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 interactions, organizational processes, and stakeholder engagement patterns, we distill four key lessons, each coupled with concrete design and governance practices. These lessons provide grounded guidance for researchers and practitioners seeking to deliver AI-driven training systems in safety-critical public sector environments where practical constraints fundamentally shape human-centric design.

翻译：紧急接警员是公共安全响应的第一道操作环节，每年处理超过2.4亿通电话，同时面临持续的训练危机：许多中心的人员短缺率超过25%，而培训一名新员工可能需要长达720小时的一对一指导，这会使经验丰富的在职人员脱离执勤岗位。传统训练方法在这些约束下难以扩展，既限制了覆盖面，也影响了反馈的及时性。与纳什维尔市大都会紧急通信部（MNDEC）合作，我们设计、开发并在现实约束条件下部署了一套基于生成式人工智能的接警训练系统。在六个月内，该系统从初始试点扩展到190名实际用户，完成了1,120次训练课程，揭示了系统交付、严谨性、鲁棒性及人为因素方面的系统性挑战——这些挑战在受控环境或纯模拟评估中大多难以察觉。通过分析记录有98,429次用户交互的部署日志、组织流程及利益相关方参与模式，我们提炼出四条关键经验，每条都附有具体的设计与治理实践。这些经验为致力于在安全关键公共部门环境中交付人工智能驱动训练系统的研究人员与实践者提供了基于实践的指导，其中现实约束从根本上塑造了以人为中心的设计。