An adaptive AI training system for American Red Cross of Quad Cities & Northwest Illinois. Disaster Duty Officers practice realistic voice conversations with adaptive AI personas before their first live call with disaster victims.
In retail management, there's an iron rule: new employees practice extensively before interacting with real customers. Every cashier runs through mock transactions. Every salesperson practices product knowledge with supervisors before working the floor.
Yet Red Cross Disaster Duty Officers in regions like Quad Cities and Northwest Illinois dealing with people who have just lost everything in a fire face a different reality. Geographic dispersion across 20 counties forces virtual shadowing only. After hearing 3-5 calls, trainees immediately take live disaster response calls themselves.
This means their first real practice is with actual disaster victims.
People who have just lost everything in a fire or disaster deserve competent, compassionate assistance from thoroughly prepared Red Cross responders, not trainees practicing for the first time.
Red Cross volunteers with limited time to train who need confidence-building practice before their first live call. Safe practice reduces anxiety and burnout risk in this emotionally demanding role.
Training coordinators in Quad Cities and Northwest Illinois regions responsible for volunteer readiness across geographically dispersed teams. 24/7 accessible training enables scalable quality assurance.
The humanitarian mission benefits from improved volunteer retention, faster time to competency (45% reduction), and consistent service quality across all disaster response calls.
DDORC (Disaster Duty Officer Realtime Conversation) uses OpenAI's Realtime API to create AI-powered voice personas that sound and behave like real people in crisis. Unlike role-playing with colleagues (where trainees know it's fake), these AI personas provide emotionally authentic practice at scale.
Trainees engage in natural voice conversations with dynamic personas who adapt their emotional state based on how the trainee communicates. Make the persona repeat information too many times? The AI becomes agitated, just like a real person would. Ask empathetic questions? Critical information gets revealed that transactional questioning misses.
Within 30 seconds of call completion, trainees receive comprehensive After-Action Reports with 7+ quantified metrics, transcript excerpts with timestamps, and specific coaching notes on what worked and what to improve.
Scenario: Cooperative caller, clear information, no injuries
Example: Total loss fire, family safe at neighbor's house. Persona knows address, phone number, insurance status.
Learning Goal: Master the intake checklist with basic empathy. Build procedural competence and confidence.
Success Rate: 85%+ trainees pass on first or second attempt
Scenario: Emotionally distressed caller, injury/casualty, information gaps
Example: House fire with family member transported to hospital. Persona speaks in whispers, long pauses, fragmented speech. Becomes agitated if trainee repeats questions.
Learning Goal: Maintain rapport under emotional stress while collecting complete information. Practice adaptive communication.
Challenge: Persona shuts down if trainee asks same question after it's been answered
Scenario: Language barrier + complex medical needs + multiple household complications
Example: Limited English proficiency caller with elderly parent needing oxygen, multiple children with disabilities. Requires interpreter protocol.
Learning Goal: Execute complete professional call with all real-world complexity factors. Demonstrates readiness for live calls.
Scored on interpreter protocol, language access, medical needs assessment
Within 30 seconds of call completion, trainees receive comprehensive feedback with quantified metrics:
"Good reflective statements (4 instances). Repeated address question at 05:12 caused agitation. Practice single confirmation technique: collect information once, verify all details together before closing. CRITICAL: You ended without asking about injuries. This is required safety information."
Built as a production-ready web application using OpenAI's Realtime API for natural voice conversation. The platform demonstrates how cutting-edge AI technology can serve humanitarian missions while maintaining enterprise-grade reliability and accessibility standards.
Pilot testing with 12 Red Cross trainees (6 with DDORC, 6 traditional training) demonstrated quantifiable improvements across multiple dimensions of disaster response competency.
Reduction in Time to Competency (8 weeks → 4.5 weeks)
Higher Empathy Scores on First Live Call
Checklist Completion (vs. 63% traditional training)
Trainee Confidence (vs. 5.1/10 traditional)
Disaster victims receive consistent, compassionate, competent assistance from thoroughly prepared responders. Every DDO practices hundreds of scenarios before their first live call.
Confidence-building through safe practice reduces anxiety and burnout risk. Volunteers report feeling prepared and supported, leading to longer tenure in this emotionally demanding role.
24/7 availability enables scalable training across geographically dispersed regions. Objective metrics enable data-driven quality assurance and skill gap identification.
In retail, cashiers run 100+ mock transactions before serving real customers. Red Cross is higher stakes than retail, yet trainees historically had minimal practice. DDORC fixes this by enabling hundreds of realistic practice scenarios before the first live call with a disaster victim.
This volunteer project demonstrates how cutting-edge AI technology can serve humanitarian organizations. DDORC shows that the same innovation we bring to commercial products can make a real difference for disaster victims and the volunteers who serve them.