Triage Tactics Phase 1: Dynamic Decision Module
This demonstration was created for a professional conference presentation on applying Merrill's First Principles of Instruction to interactive healthcare training. The module teaches Emergency Medical Technicians dynamic re-triage decision-making during mass casualty incidents, leveraging AI-generated media (video, audio, and imagery) to create emotionally engaging, Pixar-style scenarios within an accelerated development timeline.
Built in Articulate Storyline 360, the project showcases the integration of instructional design theory with cutting-edge AI tools to produce high-quality educational experiences. The demo highlights the Activation, Demonstration, and Application phases of Merrill's framework through authentic patient scenarios that balance clinical accuracy with compelling narrative.
Tools and Documents
Articulate Storyline 360
Canva
Genspark AI
We Are Learning
Gemini Veo 3 & Kling v2.5 Turbo Pro (video generation)
ElevenLabs V3 TTS (audio narration)
Flux Pro Ultra, GPT-Image-1, Ideogram V3 (image generation)
How I Applied Merril’s FPI Demo
Complete eLearning Demo
Emergency Medical Technician Mass Casualty Training
Learning Outcomes:
Upon successful completion of this interactive training module, Emergency Medical Technicians will be able to:
Apply START triage protocols to categorize patients into four priority levels (Immediate, Delayed, Minor, Deceased) during mass casualty incidents.
Demonstrate dynamic re-triage decision-making by reassessing patient priority levels as conditions change during emergency response scenarios.
Prioritize patient treatment decisions under time-constrained conditions, correctly identifying which patients require immediate intervention.
Justify triage decisions using evidence-based clinical reasoning, articulating the rationale for patient categorization based on observable vital signs and injury patterns.
Recognize the emotional and cognitive challenges of mass casualty triage and apply stress management techniques to maintain clinical effectiveness under pressure.
Research Methodologies:
Needs Analysis:
Conducted interviews with EMS training coordinators and EMT supervisors to identify gaps in mass casualty preparedness training
Reviewed incident reports from regional mass casualty simulations
Analyzed existing training materials and identified lack of emotionally engaging, realistic practice scenarios
Literature Review:
Examined research on Merrill's First Principles of Instruction and their effectiveness in healthcare training contexts
Reviewed studies on scenario-based learning in emergency medical education
Analyzed cognitive load theory applications in high-stress medical training environments
Instructional Design Framework:
Applied ADDIE methodology with emphasis on design and development phases
Mapped learning objectives to Merrill's First Principles (activation, demonstration, application)
Developed assessment criteria based on National Registry of Emergency Medical Technicians triage competency standards
Technology Integration:
Evaluated AI-generated media capabilities through pilot testing with sample scenarios
Assessed Pixar-style narrative design to increase emotional engagement while maintaining clinical accuracy
Conducted quality assurance testing with EMT instructors to validate clinical accuracy
Prototype Testing:
Developed alpha version and conducted walkthroughs with EMT instructors to verify scenario authenticity
Gathered feedback on user interface design, navigation flow, and scenario pacing
Refined branching scenarios based on instructor recommendations