A Comparative Analysis of Human and AI-Augmented Feedback in Anesthesiology Education

Background: High-quality feedback is essential for resident development, yet barriers to its provision persist. Artificial intelligence (AI) offers a promising tool to augment feedback, potentially addressing these challenges. This study explored whether exposing attending anesthesiologists to AI-generated feedback samples before crafting their own feedback improved its quality as rated by residents. Methods: Thirty attending anesthesiologists provided feedback on 2 vignettes addressing issues of preparedness and professionalism. Before delivering feedback on the second vignette, attendings reviewed an AI-generated feedback sample. Utilizing a validated rubric, 6 blinded residents randomly evaluated the quality of feedback across 2 conditions: attendings alone (human-only) and after attendings reviewed the AI sample (AI-augmented). Feedback ratings and vignette types were compared. Residents were assessed if they could correctly identify the origin of feedback. A qualitative analysis explored attendings' perceptions of using AI for feedback. Results: = .63). Attendings reported that AI provided helpful structure and phrasing, particularly for professionalism issues, and would use it if readily available. Conclusions: Although AI augmentation did not significantly improve feedback quality, it showed promise as a tool for supporting feedback provision, particularly with crafting feedback on nontechnical, more subjective issues. Additional studies are needed to better understand AI as a tool for feedback enhancement.