Human–Machine Collaboration Depth and Teacher Burnout: A Dual-Path Moderated Mediation Model of Empowerment and Depletion

This study investigates how human–machine collaboration depth is associated with teacher burnout during educational digital transformation. Grounded in Conservation of Resources theory, cross-sectional survey data from 489 Chinese university teachers were analyzed using structural equation modeling and conditional process analysis. Results indicated that human–machine collaboration depth was negatively associated with teacher burnout, mediated in parallel by digital burden and teacher agency. Within the present sample, the indirect inverse association via teacher agency was numerically larger in magnitude than the positive association via digital burden. Furthermore, perceived algorithmic control acted as a boundary condition. High levels of perceived algorithmic control strengthened the link to digital burden and weakened the protective role of teacher agency. Although the cross-sectional design precludes definitive causal inferences, this study advances a competitive integration perspective of technology use. The findings extend algorithmic management theory to higher education, indicating that mitigating occupational strain requires governance frameworks that prioritize professional autonomy over rigid algorithmic surveillance.