Arguably, the greatest impact of a healthcare simulation comes at the time of the debrief after the completion of the activity. The participants in the clinical simulation gather after their experience and discuss in a meaningful way how they arrived at the decisions, treatments, and care management completed within the allotted time frame. Debriefing for Meaningful Learning (DML) developed by Kristina Thomas Dreifuerst (2009), is a debriefing method that meets the Healthcare Simulation Standards of Best Practice (HSSBP) Debriefing Standard criteria. The integration of artificial intelligence (AI) into the DML model could enhance the process through personalized feedback, identification of key areas for improvement, and the capability to foster a deeper critical reflection. Tools such as chatbots provide facilitators with thoughtful, Socratic questions that ultimately lead to improved learner outcomes and greater retention of knowledge. This HealthySimulation.com article by Jamie Howell, MSN, RN, CHSE, will highlight how AI can be incorporated into DML with critical thought, analysis, and dialogue, which empowers learners to link their experiences in a healthcare simulation to a deeper level of comprehension and engagement with their peers and facilitator.
Integration of AI into Debriefing for Meaningful Learning
As the learners begin to unpack their healthcare simulation experience, DML allows them the process of self-reflection time, where they can do a personal deep dive into how the clinical simulation contributed to the comprehension of the content through the scenario that they completed. This reflection, while short in nature, can engage the students as they work through a layer of psychological safety components. With that intention, the students can engage AI in the process as well. The student can type their reflection statements into the chatbot with the request for guidance through the process. Through open-ended questions, students articulate their rationale for practice within the healthcare simulation. Socratic questions encourage critical thinking, which allows learners to examine their choices and the principles that guide their actions. For example, a facilitator might ask, “What factors influenced your decision to prioritize that treatment?” or “How would you approach this scenario differently in future practice?”
These questions bridge the gap between experience and comprehension, which creates a foundation for meaningful learning. As a facilitator, the objectives provide the construct of the direction and line of questions that will be asked of the participants. AI can aid in this process through a chatbot that is uploaded with educational materials appropriate to the enhancement of the scenario. The platform utilized for the chatbot at Western Technical College (WTC) in LaCrosse, Wisconsin, is Poe. This platform, like many others, offers a free and cost-associated version. The chatbot is tagged into every simulation so that the facilitator has an assistant who can provide thoughtful questions based on the events of the healthcare simulation as well as the objectives and deliverable outcomes that they hope to achieve.
Enhance Learner Outcomes with AI
At WTC, Jennifer West is the associated Chatbot for the Health and Public Safety Division. This AI tool is loaded with Open Education Resources that were developed by the State of Wisconsin for the programs in Health and Public Safety. The Chatbot can also be loaded with HSSBP standards, DML guidelines, and the healthcare simulation outline. With this information, the Chatbot is able to function as an assistant to the facilitator, where they can type in questions related to the scenario, the outcome, or treatment and care plans. The utilization of AI allows facilitators to input details about the clinical simulation scenario, objectives, or specific learner actions. The chatbot then generates targeted questions designed to promote reflection and critical thinking. For example, after a simulated cardiac arrest scenario, the chatbot might suggest questions like, “What influenced your decision to administer that particular medication?” or “What alternative actions could you have taken during the initial assessment?” This targeted support ensures consistency and depth in the debrief sessions, regardless of facilitator experience.
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Practical Applications at WTC
The integration of AI into the DML model at WTC has demonstrated numerous benefits. For example, at a mass casualty simulation, Jennifer West generated questions to help learners reflect on triage decisions and resource allocation. Facilitators reported that the chatbot’s assistance allowed them to focus more on the observation of student interactions and guidance with discussions rather than form questions in real time. In another instance, the chatbot was used to introduce a parallel scenario that involved a pediatric patient. This scenario required learners to adapt their knowledge to a new context that fostered adaptability and clinical judgment. Such applications showcase the versatility of AI and the ability to augment the debriefing process and expand upon educational opportunities.
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Participant Utilization of AI in DML
Students can utilize the chatbot feature in various ways to enhance their experience and foster collaboration. For instance, the chatbot can facilitate guided self-reflection as it offers tailored questions based on individual actions performed in the clinical simulation. Questions such as “What was your rationale for the administration of metoprolol?” or “How could holding the metoprolol change the patient’s outcome?” prompt participants to critically assess their decisions and evaluate the impact of their choices. Beyond self-reflection, the chatbot can also facilitate group discussions through structured questions for peer feedback.
Participants can collaboratively analyze decisions, compare outcomes, and learn from each other’s perspectives. For example, nursing students at Western Technical College utilized the chatbot to debrief after a scenario on fluid volume overload. The chatbot posed questions like, “How did your team prioritize care when your client was in distress?” This led to a rich discussion where students shared different approaches to the way they communicate as well as how they would manage an unstable client. This form of discussion and collaboration deepened their comprehension of effective team dynamics in high-stress environments.
As we continue to explore the benefits and contributions that AI tools offer for healthcare simulation, it will be imperative to recognize their transformative potential in the debriefing process. With real-time, personalized feedback, AI fosters clinical judgment and reflective practice, which empowers learners to gain deeper insights and improve their performance in complex scenarios. The variety of ways that AI can be integrated into practice not only supports meaningful experiences in healthcare simulation but also enhances the overall educational outcomes, which makes it more adaptable and effective. As we embrace AI in the DML process, the preparation of the learners will be key for the challenges and demands of their respective fields.
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Reference
- Dreifuerst, K.T. The essentials of debriefing in simulation learning: A concept analysis. Nursing Education Perspectives. 2009; 30: 109–11.
