7 Intelligent Design: Building Immersive Case Studies and Simulations with AI
Jennifer Roye, EdD, MSN, RN, CHSE-A, CNE
Author Bio
Jennifer Roye, EdD, MSN, RN, CHSE-A, CNE is the Assistant Dean for Simulation and Technology and a Clinical Assistant Professor at the University of Texas at Arlington College of Nursing and Health Innovation. She is lead faculty for the Fundamentals of Telehealth course and the Director of the Undergraduate Health Informatics Certificate Program. Mrs. Roye is an affiliate of the Multi Interprofessional Center of Health Informatics at UTA. She is active in the International Nursing Association for Clinical Simulation and Learning (INACSL,) serving on the Board of Directors. She received her MSN from UTA in 2003 and her EdD from The University of Alabama in 2024. She practiced as a CPNP and as an RN in the Emergency Department at Cook Children’s Medical Center in Fort Worth, Texas for 16 years. Her areas of research interest include simulation, robotics, telehealth, artificial intelligence, health informatics, rural health, interprofessional simulation education, accessibility in nursing education, and extended reality (XR) simulation.
Course Context
This activity is designed to be adaptable to a variety of contexts, but it was originally designed with the following student population in mind:
- Discipline: Healthcare professionals
- Level: Undergraduate, Graduate
- Course Name: This activity could be incorporated into any healthcare professional related instruction. It is an adaptable framework.
- Modality: Online or in person
- Context: This activity guide will provide direction on using AI to design case studies or simulations that can be used for healthcare professional education. In addition to the general guidelines, there will be an example of a created simulation activity.
- Number of Students: course enrollment may vary based on course enrollment. The framework is highly scalable.
Activity Overview
This activity will equip instructors with the necessary tools and guidance to develop case studies or simulations using generative AI, tailored to relevant course content. The session will include detailed instructions on constructing effective prompts, refining those prompts for accuracy and relevance, and implementing best practices for creating and utilizing case studies or simulations to enhance the education of healthcare professionals.
Directions
For Instructors
- Identify Selected Topics and Content
Select topics and scenarios that align with the course or clinical objectives. Ensure the topic addresses relevant medical or nursing areas (e.g., pediatrics, mental health, emergency care) and ties directly into the overall learning goals.
Example: Choose a topic like asthma management in pediatric patients for a simulation or case study.
- Identify Specific Learning Objectives
Pinpoint the specific skills or knowledge areas that the case study or simulation should cover. These objectives could include clinical skills, patient communication, or clinical decision-making.
Example: Teach learners how to assess respiratory function, communicate effectively with patients and families, and make decisions about medication administration.
- Identify Standards to Guide Delivery
Ensure the case study or simulation follows recognized guidelines or standards (e.g., INACSL Healthcare Simulation Standards of Best Practice). This step ensures consistency in delivery and quality, aligning with best practices in healthcare education.
Example: Ensure that the scenario includes a pre-brief, effective facilitation, and a structured debrief.
- Construct a Detailed Prompt
Create a prompt that includes as much specific information as possible, such as: Patient Demographics: Age, gender, medical history, setting (Hospital, clinic, or emergency room), conditions and symptoms.
Example: “Create a case study for a 7-year-old male with asthma, presenting in the ER with shortness of breath. Include patient history and focus on medication administration and patient education.”
- Define Actionable Elements
Clarify what you want learners to do within the simulation or case study. Should they assess symptoms, administer medications, communicate with patients, or make critical decisions?
Example: Students should assess respiratory status, administer bronchodilators, and provide education on proper inhaler use.
- Adjust Tone and Complexity
Tailor the language, tone, and complexity of the prompt to match the learners’ level of expertise. For example, a more basic tone and less complex scenario would suit beginners, while advanced students might need a more challenging scenario.
Example: Use clinical terminology and multi-step decision-making for advanced learners; keep scenarios simple for beginners.
- Identify Your Target Audience
Clearly define the target audience for the case study or simulation. Specify whether the learners are beginners, intermediate, or advanced so that the scenario is appropriately challenging and aligned with their skill level.
Example: “This case study is intended for intermediate nursing students who have completed foundational courses on patient assessment.”
- Request Detailed Output Using an AI Tool
Use a generative AI tool such as ChatGPT, Copilot, or Gemini to create your case study or simulation. Input the refined prompt and request the AI to generate the case study content.
Example: Use ChatGPT to create a case study, including the patient’s medical history, symptoms, and expected outcomes.
- Refine the Prompt Based on Initial Output
Review the AI-generated content and make necessary adjustments to refine the prompt. Request revisions as needed to ensure the output aligns closely with your learning objectives and standards.
Example: If the initial output lacks complexity or detail, update the prompt to include more critical decision points or add complexity to the patient’s condition.
Example input
- Initial Prompt: “Create a pediatric case study on asthma.”
- Refined Prompt: “Develop a case study for a 7-year-old male patient with asthma who presents to the emergency room with wheezing and shortness of breath. The scenario should include patient assessment, medication administration, and instructions for inhaler use. Focus on educating the patient’s parents to prevent future emergency visits. Tailor the case study for advanced nursing students.”
See Appendix A for an example of a simulation scenario created using the above steps.
For Students
- Complete simulation or case study according to instructor’s directions.
- Actively participate in debriefing or discussion led by your instructor.
- Submit any documentation required by your instructor.
Benefit to Students
Case studies are a powerful tool in healthcare education, offering students the opportunity to engage with real-world clinical scenarios in a controlled and reflective environment. By presenting learners with detailed, patient-centered problems, case studies promote critical thinking and decision-making, essential skills for healthcare professionals. They allow students to integrate theoretical knowledge with practical application, bridging the gap between classroom learning and clinical practice. This method encourages active learning and helps develop problem-solving skills by requiring students to analyze data, consider multiple perspectives, and make evidence-based decisions (Popil, 2011).
One of the most significant benefits of using case studies is the opportunity to explore complex, multifaceted patient care situations. In a structured yet flexible way, case studies expose students to the nuanced challenges of healthcare, such as dealing with ethical dilemmas, coordinating multidisciplinary care, and managing patient communication. Studies have shown that this approach not only enhances clinical reasoning but also improves students’ ability to adapt to real-life clinical environments (Bastable, 2019). Furthermore, case studies often encourage collaboration and discussion, allowing students to learn from peers and instructors, fostering a deeper understanding of the subject matter and enhancing teamwork skills crucial in healthcare settings (Forrest et al., 2020).
Simulation-based learning is a cornerstone of modern healthcare education, providing students with a safe and controlled environment to practice clinical skills, decision-making, and patient care. Unlike traditional methods, simulations offer immersive, hands-on experiences that closely mimic real-life healthcare scenarios. This method allows students to practice and refine their skills without the risk of harm to actual patients, making it particularly valuable in high-risk or complex clinical situations. Research has shown that simulations enhance learner confidence, improve clinical competence, and support better retention of knowledge compared to didactic learning alone (Cant & Cooper, 2017).
Simulations also enables educators to create realistic, patient-centered scenarios that include the dynamic and unpredictable nature of healthcare. Through high-fidelity simulations, students can experience acute care situations, practice interprofessional collaboration, and develop critical thinking skills by responding to evolving patient conditions. This experiential learning is crucial in preparing students for the demands of real-world practice. Furthermore, the ability to provide structured feedback through debriefing sessions ensures that students can reflect on their performance, identify areas for improvement, and solidify their learning (INACSL Standards Committee, 2021). By engaging in repeated practice, learners can build muscle memory for procedural tasks, enhance their problem-solving skills, and ultimately transition more seamlessly into clinical roles. This approach highlights the transformative potential of simulation in healthcare education, ensuring students are practice-ready and capable of delivering safe, effective care in complex clinical environments.
Assessment
In addition to the assignment related assessments and evaluation, you can also evaluate the effectiveness of the simulation activity with the Simulation Effectiveness Tool- Modified (SET-M).
Overview of the SET-M (Simulation Evaluation Tool-Modified)
The Simulation Evaluation Tool-Modified (SET-M) is a widely used method for evaluating learners’ experiences and outcomes in simulation-based education. Developed and validated to measure key components of simulation learning, the SET-M focuses on capturing student perceptions of the simulation environment, their learning outcomes, and the debriefing process.
The SET-M uses a three-point Likert scale to measure participants’ perceptions of various aspects of their simulation experience, including prebriefing, the scenario, and debriefing. The scale values are 1 – do not agree, 2 – somewhat agree, and 3 – strongly agree.
Key Components:
Simulation Design: The SET-M evaluates how well the simulation design supported the learning objectives. This includes the realism of the scenario (fidelity), clarity of roles, and alignment with course goals.
Facilitation and Guidance: This component assesses how the simulation facilitator guided learners through the experience, including how feedback was delivered, whether learners were appropriately challenged, and how the facilitator supported the learning process.
Debriefing: Since debriefing is a critical element of simulation, the SET-M measures how well the debriefing process helped learners reflect on their performance. It evaluates the effectiveness of debriefing in reinforcing learning, correcting misunderstandings, and encouraging critical thinking.
Learner Outcomes: The tool examines learners’ self-reported outcomes, such as improvements in clinical reasoning, confidence, and decision-making. It assesses whether the simulation experience helped learners meet specific competencies relevant to their field.
Evaluation Process:
Survey Format: The SET-M is typically administered through a Likert-scale survey, where students rate various aspects of the simulation experience. It allows for quantitative analysis of learner feedback and can be supplemented with open-ended questions for qualitative insights.
Reflection and Feedback: In addition to the quantitative ratings, learners are encouraged to reflect on their experience, providing valuable feedback that can inform future simulation design and delivery.
In addition to the Likert scale items, SET-M may include optional open-ended questions where learners can provide qualitative feedback. These responses are not scored but can provide deeper insights into specific areas for improvement.
The SET-M offers a standardized and reliable method of evaluation that is specifically tailored to simulation-based education. It provides instructors with valuable insights into how simulations contribute to learner development and highlights areas where improvements can be made. By focusing on design, facilitation, debriefing, and outcomes, the SET-M ensures that simulation experiences are both educational and impactful.
Other methods of Assessment and evaluation may include open ended question surveys, focus groups, and knowledge checks related to content.
Assessment
Assessment will be based on the students’ ability to recognize symptoms of malignant hyperthermia, apply appropriate interventions, and effectively communicate within the healthcare team. A rubric will be used to evaluate these competencies during the simulation and debrief.
Cross-Disciplinary Applications
The use of AI to create case studies or simulations can be applied across various disciplines. Here are some examples:
Medicine
Simulated Emergency Response for Critical Care Training
In medical education, the activity can be adapted to focus on emergency response scenarios such as cardiac arrest, sepsis, or trauma care. The simulation would be designed to help medical students or residents practice rapid assessment, decision-making, and multidisciplinary collaboration in high-stakes situations. The simulation could include AI-generated patient histories, real-time vital sign changes, and communication with other healthcare professionals. This adaptation would improve critical thinking and teamwork under pressure, enhancing readiness for real-world emergencies.
Pharmacy
Pharmaceutical Intervention in Patient Care Simulation
For pharmacy students, the simulation could be adapted to focus on drug management and patient counseling in a clinical setting. Students could engage in simulated interactions with patients suffering from chronic illnesses like diabetes or hypertension, where they must review medication orders, detect errors, and educate patients on medication adherence. The AI could generate different case scenarios with varying degrees of complexity, ensuring students can practice dosage calculations, drug interactions, and patient education.
Social Work
Family Support Simulation in Crisis Management
In social work education, the activity could be adapted to focus on providing emotional and logistical support to families in crisis. Simulations could involve scenarios such as child welfare assessments, mental health crises, or domestic abuse interventions. The AI-generated cases could include complex family dynamics, enabling students to practice active listening, crisis intervention techniques, and collaboration with healthcare professionals. This would help social work students develop the skills needed to navigate high stress, emotionally charged environments.
Business Administration
Crisis Management and Decision-Making Simulation
In a business education context, the activity could be adapted to simulate crisis management in corporate settings, such as responding to a public relations disaster or handling a financial downturn. The AI-generated scenarios would require students to assess the situation, make strategic decisions, and communicate with stakeholders. This adaptation would focus on leadership, decision-making under stress, and effective communication, providing practical experience in managing complex business challenges.
Education
Classroom Management and Student Behavior Simulation
In education, the simulation could be adapted to help teacher candidates practice managing classroom behaviors and making instructional decisions in real-time. AI-generated scenarios could involve various classroom challenges, such as disruptive behavior, diverse learning needs, or emergency situations (e.g., fire drills or health incidents). This activity would allow educators to test different intervention strategies and reflect on their classroom management skills in a risk-free environment.
These activities could easily be adapted to provide an interdisciplinary experience as well.
Resources
SET-M (Simulation Evaluation Tool-Modified)
The Simulation Effectiveness Tool – Modified, (SET-M) is used to assess the effectiveness of simulation experiences in healthcare simulation education. It evaluates learners’ perceptions of their skills, knowledge, and confidence gained through the simulation-based learning activities.
Leighton, K., Ravert, P., Mudra, V., & Macintosh, C. (2015). Updating the simulation effectiveness tool: Item modifications and reevaluation of Psychometric Properties. Nursing Education Perspectives, 36(5), 317–323. https://doi.org/10.5480/15-1671
Disclosures
Mavs Open Press defines work as AI-assisted when author-created content is enhanced, organized, or edited using an AI tool.
This OER contains AI-generated content. AI-generated content appears in the development of the malignant hyperthermia simulation scenario using ChatGPT.
This simulation scenario was developed using AI-based technology and adheres to the INACSL standards.
References
Cant, R. P., & Cooper, S. J. (2017). Use of simulation-based learning in undergraduate nurse education: An umbrella systematic review. Nurse Education Today, 49, 63-71.
Bastable, S. B. (2019). Nurse as educator: Principles of teaching and learning for nursing practice (5th ed.). Jones & Bartlett Learning.
Forrest, K., McKimm, J., & Edgar, S. (2020). Essential simulation in clinical education. MedEdPublish.
INACSL Standards Committee. (2021). INACSL Healthcare Simulation Standards of Best Practice: Simulation design. Clinical Simulation in Nursing, 12(S), S5-S12
Popil, I. (2011). Promotion of critical thinking by using case studies as a teaching method. Nurse Education Today, 31(2), 204-207.
Appendix A
The following example is a simulation scenario set in the operating room. We needed an OR environment to provide a simulation for a group of undergraduate nursing students participating in a perioperative elective. The learners had never been in an OR before. For the scenario, we chose a disorder known as Malignant Hyperthermia (MH)—a rare but life-threatening condition that can occur when a person is exposed to certain anesthetic drugs, often during surgery.
Increasing Capacity in Nursing Simulation Education Using AI and Immersive 3D Technology
Course Context
This activity is designed to be adaptable to a variety of contexts, but it was originally designed with the following student population in mind:
Discipline: Nursing
Level: Undergraduate
Course Name: Perioperative Nursing Elective
Modality: Face-to-face, Simulation-based
Context: This course focuses on perioperative nursing practices and includes hands-on simulation experiences to meet course objectives. The elective was developed to introduce students to high-acuity, low-occurrence events such as malignant hyperthermia.
Number of Students: 10-20
Activity Overview
In the summer of 2024, there was a need to create a perioperative elective course to expose nursing students to complex, high-acuity, low-occurrence situations like malignant hyperthermia. We utilized AI, specifically ChatGPT, to develop a malignant hyperthermia simulation scenario. The scenario was conducted in an immersive 3D space, the Igloo, which provided a realistic operating room (OR) environment using real-life images from a local OR. This simulation experience not only increased student engagement but also built confidence when they encountered a similar setting during their clinical the following day. By interacting with a life-threatening scenario, the students practiced critical decision-making in a controlled yet immersive environment, gaining skills that are difficult to replicate in clinical settings due to the rarity of such events.
Directions
For Instructors
Begin by preparing students with foundational knowledge of perioperative care and the pathophysiology of malignant hyperthermia.
Create the prompt and enter into the preferred generative AI tool. (ChatGPT was used in this case).
The prompt– Create a detailed simulation scenario for undergraduate nursing students participating in a perioperative elective course. The scenario should be set in an immersive 3D operating room (OR) environment and focus on the recognition and management of Malignant Hyperthermia (MH), a rare but life-threatening condition triggered by anesthesia. The students have never been in an OR before, and the purpose of the simulation is to expose them to high-acuity, low-occurrence events. The learning objectives should focus on recognizing MH symptoms, applying interventions (such as administering dantrolene), and effective teamwork and communication. The patient should experience progressive phases where symptoms worsen over time. Align the simulation with the Healthcare Simulation Standards of Best Practice. Student actions during the scenario should include monitoring vitals, diagnosing MH, and initiating emergency interventions. The assessment rubric should include evaluating student performance in recognizing symptoms, applying interventions, and working as a team. Additionally, include how this simulation could be adapted for other healthcare disciplines and what resources and equipment would be required to run the simulation effectively.
Output received:
Simulation Title:
Managing Malignant Hyperthermia in the Operating Room
Duration:
60-90 minutes (including pre-brief, simulation, and debrief)
Target Learners:
Discipline: Undergraduate Nursing
Level: Junior or Senior Nursing Students
Course Name: Perioperative Nursing Elective
Clinical Scenario:
This scenario focuses on the recognition and management of Malignant Hyperthermia (MH), a life-threatening condition that can occur in patients undergoing anesthesia. The goal is to simulate a high-acuity, low-frequency event in the operating room, providing students with a realistic and immersive learning experience.
Learning Objectives:
Recognize early symptoms of Malignant Hyperthermia (e.g., tachycardia, hypercapnia, muscle rigidity, and increased temperature).
Apply appropriate interventions, including administration of dantrolene and cooling measures.
Demonstrate effective teamwork and clear communication with the healthcare team in a high-pressure environment.
Utilize critical thinking skills to rapidly assess and respond to changes in the patient’s condition.
Reflect on the experience during debriefing, identifying areas for improvement and reinforcing successful strategies.
Simulation Design:
Scenario Setting: Operating Room (OR) in an immersive 3D environment (e.g., the Igloo).
Patient Demographics:
Name: Alex Johnson
Age: 25
Gender: Male
Medical History: Healthy, no known allergies, undergoing a routine laparoscopic surgery.
Initial Presentation: Stable vitals at the start of surgery, patient under anesthesia.
Pre-Briefing:
Provide students with a brief introduction to perioperative care and the basic physiology of Malignant Hyperthermia.
Review the roles of the healthcare team in the OR (e.g., circulating nurse, scrub nurse, anesthesia provider, surgeon).
Discuss the importance of clear communication and role assignments during high-stakes situations.
Scenario Progression:
Phase 1: Patient Undergoing Routine Surgery
Vital Signs: Stable (HR: 80, BP: 120/80, Temp: 36.8°C).
Instructor Cues: The patient is receiving anesthesia. Everything appears normal at first.
Phase 2: Onset of Malignant Hyperthermia Symptoms
Instructor Cues: A few minutes into the scenario, the patient’s heart rate increases to HR: 120, temperature rises to 38.5°C, and muscle rigidity is observed.
Expected Actions:
Students should assess the patient’s vitals and recognize the changes.
Begin discussing the possibility of Malignant Hyperthermia.
Phase 3: Critical Symptoms
Vital Signs: HR: 160, Temp: 40°C, CO2 levels rising on anesthesia monitor.
Instructor Cues: The patient’s condition worsens. Vital signs continue to deteriorate.
Expected Actions:
Diagnosis of Malignant Hyperthermia.
Immediate call for the MH cart and administration of dantrolene.
Begin cooling measures (e.g., ice packs, cold IV fluids).
Phase 4: Patient Stabilization
Instructor Cues: After dantrolene administration and cooling, the patient’s condition begins to stabilize.
Expected Actions:
Monitor vitals closely and continue cooling measures until vitals return to normal.
Debriefing:
Duration: 30 minutes
Key Focus Areas:
Clinical Knowledge: Did the students correctly diagnose Malignant Hyperthermia and initiate proper treatment?
Teamwork and Communication: How effectively did the team communicate and assign roles?
Critical Thinking: Did students make timely and evidence-based decisions?
Reflection: Encourage students to reflect on their individual and team performance, discussing what went well and what could be improved.
Assessment:
Tools: Rubric for assessing clinical decision-making, teamwork, and communication.
Criteria:
Recognition of MH Symptoms: Ability to identify tachycardia, hypercapnia, and muscle rigidity.
Appropriate Interventions: Administering dantrolene, initiating cooling measures.
Team Collaboration: How well the group assigned roles, communicated, and worked together.
Debrief Participation: Engaging in reflective learning and identifying areas for improvement.
Healthcare Simulation Standards of Best Practice Alignment:
Simulation Design:
Scenario adheres to INACSL standards by providing clear objectives, realistic settings, and structured progression from low to high acuity.
Facilitation:
The instructor plays a crucial role in guiding learners through each phase, providing cues, and ensuring that learners remain engaged and challenged.
Debriefing:
Structured debriefing using INACSL standards, focusing on student reflection, discussion of outcomes, and team dynamics.
Participant Engagement:
Learners are placed in a high-fidelity simulation environment that immerses them in a realistic OR scenario, engaging them fully in both clinical practice and critical thinking.
Resources and Equipment:
Igloo Immersive 3D OR Space.
MH Cart: Including dantrolene and cooling materials.
Patient Monitor: Displays HR, BP, Temp, CO2 levels.
Simulated Anesthesia Machine: To demonstrate changes in CO2 and monitor settings.
Provide guidance on the simulation, ensuring students understand the high-stakes nature of the scenario and how to manage the patient situation in real-time.
After the simulation, debrief with the students to discuss their experiences, focusing on critical thinking, team communication, and decision-making processes.
For Students
You will be placed in a simulated OR environment where you will encounter a patient during surgery. This scenario is designed to provide you with the experience of managing a patient in the perioperative environment and monitoring the status throughout the procedure. You may be asked to intervene appropriately if the patient status changes in any way. Pay close attention to your patient status as you should as the nurse caring for this patient. After the simulation, reflect on your actions and the outcomes during the debriefing session.
Benefit to Students
Critical thinking and decision-making in high-pressure situations
Exposure to rare clinical events (high-acuity, low-occurrence scenarios)
Digital literacy through the use of AI and immersive 3D technologies
Enhanced clinical confidence and preparedness.
Improved teamwork and communication skills
