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From technology adopters to creators: Leveraging AI-assisted vibe coding to transform clinical teaching and learning
Minyang Chow1,2, Olivia Ng1
1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
Medical Teacher
|April 9, 2025
Summary
Educators can now create custom clinical learning simulations using AI-assisted, no-code tools. This approach enhances diagnostic reasoning and metabolic dynamics training, improving accessibility and scalability in medical education.
Area of Science:
- Medical Education
- Artificial Intelligence in Healthcare
- Simulation-Based Learning
Background:
- Integrating theoretical knowledge with practical clinical skills is a persistent challenge in medical education.
- Traditional teaching methods inadequately prepare clinicians for complex decision-making and heuristic development.
- There is a need for accessible tools to create dynamic clinical learning experiences.
Purpose of the Study:
- To introduce vibe coding, an AI-assisted, no-code approach for educators to develop custom clinical simulations.
- To demonstrate the creation and utility of two distinct simulation applications: Differential Diagnosis Trainer (DDT) and Insulin and Blood Sugar Simulation (IBSS).
- To highlight the benefits of AI-driven no-code platforms in medical simulation development.
Main Methods:
- Developed vibe coding, an AI-assisted, no-code platform enabling educators to create simulations via conversational prompts.
- Utilized AI-powered no-code platforms for rapid prototyping and iterative refinement of learning tools.
- Applied the approach to build the Differential Diagnosis Trainer (DDT) and Insulin and Blood Sugar Simulation (IBSS).
Main Results:
- Vibe coding allows educators to create interactive, customizable clinical simulations without programming expertise.
- The DDT and IBSS demonstrated significant improvements in accessibility, cost-effectiveness, and scalability.
- The approach shifts focus from technical limitations to pedagogical goals, fostering educator creativity.
Conclusions:
- AI-driven no-code platforms empower educators to become creators of innovative clinical simulations.
- This approach transforms medical learning experiences, enhancing diagnostic reasoning and clinical judgment.
- The development of scalable, personalized simulations ultimately improves patient care outcomes.
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