Twelve tips: Using generative AI to create and optimize content for virtual patient simulations
Bibliographic record
Abstract
Generative Artificial Intelligence (GenAI) is increasingly being used in medical education, including the creation of content for clinical virtual patient simulation (VPS). We share practical insights drawn from our experience as medical educators, designed to help other educators integrate GenAI tools such as ChatGPT, Gemini, or Claude. We provide 12 practical tips that illustrate how GenAI can be used for the creation of content for VPS to streamline case development and increase realism for our learners. GenAI saves resources of time and expense by streamlining case creation, including creating patient images, clinical reports, and assessment questions. Clinical content experts can now concentrate on defining the case focus and on final-stage review, improving efficiency. It simplifies the development of effective scoring rubrics and meaningful formative feedback, enhancing learner engagement and clinical reasoning. By reducing the time needed to create VPS content, GenAI can allow educators to focus more on educational objectives and feedback to programs and learners. While GenAI is new and has limitations, these are likely to be addressed in the near future. As GenAI evolves, it will continue to transform VPS through improved realism, streamlined case creation, and more informative feedback, further supporting its role in medical education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".