Preparing Citizens for Emergency Calls with a Hybrid FSM-LLM Dialogue Agent
Emergency calls are time-critical, verbal-only interactions in which call takers must assess severity and make decisions based solely on the caller’s description. Effective communication is critical during these calls, especially since most callers are inexperienced and untrained due to the rarity of emergency situations. To address this challenge, we develop a task-oriented dialogue agent that simulates emergency call takers to prepare citizens for effective medical emergency communication. It uses a finite-state machine as its dialogue policy and large language models for natural language understanding. This agent conducts protocol-guided interactions and maps caller descriptions to structured symptoms. In a small exploratory study with human participants, our agent achieved higher observed classification accuracy and dialogue efficiency while maintaining high naturalness scores.