| Title: |
LogiDebrief: A Signal-Temporal Logic Based Automated Debriefing Approach with Large Language Models Integration |
| Language: |
English |
| Authors: |
Zirong Chen (ORCID 0000-0002-6466-4549); Ziyan An; Jennifer Reynolds; Kristin Mullen; Stephen Martini; Meiyi Ma (ORCID 0000-0001-6916-8774) |
| Source: |
Grantee Submission. 2025Paper presented at the International Joint Conference on Artificial Intelligence (IJCAI 2025) (34th, 2025). |
| Peer Reviewed: |
Y |
| Page Count: |
15 |
| Publication Date: |
2025 |
| Sponsoring Agency: |
Institute of Education Sciences (ED) |
| Contract Number: |
R305C240010 |
| Document Type: |
Speeches/Meeting Papers; Reports - Research |
| Descriptors: |
Emergency Programs; Automation; Artificial Intelligence; Safety; Safety Equipment; Quality Assurance; Information Systems; Technology Integration; Guidelines; Audits (Verification); Compliance (Legal); Police |
| Geographic Terms: |
Tennessee (Nashville) |
| 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. |
| Abstractor: |
As Provided |
| IES Funded: |
Yes |
| Entry Date: |
2025 |
| Accession Number: |
ED672542 |
| Database: |
ERIC |