Teacher-Facing Generative AI and Workplace Mental Health in Higher Education: Perceived Demands and Resources
Abstract
Introduction
Workplace mental health promotion in higher education requires attention to how digital technologies reshape teaching demands and resources.
Purpose
To identify perceived demands and resources surrounding teacher-facing generative artificial intelligence (AI) and interpret their implications for teachers’ mental health promotion.
Methodology
An exploratory mixed-methods study combined a survey of 150 participants (56 English as a foreign language (EFL) teachers and 94 students/student-tutors) with 12 teacher interviews at two Ukrainian universities in January-February 2026. Descriptive statistics and four exploratory Fisher exact tests with Holm adjustment were integrated with thematic analysis of 30 selected translated interview extracts. Job Demands-Resources and self-determination theories informed a post-hoc interpretation.
Results
Among teachers, 53.6% rated an AI platform for EFL teachers (Twee) very useful for preparation-time saving and 57.1% for task variety. Across the mixed sample, access (27.3%), quality assurance (22.0%), curriculum alignment (20.0%), and training/time (20.0%) were the leading constraints. Interview extracts linked anticipated workload relief to individual student support, while highlighting learning demands and the importance of professional judgment, empathy, and mentoring.
Conclusions
These perceptions identify work-design issues relevant to mental health promotion during AI implementation. Participatory decisions, protected learning and recovery time, and accessible psychosocial support are priorities for institutional consideration.
© 2026 Liliya Batyuk, Volodymyr Borysov, Iryna Hlazkova, Yana Romantsova, Olena Karasova, Oksana Dubrova, Olena Ronzhes, published by International Platform on Mental Health
This work is licensed under the Creative Commons Attribution 4.0 License.