AI MedEd ObservatoryWhat Can AI Do in Medical Education? An observatory

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What can AI do for generating PBL scenarios?

AI can draft progressively disclosed PBL scenarios from specified learning objectives when guided by a well-structured prompt. In one matched expert evaluation, AI-generated scenarios received 90.7% positive checklist ratings versus 80.3% for existing faculty scenarios.

Evidence summary

A study compared 10 matched pairs of existing faculty-authored and ChatGPT-generated PBL scenarios addressing identical learning objectives. Twenty-two experienced PBL educators, blinded to source, completed 48 checklist evaluations using a 24-item author-developed instrument. Across valid responses, AI-generated scenarios received 519/572 positive ratings (90.7%) and human-written scenarios received 460/573 (80.3%). The largest descriptive differences favored AI for cognitive load balance, consistency, scientific or clinical accuracy, clarity, and differential diagnosis support. Human scenarios were rated slightly higher for psychosocial context and cultural sensitivity. Learners did not use the scenarios in the study.

Practical starting points

  • Provide explicit learning objectives and the target learner group before generation
  • Use the generated scenario as a first draft and review it against clinical and educational criteria
  • Adapt psychosocial, cultural, and health-system details to the local setting
  • Pilot with learners before routine implementation

Last updated: September 20, 2026

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Related articles

Selected studies

2026Non-randomized experiment

Expert evaluation of AI-assisted scenario design for interactive problem based learning environments in medical education

Burcu Kucuk Bicer, Yavuz Selim Kıyak, Özlem Coşkun · Interactive Learning Environments

This matched, source-blinded expert evaluation found that PBL scenarios generated from the same learning objectives with a structured one-shot prompt received a higher overall proportion of positive checklist ratings than existing faculty-authored scenarios: 90.7% versus 80.3%. The strongest descriptive differences favored the AI scenarios for cognitive load balance, consistency, scientific or clinical accuracy, clarity, and support for differential diagnosis.

Related subquestions: What can AI do for generating PBL scenarios?

Published prompts

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Problem-based learning scenario generation prompt

Prompt

Notes: The study used ChatGPT-5.2 Thinking in January 2026, generated each scenario in a separate conversation, and evaluated 10 AI scenarios matched to existing faculty scenarios.

Related subquestions: What can AI do for generating PBL scenarios?

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Practical guides

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