Evaluation of Few-Shot AI-Generated Feedback on Case Reports in Physical Therapy Education: Mixed Methods Study.

ABSTRACT:

Background

While artificial intelligence (AI)-generated feedback offers significant potential to overcome constraints on faculty time and resources associated with providing personalized feedback, its perceived usefulness can be undermined by algorithm aversion. In-context learning, particularly the few-shot approach, has emerged as a promising paradigm for enhancing AI performance. However, there is limited research investigating its usefulness, especially in health profession education.

Objective

This study aimed to compare the quality of AI-generated formative feedback from 2 settings, feedback generated in a zero-shot setting (hereafter, "zero-shot feedback") and feedback generated in a few-shot setting (hereafter, "few-shot feedback"), using a mixed methods approach in

SUBMITTER: Sudo H 

PROVIDER: S-EPMC12811036 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature