![[2602.13241] Real-World Design and Deployment of an Embedded GenAI-powered 9-1-1 Calltaking Training System: Experiences and Lessons Learned](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13241] Real-World Design and Deployment of an Embedded GenAI-powered 9-1-1 Calltaking Training System: Experiences and Lessons Learned
Summary
This article discusses the design and deployment of a GenAI-powered training system for 9-1-1 call-takers, highlighting the challenges faced and lessons learned from real-world implementation.
Why It Matters
The integration of AI in emergency services training addresses critical staffing shortages and enhances the efficiency of training programs. This research provides valuable insights for public safety organizations looking to adopt AI technologies in high-stakes environments.
Key Takeaways
- AI-driven training systems can significantly improve the efficiency of emergency call-taker training.
- Real-world deployment exposes unique challenges not visible in controlled environments.
- Engagement patterns and user interactions provide critical data for refining training systems.
- Concrete design practices can help mitigate challenges in embedding AI in public safety.
- Lessons learned can guide future implementations of AI in safety-critical sectors.
Computer Science > Computers and Society arXiv:2602.13241 (cs) [Submitted on 30 Jan 2026] Title:Real-World Design and Deployment of an Embedded GenAI-powered 9-1-1 Calltaking Training System: Experiences and Lessons Learned Authors:Zirong Chen, Meiyi Ma View a PDF of the paper titled Real-World Design and Deployment of an Embedded GenAI-powered 9-1-1 Calltaking Training System: Experiences and Lessons Learned, by Zirong Chen and 1 other authors View PDF HTML (experimental) Abstract:Emergency call-takers form the first operational link in public safety response, handling over 240 million calls annually while facing a sustained training crisis: staffing shortages exceed 25\% in many centers, and preparing a single new hire can require up to 720 hours of one-on-one instruction that removes experienced personnel from active duty. Traditional training approaches struggle to scale under these constraints, limiting both coverage and feedback timeliness. In partnership with Metro Nashville Department of Emergency Communications (MNDEC), we designed, developed, and deployed a GenAI-powered call-taking training system under real-world constraints. Over six months, deployment scaled from initial pilot to 190 operational users across 1,120 training sessions, exposing systematic challenges around system delivery, rigor, resilience, and human factors that remain largely invisible in controlled or purely simulated evaluations. By analyzing deployment logs capturing 98,429 user interactio...
