No Single Code is Required: How Vibe Coding Can Change Medical Education
What if you could build a fully functional clinical learning web app—without writing a single line of code?
Dear Medical Educator,
For decades, clinical education has wrestled with the challenge of helping learners bridge the gap between knowing and doing. We’ve seen the rise of simulation labs, OSCEs, case-based learning, and now AI-enhanced tools. But even as artificial intelligence promises transformative potential, a critical barrier remains:
Most clinicians don’t code.
So what happens when we remove that barrier?
What happens when a tool doesn’t require programming knowledge or tech teams—and suddenly, meaningful innovation is within reach for every educator?
A study published in Medical Teacher last week provides a compelling example. I'll walk you through their work—and share a demo of the app I created, too.
Enter: Vibe Coding
Vibe coding is a no-code, AI-assisted approach that lets educators build their own apps, e.g. interactive clinical simulations, by simply describing what they want in plain English. Using tools powered by models, educators can prototype and deploy apps by iterating on prompts.
The goal? Shift the focus from how to code to what the learning experience should feel like.
It’s about design thinking + pedagogy + AI—without the syntax headaches.
Two Examples That Made Us Look Twice
The Differential Diagnosis Trainer (DDT):
https://ddxchallenge.aine.education
Learners are thrown into randomized clinical scenarios under timed pressure and asked to generate differential diagnoses. The tool then uses AI to provide ranked feedback, explanations, and learning tips. Think: less rote memorization, more metacognitive muscle.The Insulin and Blood Sugar Simulation (IBSS)
https://sugar.aine.education
A dynamic playground for exploring glycemic control. Learners tweak insulin doses, meal timing, and sensitivity settings, then watch glucose trends evolve in real time. It's like watching pathophysiology unfold before your eyes.
Both tools were built on Replit using AI-generated code. Total build time? Under two hours. No programming background needed.
In addition, I want to show you what I built using Replit, without writing a single code. Take a look at this video. It is a personalized feedback system for medical students. No human involvement. It is just AI. We conducted an experiment with this and showed that there is no hallucination and it improves diagnostic reasoning of medical students. The paper is in review.
So... How?
The vibe coding process looks something like this:
Define your vision with a structured prompt.
AI generates a code plan and builds a working prototype.
Iterate and refine through conversational tweaks.
Deploy and test—all without leaving your browser.
Educators stay in control, focusing on content, context, and learning outcomes—not tech specs.
What This Means (Globally, Practically, and Educationally)
Accessibility: No-code tools lower the barrier to entry—no CS degree required.
Affordability: Say goodbye to hiring a developer just to build your teaching tool.
Educational Fit: Educators can now design tools that directly align with how they teach and what their learners need.
Instead of using someone else’s simulation, you can now build one that reflects your own clinical stories, frameworks, and priorities.
This is promising, but a word of caution. Be cautious: Security and privacy concerns should be addressed by qualified experts. AI-generated tools may still contain vulnerabilities that need careful review before deployment.
What’s Next?
This isn’t just about making tools. It’s about a mindset shift—from adopters to creators. Vibe coding invites educators to tinker, experiment, and build for their own classrooms.
You don’t need to start big. Start with a single teaching problem you’ve always wanted to solve, and let AI help you prototype a solution.
Because the future of clinical education might not be built for you—it might be built by you.
Yavuz Selim Kıyak, MD, PhD (aka MedEdFlamingo)
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Related #MedEd reading:
Kıyak, Y. S. (2024). Beginner-Level Tips for Medical Educators: Guidance on Selection, Prompt Engineering, and the Use of Artificial Intelligence Chatbots. Medical Science Educator, 34, 1571–1576. https://link.springer.com/article/10.1007/s40670-024-02146-1
Kıyak, Y. S., & Kononowicz, A. A. (2024). Case-based MCQ generator: a custom ChatGPT based on published prompts in the literature for automatic item generation. Medical Teacher, 46(8), 1018-1020.. https://www.tandfonline.com/doi/full/10.1080/0142159X.2024.2314723
Kıyak, Y. S., & Emekli, E. (2024). ChatGPT prompts for generating multiple-choice questions in medical education and evidence on their validity: a literature review. Postgraduate Medical Journal, 100(1189), 858-865. https://academic.oup.com/pmj/advance-article/doi/10.1093/postmj/qgae065/7688383
Kıyak, Y. S., & Emekli, E. (2024). A Prompt for Generating Script Concordance Test Using ChatGPT, Claude, and Llama Large Language Model Chatbots. Revista Española de Educación Médica, 5(3). https://revistas.um.es/edumed/article/view/612381