As Physician Assistant (PA) programs face increasing demands for individualized student support and limited faculty bandwidth, innovative solutions are essential. This lightning talk explores the integration of a custom GPT-powered tool through ChatGPT to remediate clinical year PA students who fail end-of-rotation exams. The process begins by analyzing each student’s missed keywords from their exam results, which are then used to generate tailored clinical cases reflecting their specific knowledge gaps. Faculty members interact with the student using these AI-generated cases in a structured format, guiding them through differential diagnoses, clinical reasoning, and management plans.

This approach enhances engagement, targets weak areas with precision, and promotes deeper learning while reducing faculty preparation time. Early implementation has resulted in clear improvements in student performance on repeat exams, with consistently positive feedback on the personalized and interactive nature of the cases. Attendees will gain insight into scalable, AI-driven remediation and practical strategies for integrating similar methods into their own programs.