Abstract
Nephrology requires students to integrate nephron transport, hormonal regulation, medication effects, and serum and urine electrolyte patterns, which can be difficult to represent with static instructional materials. This study describes the development and early evaluation of Nephron Flow Lab, a web-based interactive nephrology learning application created through a generative artificial intelligence-assisted “vibe coding” workflow. Initial visual prototyping was performed using Gemini 3.1 Pro, with Codex using GPT 5.5 used for complex physiologic logic, animations, and implementation. Idaho WWAMI students received optional access to the application before Exam 5 and the course final examination, whereas students at other WWAMI sites served as controls. Aggregate item-level cross-sectional difference-in-differences contrasts compared focal-versus-baseline performance at the Experimental site with corresponding contrasts at individual and pooled control sites. Pooled-control estimates showed no measurable advantage for Idaho on Exam 5 (−0.88 percentage points; 95% CI, −5.96 to 4.87), the final examination (0.66 percentage points; 95% CI, −4.57 to 5.48), or both assessments combined (0.47 percentage points; 95% CI, −3.36 to 4.52). Survey respondents rated the application favorably using a modified uMARS survey, with an overall app-quality score of 4.54/5. Nephron Flow Lab demonstrates the feasibility of faculty-built AI-assisted educational software and was favorably perceived by survey respondents; however, access to the application was not associated with a measurable aggregate achievement advantage in this exploratory evaluation.