---
title: "LogiDebrief: A Signal-Temporal Logic Based Automated Debriefing Approach with Large Language Models Integration"
authors:
  - "Zirong Chen"
  - "Ziyan An"
  - "Jennifer Reynolds"
  - "Kristin Mullen"
  - "Stephen Maritini"
  - "Meiyi Ma"
doi: "10.24963/ijcai.2025/1065"
url: "https://ijcai.org/proceedings/2025/1065"
pdf: "https://ijcai.org/proceedings/2025/1065.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "9582-9590"
track: "AI and Social Good"
keywords:
  - "Multidisciplinary Topics and Applications: General"
  - "Natural Language Processing: General"
---

# LogiDebrief: A Signal-Temporal Logic Based Automated Debriefing Approach with Large Language Models Integration

Zirong Chen, Ziyan An, Jennifer Reynolds, Kristin Mullen, Stephen Maritini, Meiyi Ma

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 9582-9590. DOI: [10.24963/ijcai.2025/1065](https://doi.org/10.24963/ijcai.2025/1065)

## Abstract

Emergency response services are critical to public safety, with 9-1-1 call-takers playing a key role in ensuring timely and effective emergency operations. To ensure call-taking performance consistency, quality assurance is implemented to evaluate and refine call-takers' skillsets. However, traditional human-led evaluations struggle with high call volumes, leading to low coverage and delayed assessments.  We introduce LogiDebrief, an AI-driven framework that automates traditional 9-1-1 call debriefing by integrating Signal-Temporal Logic (STL) with Large Language Models (LLMs) for fully-covered rigorous performance evaluation. LogiDebrief formalizes call-taking requirements as logical specifications, enabling systematic assessment of 9-1-1 calls against procedural guidelines. It employs a three-step verification process: (1) contextual understanding to identify responder types, incident classifications, and critical conditions; (2) STL-based runtime checking with LLM integration to ensure compliance; and (3) automated aggregation of results into quality assurance reports.  Beyond its technical contributions, LogiDebrief has demonstrated real-world impact. Successfully deployed at Metro Nashville Department of Emergency Communications, it has assisted in debriefing 1,701 real-world calls, saving 311.85 hours of active engagement. Empirical evaluation with real-world data confirms its accuracy, while a case study and extensive user study highlight its effectiveness in enhancing call-taking performance.