Across nursing programs, faculty are beginning to explore how AI can support teaching, streamline preparation, and enhance how students build clinical judgment. What began as experimentation just a few years ago is now moving into more structured use — particularly in areas that require significant faculty time and coordination.
Simulation is emerging as one area where faculty can begin incorporating AI tools into their work. Many computer-based simulations are beginning to integrate AI, and faculty can use AI to enhance standardized patient care, role-playing, and manikin simulations. Today, more educators are using AI tools to design the scenarios and the accompanying assessments and debriefings. Bringing in AI tools like ChatGPT, Gemini, Claude, or others can streamline the entire process, ultimately saving time and helping faculty standardize their approach throughout the department.
During a recent Wolters Kluwer webinar, Tools of the Trade: Practical strategies to leverage AI in simulation, leading educators, Dr. Kellie Bryant and Dr. Jennifer Roye found that 60% of nurse educator participants were not currently using AI tools in their simulation programs. Drs. Bryant and Roye shared several examples of how faculty are using AI tools in these ways. They stressed that doing so takes strategic, thoughtful implementation to add value without compromising educational quality. The key, they say, is to keep human judgment, empathy, and guidance at the center. AI should always complement faculty expertise rather than try to replace it.
How AI in Nursing Education can strengthen simulation-based learning
Designing simulations can take significant work, yet simulations are vital for helping students develop critical thinking skills. Many faculty are finding that, as highlighted in the Trends & Insights whitepaper, leveraging AI tools can help streamline the time-consuming, non-student-facing tasks surrounding simulations — such as designing and revising scenarios and their related materials.
During the webinar, Drs. Bryant and Roye walked through several example “prompts” that faculty can use to develop these materials. A prompt is the instructions given to an AI agent that tells the tool exactly what the user intends. Successful AI usage rests in crafting strong prompts, the more specific, the better.
For example, the speakers shared this sample prompt: “Design a 15-minute, comprehensive, manikin-based, unfolding simulation scenario for prelicensure nursing students in a hospital setting. The simulation should support the following learning objective: By the end of the simulation, the student will recognize signs of congestive heart failure exacerbation, prioritize appropriate interventions, and implement timely nursing care.”
Note that the prompt details:
- The type of simulation that the instructor wants to present
- The length
- The target audience
- The learning objectives
From there, Drs. Bryant and Roye demonstrated that while the resulting output was not perfect, they were able to use follow-up prompts to create scripts, Socratic debriefing questions, assessment rubrics, pre-simulation assignments, and other elements. Each of these outputs required the educator to revise and weave in their own judgment and experience, but it saved significant time.
Many educators find AI to be particularly helpful for assessment tools, debriefings, and feedback for both learners and facilitators. After a scenario is complete, an educator can ask AI to analyze an anonymized transcript, especially after a computer-based simulation. The resulting feedback can guide the instructor to prioritize personalized remediation for students who need it, even if they were unable to closely observe every student’s live actions.
Once refined, faculty can combine these materials into a “single source of truth” packet that includes a simulation’s objectives, scripts, debriefing, assessment, and required equipment. Such packets allow for easier scaling to larger or more frequent classes.
When used in these ways, research shows that AI in screen-based simulation improves knowledge, performance, communication, and teamwork — qualities essential for developing practice-ready nurses. In fact, one study found that nursing students experienced a 19% increase in their perceived confidence after using tools like vSim® for Nursing.
How AI can improve faculty efficiency and simulation operations
AI can help faculty when workloads are challenging. Simulation staff and other facilitators can personalize learning paths for each student’s strengths and weaknesses. In aggregate, AI can surface patterns across learners, helping faculty refine their approach.
Through predictive modeling, AI holds the potential to support simulation operations, such as resource planning and scheduling. AI can estimate how many consumables (such as syringes) will be required over a simulation — or a semester — and plan how to best utilize different facilities.
Effective AI usage requires keeping a human in the loop to validate outputs and insert essential judgment — but AI can remove much of the drudgery and repetitive work.
What does effective AI integration into simulation require?
Educators who want to effectively integrate AI into their work should apply several key principles:
- Start with the outcomes. The backward design approach used by competency-based education also works well for AI design. Clearly defining the desired outputs keeps your actions targeted.
- Continue to iterate. AI experts often say, “The better the prompt, the better the output.” That means that your first prompt should not be your final prompt. Instead, continue to refine your requests and learn from your peers. You can even instruct AI, “Help me strengthen this prompt.”
- Keep faculty in the loop for decision-making. Preserve academic integrity by ensuring faculty review all AI outputs before they go to students or anyone else. And never let AI make the final decisions, especially about grading, assessments, or competencies.
- Protect privacy. Make sure all data is anonymized or de-identified, and understand how learner data is collected, stored, and used. Involve your IT and legal departments to define protocols for data breaches or other issues.
- Build a multi-disciplinary team. Include simulation staff, faculty, IT, risk management, administrators, legal, learners, and data scientists to set policies and guardrails around how your department selects, governs, and monitors AI tools.
Why caution around AI in nursing education still matters
Even as AI usage expands, caution remains essential. AI tools are only as good as the models upon which they are built, which can introduce risks of bias and inequities. Many systems are trained on white, healthy, middle-aged, able-bodied patients that don’t reflect the broader populations students will see in clinical settings. That’s why it’s so important to recognize this risk of bias to ensure that students learn from simulations that represent what students will encounter in their clinical roles.
On the learner side, AI models may miss how neurodiverse learners process and respond, or they may issue lower scores for voices that are female or accented. Also, physical controllers may be sized for larger hands, making practice harder for some students.
Hallucinations (in which AI “makes up” information) and unreliable content remain an issue. In fact, Wolters Kluwer research points to a lack of trust in AI-generated content accuracy as a key barrier to adoption among students. Programs may be liable or accountable if students learn inaccurate skills and later apply them in clinical practice. Faculty must stay vigilant and monitor for errors and inconsistencies while keeping human judgment front and center.
Meanwhile, some students are becoming over reliant on AI, potentially threatening how they develop critical thinking and clinical judgment skills. Faculty should help students understand the stakes and risks.
While these threats are significant, educators can work closely with vendors to understand how they trained their models. Not every vendor will be ideal for the unique challenges of simulation, but some, like vrClinicals for Nursing, are designed explicitly for nursing education.
Start using AI in Nursing Education
AI can help expand capacity, support faculty, and enhance learning — but it should not replace the human element at the core of good nursing education.
Watch the on-demand webinar, Tools of the trade: Practical strategies to leverage AI in simulation, for additional tips and sample prompts — building on the approaches highlighted in the Trends & Insights whitepaper — to help bring simulation into your curriculum and classroom.