassell, D. L. (2007). Stopping stepwise: Why stepwise and similar selection methods are harmful, and what you should use. In NorthEast SAS Users Group Inc 20th Annual Conference: 11-14th November 2007 (pp. 1-6).

Guo, K., & Wang, D. (2024). To resist it or to embrace it? Examining ChatGPT’s potential to support teacher feedback in EFL writing. Education and Information Technologies29(7), 8435-8463. https://doi.org/10.1007/s10639-023-12146-0

Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.

Hobfoll S. E., Halbesleben J., Neveu J.-P., Westman M. (2018). Conservation of resources in the organizational context: The reality of resources and their consequences. Annual Review of Organizational Psychology and Organizational Behavior, 5, 103–128. https://doi.org/10.1146/annurev-orgpsych-032117-104640

Hollander, M., Wolfe, D. A., & Chicken, E. (2014). Nonparametric statistical methods (3rd ed.). John Wiley & Sons.

Hyland, K., & Hyland, F. (Eds.). (2019). Feedback in second language writing: Contexts and issues Cambridge, UK: Cambridge University Press. http://dx.doi.org/10.1017/CBO9781139524742.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (2nd ed.). Spinger.

Jamieson, S. (2004). Likert scales: How to (ab)use them. Medical Education, 38(12), 1217-1218. https://doi.org/10.1111/j.1365-2929.2004.02012.x 

Jonäll, K. (2024). Artificial intelligence in academic grading: A mixed-methods study [Master's Dissertation, University of Gothenburg]. Gothenburg University Publications Electronic Archive. Available at: https://gupea.ub.gu.se/bitstream/handle/2077/83561/Jonäll_HPA202_VT24.pdf

Kenny, J., & Fluck, A. E. (2022). Emerging principles for the allocation of academic work in universities. Higher Education83(6), 1371–1388. https://doi.org/10.1007/s10734-021-00747-y

Ko, C. J. (2022). Online individualized corrective feedback on EFL learners’ grammatical error correction. Computer Assisted Language Learning, 1–29. https://doi.org/10.1080/09588221.2022.2118783

Kovalkova, T., & Malkova, T. (2021). Burnout Syndrome: A Study among Lecturers. Proceedings of the International Conference on Economics, Law and Education Research (ELER 2021), Vol 170, 69-74. https://doi.org/10.2991/aebmr.k.210320.013

Lee, Y. (2017). Emotional labor, teacher burnout, and turnover intention in high-school physical education teaching. European Physical Education Review, 25, 236 - 253. https://doi.org/10.1177/1356336X17719559.

Li, S., & Vuono, A. (2019). Twenty-five years of research on oral and written corrective feedback in System. System, 84, 93–109. https://doi.org/10.1016/j.system.2019.05.006

Ma, Y., & Liu, Z. (2024). Emotion regulation and well-being as factors contributing to lessening burnout among Chinese EFL teachers. Acta Psychologica245, 104219. https://doi.org/10.1016/j.actpsy.2024.104219.

Malik, A., Khan, M. L., Hussain, K., Qadir, J., & Tarhini, A. (2025). AI in higher education: unveiling academicians’ perspectives on teaching, research, and ethics in the age of ChatGPT. Interactive Learning Environments33(3), 2390-2406. https://doi.org/10.1080/10494820.2024.2409407

Manuel, J., Carter, D., & Dutton, J. (2018). 'As much as I love being in the classroom': Understanding secondary English teachers' workload. English in Australia53(3), 5 https://search.informit.org/doi/10.3316/ielapa.232694249888988

Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205.

Naidoo, S. (2022). Adapting the technology acceptance model to investigate student responses to e-learning. South African Journal of African Languages42(1), 1-8. https://doi.org/10.1080/02572117.2021.2015111 

Nayernia, A., & Babayan, Z. (2019). EFL teacher burnout and self-assessed language proficiency: exploring possible relationships. Language Testing in Asia, 9(1), 3. https://doi.org/10.1186/s40468-019-0079-6.

Pennington, M. C., & Richards, J. C. (2016). Teacher Identity in Language Teaching: Integrating Personal, Contextual, and Professional Factors. RELC Journal, 47(1), 5-23. https://doi.org/10.1177/0033688216631219

Persico, D., Manca, S., & Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior30, 614-622. https://doi.org/10.1016/j.chb.2013.07.045

Pike, M., Towey, D., & Walker, J. (2019). Identifying and Alleviating Assessment Stress in Higher Education. In K. C. Li, & E. Tsang (Eds.), 2019 International Conference on Open and Innovative Education (ICOIE 2019) (pp. 589-595). The Open University of Hong Kong. https://research.nottingham.edu.cn/files/449287278/ICOIE.2019.StressRelief.Preprint.pdf

Pishghadam, R., Ebrahimi, S., Golzar, J., & Miri, M. A. (2023). Introducing emo-educational divorce and examining its relationship with teaching burnout, teaching motivation, and teacher success. Current Psychology42(33), 29198-29214. https://doi.org/10.1007/s12144-022-04000-2

Praphan, P. W., & Praphan, K. (2023). AI technologies in the ESL/EFL writing classroom: The villain or the champion?. Journal of Second Language Writing62, 101072. https://doi.org/10.1016/j.jslw.2023.101072.

Pressley, T. (2021a). Factors Contributing to Teacher Burnout During COVID-19. Educational Researcher, 50(5), 325-327. https://doi.org/10.3102/0013189X211004138

Pressley, T. (2021b). Returning to teaching during COVID‐19: An empirical study on elementary teachers' self‐efficacy. Psychology in the Schools58(8), 1611-1623. https://doi.org/10.1002/pits.22528

Pressley, T., & Ha, C. (2022). Teacher exhaustion during COVID-19: Exploring the role of administrators, self-efficacy, and anxiety. The Teacher Educator Journal, 57(1), 61-78. https://doi.org/10.1080/08878730.2021.1995094.

R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Rajab, H., Khan, K., & Elyas, T. (2016). A case study of EFL teachers’ perceptions and practices in written corrective feedback. International Journal of Applied Linguistics and English Literature, 5(1), 119-131. https://doi.org/10.7575/aiac.ijalel.v.5n.1p.119

Ryan, R.M., Deci, E.L. (2023). Self-Determination Theory. In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-031-17299-1_2630

Sadoughi, M., Hejazi, S., & Khajavy, G. (2024). Protecting language teachers from burnout: The roles of teaching mindset, teaching grit, and emotion regulation. Language Teaching Research. https://doi.org/10.1177/13621688241238350.

Sato, M., Castillo, F. F., & Oyanedel, J. C. (2022). Teacher motivation and burnout of English-as-a-foreign-language teachers: Do demotivators really demotivate them? Frontiers in Psychology, 13, 1–12. https://doi.org/10.3389/fpsyg.2022.891452

Schaufeli W. B., Hakanen J. J., Shimazu A. (2023). Burning questions in burnout research. In De Cuyper N., Selenko E., Euwema M., Schaufeli W. (Eds.), Job insecurity, precarious employment and burnout (pp. 127–148). Edward Elgar Publishing eBooks. https://doi.org/10.4337/9781035315888.00017

Seo, Y. (2023). Enthusiasm, obsession, or delusion? language ideologies and negotiating identities of one non-native English teacher in an EFL context. International Journal of Multilingualism21(3), 1621–1634. https://doi.org/10.1080/14790718.2023.2200253

Shadiev, R., & Feng, Y. (2023). Using automated corrective feedback tools in language learning: A review study. Interactive Learning Environments, 1–29. https://doi.org/10.1080/10494820.2022.2153145

Shi H, Aryadoust V. A systematic review of AI-based automated written feedback research. ReCALL. 2024;36(2):187-209. https://doi.org/10.1017/S0958344023000265

Siyum, B. A. (2023). University instructors' burnout: antecedents and consequences. Journal of Applied Research in Higher Education15(4), 1056-1068. https://doi.org/10.1108/jarhe-04-2022-0131

Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J., Moon, Y., Tseng, W., Warschauer, M., & Olson, C. (2024). Comparing the quality of human and ChatGPT feedback of students’ writing. Learning and Instruction91, 101894. https://doi.org/10.1016/j.learninstruc.2024.101894

Sun, X., & Zhang, W. (2023). Teacher emotions and professional development: A case study of three first-year EFL teachers. Issues in Educational Research, 33(3), 1190–1209. https://search.informit.org/doi/10.3316/informit.T2024050800009590936811819

Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.

Talbot, K., & Mercer, S. (2018). Exploring University ESL/EFL Teachers’ Emotional Well-Being and Emotional Regulation in the United States, Japan and Austria. Chinese Journal of Applied Linguistics, 41, 410 - 432. https://doi.org/10.1515/CJAL-2018-0031.

Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage handbook of mixed methods in social & behavioral research (2nd ed.). Sage Publications.

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x

Walker, M. E., Mehmood, R., Koshinsky, J., Hedlin, P., Chakravarti, A., Goncin, U., ... & O’Brien, J. M. (2024). Professional fulfillment, burnout, and wellness: a repeated cross-sectional survey in the COVID-19 pandemic era. Canadian Journal of Anesthesia/Journal canadien d'anesthésie71(12), 1775-1777. https://doi.org/10.1007/s12630-024-02807-0

Wang, Z. (2022). The effect of EFL teacher apprehension and teacher burnout on learners’ academic achievement. Frontiers in Psychology, 12, 839452. https://doi.org/10.3389/fpsyg.2021.839452.

Willis, G. B. (2005). Cognitive interviewing: A tool for improving questionnaire design. Sage Publications. https://doi.org/10.4135/9781412983655

Yin, H., Huang, S., & Chen, G. (2019). The relationships between teachers’ emotional labor and their burnout and satisfaction: A meta-analytic review. Educational Research Review. https://doi.org/10.1016/J.EDUREV.2019.100283.

Yu, S., Zheng, Y., Jiang, L., Liu, C., & Xu, Y. (2021). “I even feel annoyed and angry”: Teacher emotional experiences in giving feedback on student writing. Assessing Writing, 48, 100528. https://doi.org/10.1016/j.asw.2021.100528

Zarrinabadi, N., Jamalvandi, B., & Rezazadeh, M. (2023). Investigating fixed and growth teaching mindsets and self-efficacy as predictors of language teachers’ burnout and professional identity. Language Teaching Research, 0(0). https://doi.org/10.1177/13621688231151787

Zhang, Z. V., & Hyland, K. (2018). Student engagement with teacher and automated feedback on L2 writing. Assessing Writing, 36, 90–102. https://doi.org/10.1016/j.asw.2018.02.004

Zhang, Z. V., & Hyland, K. (2022). Fostering student engagement with feedback: An integrated approach. Assessing Writing, 51, 100586. https://doi.org/10.1016/j.asw.2021.100586.

Zhou, C., & Hou, F. (2025). How Do EFL Teachers Utilize AI Tools in Their Language Teaching?. Theory & Practice in Language Studies (TPLS), 15(2). https://doi.org/10.17507/tpls.1502.10.

Zhou, J., Ke, P., Qiu, X., Huang, M., & Zhang, J. (2023). ChatGPT: Potential, prospects, and limitations. Frontiers of Information Technology & Electronic Engineering25(1), 6–11. https://doi.org/10.1631/FITEE.2300089

Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to explore students’ intention to use an online education platform at a University in China. Sage Open12(1), 21582440221085259. https://doi.org/10.1177/21582440221085259