arXiv:2605.25440v1 Announce Type: new Abstract: Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident trainee skill acquisition. Yet, assessing the quality of trainer feedback and its effectiveness in influencing trainee behavior during live surgery remains a challenge. Prior studies assessed feedback content relying on extensive manual annotation by expert human raters and focused on developing broad taxonomies that overlook the qualitative aspects of feedback delivery such as clarity or urgency. Limited existing automated methods, i
The increasing sophistication of large language models makes them applicable to complex, nuanced tasks like evaluating human performance feedback, pushing the boundaries of AI utility in professional training.
This development suggests a scalable, automated approach to quality assessment in skilled professions, potentially standardizing and improving feedback mechanisms where human expert time is limited.
Traditional manual, expert-driven assessment of qualitative feedback can now be augmented or potentially replaced by AI-driven frameworks, offering more consistent and timely evaluations.
- · Medical training institutions
- · AI/LLM developers
- · Surgical residents
- · Healthcare technology providers
Automated systems begin to assist in qualitative assessment of professional training feedback across various complex domains beyond surgery.
Standardized, AI-driven feedback leads to more measurable and accelerated skill acquisition rates in high-stakes professions.
The role of human instructors shifts from primary evaluators to curators and refiners of AI-generated insights, increasing their leverage and focus on advanced pedagogy.
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