Vibe Coding in nephrology education: clinician-led, AI-assisted development of open-source interactive learning tools

Abstract

Medical education increasingly incorporates digital technologies; however, many tools remain passive and text-based. <i>Vibe Coding</i> is a clinician-led design framework that embeds expert reasoning and the cognitive 'feel' of clinical decision-making into interactive educational tools. This study demonstrates its application in nephrology training through the rapid development of open-source, AI-assisted, web-based applications. We conducted a proof-of-concept development study using a structured, physician-led, AI-assisted process combining (1) deconstruction of clinical algorithms, (2) natural-language-to-code generation with modern large language models, and (3) iterative refinement of user interfaces. The target audience included nephrology trainees and educators, with source content derived from peer-reviewed educational literature. Four open-source, web-based applications were developed: (1) <i>Kidney Stone Navigator</i> for 24-hour urine analysis interpretation, (2) <i>NephroFlow CKRT Clinical Copilot</i> for dose and anticoagulation management, (3) <i>Renal Tubular Acidosis Diagnostic Assistant</i> for algorithmic diagnosis, and (4) <i>Interactive Guide to Disorders of Volume</i> for dynamic visualization of pathophysiology. Each tool mirrored expert reasoning, integrated automated calculations, and was publicly released on GitHub with live deployment for global educational use. Clinician-led, AI-assisted development enables the translation of static educational materials into interactive, open-access tools. The <i>Vibe Coding</i> framework demonstrates a scalable, reproducible model for innovation in medical education and supports transparent digital scholarship in nephrology.

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