{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:41:58Z","timestamp":1766050918218,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1065","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"9582-9590","source":"Crossref","is-referenced-by-count":1,"title":["LogiDebrief: A Signal-Temporal Logic Based Automated Debriefing Approach with Large Language Models Integration"],"prefix":"10.24963","author":[{"given":"Zirong","family":"Chen","sequence":"first","affiliation":[{"name":"Vanderbilt University"}]},{"given":"Ziyan","family":"An","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}]},{"given":"Jennifer","family":"Reynolds","sequence":"additional","affiliation":[{"name":"Metro Nashville Department of Emergency Communications"}]},{"given":"Kristin","family":"Mullen","sequence":"additional","affiliation":[{"name":"Metro Nashville Department of Emergency Communications"}]},{"given":"Stephen","family":"Maritini","sequence":"additional","affiliation":[{"name":"Metro Nashville Department of Emergency Communications"}]},{"given":"Meiyi","family":"Ma","sequence":"additional","affiliation":[{"name":"Vanderbilt University"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:01Z","timestamp":1758627361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1065"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1065","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}