structural model of imposter syndrome and techno-stress on faculty job burnout: mediating role of digital work addiction and the moderating role of ai literacy

Objective: higher education in the ai era faces a paradigmatic rupture, exposing faculty to novel psychological challenges. this study aimed to develop and test a structural model examining how imposter syndrome and techno-stress affect faculty job burnout, with digital work addiction as a mediator and ai literacy as a moderator.methods: this applied, correlational study employed structural equation modeling (sem). the statistical population comprised all full-time faculty at tehran's public universities (~7,500 individuals) in the 2024–2025 academic year; 400 were selected via stratified random sampling. instruments included: maslach's burnout inventory (1996), clance's imposter phenomenon scale (1985), tarafdar's techno-stress inventory (2007), digital work addiction scale (2017), and ai literacy scale (2024). data analysis was conducted using spss-27 and amos-26.results: results indicated significant direct effects of techno-stress and imposter syndrome on job burnout. digital work addiction fully mediated the pathways from technological stress and internal doubts to burnout. multi-group analysis in amos confirmed ai literacy's significant moderating role: among faculty with high ai literacy, the harmful impacts of techno-stress and imposter syndrome on digital work addiction and subsequent burnout were significantly reduced.conclusions: ai literacy transcends technical proficiency and functions as a psychological protective buffer. universities are advised to prioritize capacity-building strategies grounded in critical ai literacy and sustainable digital well-being protocols to prevent burnout and safeguard faculty mental health.