Artificial intelligence is quickly becoming part of healthcare education. In simulation programs, AI tools can help educators develop scenarios, support learner assessment, identify performance trends, and reduce routine administrative work. These capabilities offer real value, particularly for programs managing limited faculty time and growing demands.
They also introduce important questions. How was an AI-generated recommendation produced? Is learner data protected? Could the system perform differently across learner groups? When should an educator override the output?
Responsible adoption does not require programs to avoid AI. It requires a clear process for deciding where AI belongs, how it will be evaluated, and who remains accountable. The following framework can help simulation leaders move from experimentation to thoughtful, educator-led implementation.
1. Start With a Defined Educational Purpose
Begin with the problem, not the technology. Identify the specific need an AI tool is expected to address and the outcome that would demonstrate value.
For example, a program may want to reduce the time required to draft simulation scenarios, improve consistency across assessment rubrics, identify trends in learner performance, or automate routine scheduling and reporting tasks. Defining the intended use helps prevent a tool designed for one purpose from being applied to decisions it was never built to support.
2. Match Oversight to the Level of Risk
Not every AI use carries the same consequences. Generating ideas for a scenario is different from recommending a score that contributes to a high-stakes learner decision.
Programs can group uses by risk. Low-risk applications might include brainstorming, formatting content, or summarizing non-sensitive program data. Higher-risk uses include evaluating communication, clinical reasoning, professionalism, or other competencies. As the potential effect on a learner increases, so should the requirements for faculty review, documentation, testing, and appeal.
3. Protect Privacy and Use Data Deliberately
Simulation recordings, transcripts, learner documentation, evaluator comments, and performance records may contain sensitive information. Before using an AI tool, programs should understand what data it accesses, where that data is stored, how long it is retained, and whether it may be used to train other models.
Only approved tools should receive institutional or learner data. Programs should minimize the information collected, remove identifying details when possible, limit access by role, and involve privacy, security, legal, and IT teams as appropriate.
4. Keep Educators Accountable for Decisions
AI should support professional judgment, not replace it. An AI-generated scenario still requires review for clinical accuracy, educational alignment, realism, and potential bias. A recommended learner score requires faculty interpretation within the context of the scenario, rubric, and observed performance. An analytics tool may reveal a pattern, but program leaders must determine what that pattern means and whether action is warranted.
Every AI-supported workflow should identify a human decision-maker with the authority to question, revise, or reject the output. For higher-stakes uses, programs should also provide a clear way for learners to request review.
5. Document, Monitor, and Improve
Responsible AI use is an ongoing practice, not a one-time approval. Programs should document the tool, intended use, data sources, review process, known limitations, responsible owners, and criteria for success.
After implementation, monitor accuracy, time savings, faculty experience, learner feedback, and any unexpected effects. Regular review helps ensure the tool continues to serve its original purpose and provides an opportunity to pause or revise its use when needed.
Move Forward With Educator-Led AI
The responsible use of AI in healthcare simulation requires purposeful use, proportionate oversight, strong data practices, meaningful validation, transparency, and continuous review. With those safeguards in place, AI can help educators work more efficiently while preserving the judgment, context, and accountability that effective simulation education demands.
EMS developed AI Insights to support an educator-led approach to AI-enhanced assessment. Faculty remain in control of reviewing and approving results while AI helps reduce repetitive work and strengthen scoring consistency. Contact an EMS solutions expert to explore how responsible AI tools can support your simulation program.