Table 1_Artificial intelligence-driven work systems and employee burnout: a systematic review of mechanisms, moderators, and a dynamic meta-system framework.docx

Methods

Appraisal Tool at the criterion level.</p>Results<p>Findings varied according to the functional role of AI and the study design. Assistive AI was generally associated with lower burnout or exhaustion in randomized and pre–post healthcare studies, whereas monitoring and algorithmic-control exposures were associated with greater burnout or related psychosocial strain in observational studies. Perceptual AI exposure produced direct, indirect, and null associations through pathways involving job stress, job insecurity, work–family interference, perceived organizational support, and organizational commitment.

Three randomized studies provided the strongest evidence for assistive interventions. Evidence for nonlinear effects was limited to supporting proxy outcomes and did not directly establish an inverted U-shaped AI–burnout relationship.</p>Conclusion<p>Artificial intelligence is not inherently harmful or beneficial; its associations with burnout depend on its functional role, implementation, and employee appraisal. The AIMS framework integrates these pathways and identifies propositions requiring longitudinal and experimental testing.</p>