Health administration programs adopt simulations and AI to train decision-making
A scoping review from George Mason University analyzed 83 articles published between 2017 and 2025 on case-based learning in health administration. It found educators are using tools such as WeChat, lifelike mannequins, and ChatGPT to create realistic scenarios. Faculty guidance, discussion, and feedback remain central to effective learning.
A George Mason scoping review, led by John Cantiello with Renee Geschke, examined 83 articles from 2017 to 2025. It appears in a special issue of the Journal of Health Administration Education centered on case studies, simulation, and game-based learning. Because health administration scholarship on this method is limited, the authors also considered nursing, public health, medicine, and business.
Examples include WeChat-based pediatric orthopedic cases, lifelike mannequins, and ChatGPT-generated management scenarios. The review cautions that generative AI may leave out details or give wrong answers, and learners might depend on it too much. Active formats were associated with sharper analytical reasoning, stronger problem-solving, and greater involvement, while clear objectives, structured cases, and instructor feedback remained essential.
This evolution in health administration training could shape how future leaders handle real-world pressures, potentially affecting patients and staff who depend on their decisions. Educators and students may benefit from realistic practice, but programs could also face challenges ensuring AI tools are accurate and appropriately supervised. If simulations supplement, rather than replace, guided discussion and feedback, they may help graduates make more thoughtful choices under uncertainty.