Chatgpt vs traditional pedagogy: a comparative study in urological learning
By
Alessio Digiacomo
Angelo Orsini
Rossella Cicchetti
Ludovica Spadano
Sara De Santis
Laura Di Sessa
Miriana Vitale
Marta Di Nicola
Flavia Tamborino
Martina Basconi
Riccardo De Archangelis
Gaetano Salzano
Guglielmo Dello Stritto
Peppino Lannutti
Luigi Schips
Michele Marchioni
May 8, 2025
Clinical Scorecard: Comparative Analysis of ChatGPT and Conventional Teaching Approaches in Urological Education
At a Glance
Category Detail
Condition Urological education focusing on adrenal gland topics Key Mechanisms Comparison of learning outcomes using ChatGPT, traditional lectures, and combined methods Target Population 3rd and 4th year medical students without prior formal urology curriculum Care Setting University medical education setting
Key Highlights
Study conducted as a prospective, randomized, triple-blind trial with 121 medical students. Three groups compared: ChatGPT self-directed learning, traditional lecture, and combined ChatGPT plus lecture. Evaluation based on a 30-question test covering anatomy, physiology, pathologies, surgery, and clinical cases of adrenal glands. Guideline-Based Recommendations
Diagnosis
Use validated question banks from standard urology textbooks for assessment. Management
Incorporate AI-based tools like ChatGPT as a complementary self-directed learning resource. Combine AI tools with traditional lectures to potentially enhance learning outcomes. Monitoring & Follow-up
Assess student knowledge acquisition through standardized testing post-intervention. Monitor student engagement and prior experience with AI tools to tailor educational approaches. Risks
Ensure content accuracy when using AI-generated materials by expert review. Avoid overreliance on AI tools without integration of expert-led teaching. Patient & Prescribing Data
Not applicable (medical student education study).
Not applicable.
Clinical Best Practices
Randomize and stratify student groups to minimize selection bias in educational research. Use triple-blind study design to reduce bias in educational outcome assessment. Allow students autonomy in AI tool use to simulate real-life self-directed learning. Employ multimedia and AI-generated illustrations to support traditional teaching materials. References
