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

Summary/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.

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