The impact of artificial intelligence-based clinical training on burnout among practitioners treating serious mental illness.

= 0.38. There were no significant differences in burnout change between the TAU + AI and TAU conditions, suggesting brief AI-supported interventions may be insufficient to alleviate burnout. Secondary analyses revealed no association between frequency of practice and burnout change. However, there was a significant negative relationship between posttraining burnout and the proportion of practices that met fidelity benchmarks. A significant interaction effect also indicated that meeting fidelity benchmarks predicted lower burnout only when those exercises were completed at a high level of proficiency. Together, these findings suggest that training may impact burnout only if learners successfully acquire new skills, potentially improving confidence and self-efficacy. While training alone may not be sufficient to reduce burnout, integrating AI-based feedback and skill assessment into training may allow systems to better verify and support provider skill acquisition and ultimately reduce burnout. (PsycInfo Database Record (c) 2026 APA, all rights reserved).