Dispatcher Training Simulator and Feedback Engine

Inspiration

The project was inspired by the critical role emergency dispatchers play in saving lives. We identified a gap in traditional training methods, which often lack realistic simulations and tailored feedback. Our goal was to build a solution that not only prepares dispatchers for high-pressure situations but also provides actionable insights to improve their performance.

What We Learned

Understanding Emergency Protocols: Gained a deeper understanding of 911 dispatcher protocols and the importance of structured communication in emergencies.
AI in Simulation: Explored the use of conversational AI for creating realistic and dynamic role-play scenarios.
Feedback Automation: Learned how to leverage LLMs to generate meaningful, actionable feedback reports.
Team Collaboration: Enhanced our skills in coordinating across different roles, from data scientists to developers.

How We Built It

Architecture Design: Designed a modular architecture with components for scenario generation, distressed caller simulation, and feedback analysis.
Technology Stack: Used Python for core logic, AI Agents framework along with LLMs for conversation and feedback generation, and structured a scalable system for seamless integration.
Iterative Development: Adopted an agile approach, testing and refining each module with realistic scenarios.

Challenges Faced

Scenario Realism: Creating highly realistic and dynamic scenarios to mimic real-life emergency calls.
Feedback Accuracy: Ensuring the generated feedback aligned with established protocols while remaining actionable.
Scalability: Designing a system that can handle multiple users and scenarios without compromising performance.
Balancing Complexity: Maintaining a balance between creating complex simulations and ensuring user-friendliness for trainees.

This journey has been a transformative experience, combining innovation and real-world impact to make a difference in emergency preparedness.