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Artificial intelligence-driven work systems and employee burnout: a systematic review of mechanisms, moderators, and
Zicheng Fang1, Liangliang Han1, Te Ma2
1Business School, Liaoning University, Shenyang, Liaoning, China.
Background:
The rapid integration of artificial intelligence (AI) into workplace systems is transforming job design and employee experiences. While AI promises efficiency gains, it also creates a paradox by simultaneously reducing workload and increasing psychological strain, leading to divergent effects on burnout.
Objective:
This study systematically reviews evidence on the relationship between AI exposure and employee burnout and develops the AI as Meta-System (AIMS) framework.
Methods:
Following PRISMA 2020, four bibliographic databases and Google Scholar were searched from inception to 31 March 2026. Forty-three peer-reviewed primary empirical studies were included: 20 measured burnout or an established burnout dimension, and 23 examined mechanisms, moderators, proxy outcomes, measurement, or implementation conditions. Methodological quality was appraised using the Mixed Methods Appraisal Tool at the criterion level.
Results:
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.
Conclusion:
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.