Currently submitted to: JMIR Medical Education
Date Submitted: Oct 21, 2026
Open Peer Review Period: Oct 22, 2026 - Dec 17, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
AI-Supported Flipped Learning with Gamified Video Scenarios for Prehospital Triage Training in Emergency Medical Technicians: A Quasi-Experimental Study
ABSTRACT
Background:
Growing demand for emergency medical services in Taiwan underscores the need for effective prehospital triage education. Conventional instructions offer limited opportunities to apply triage knowledge in realistic, time-pressured situations. AI-supported flipped learning with gamified video scenarios may provide individualized support and realistic decision-making practice.
Objective:
To evaluate the effects of AI-supported flipped learning with gamified video scenarios on triage knowledge, decision-making ability, confidence, accuracy, and course satisfaction among EMT-Ps learning the 5-level Prehospital Taiwan Triage and Acuity Scale.
Methods:
This two-group quasi-experimental study included 127 EMT-Ps. The experimental group (n=55) used AI for pre-class learning and on-demand information retrieval during in-class gamified video scenarios; the control group (n=72) received lectures and paper-based case discussions. Triage knowledge, decision-making ability, confidence, accuracy, and satisfaction were assessed using a test, questionnaires, and a skills examination. Generalized estimating equations estimated adjusted group × time effects.
Results:
Significant group × time effects favored the experimental group for triage knowledge (B=12.18; P<.001), decision-making ability (B=29.38; P<.001), and confidence (B=15.36; P<.001). The experimental group also had higher post-intervention accuracy and satisfaction (both P<.001).
Conclusions:
AI-supported flipped learning with gamified video scenarios improved all measured outcomes compared with conventional instruction. This approach may address pre-class knowledge gaps and strengthen triage decision-making practice in simulated clinical scenarios.
Citation
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Copyright
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