PACE A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training

Jul 16, 2026

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9-1-1 call-taking training demands mastery of more than a thousand interdependent skills across heterogeneous incident types and protocol-specific nuances. A nationwide staffing shortage strains instructor capacity, yet effective teaching still requires adapting objectives to each trainee’s evolving competence and forgetting patterns. PACE (Personalized Adaptive Curriculum Engine), a co-pilot with Metro Nashville Department of Emergency Communications, maintains probabilistic skill-state beliefs, models learning and retention dynamics, and recommends scenarios balancing acquisition and preservation. Empirical results show 19.50% faster time-to-competence and 10.95% higher terminal mastery versus state-of-the-art methods, with 95.45% alignment to expert judgments and up to 95.08% turnaround-time reduction under estimation limits.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

Introduction

Engagement-Learning Discrepancy

Inductive Knowledge Tracing

Fine-Grained Tutoring

Persistent Learner Modeling

PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training Zirong Chen , Hongchao Zhang , Meiyi Ma College of Connected Computing, Vanderbilt University, Nashville, Tennessee 37235, USA {zirong.chen, hongchao.zhang, [meiyi.ma](meiyi.ma}@vanderbilt.edu)[}](meiyi.ma}@vanderbilt.edu)[@vanderbilt.edu](meiyi.ma}@vanderbilt.edu) arXiv :2603 .0536 1v2 [ cs .AI] 10 Jul 2026 Abstract 9-1-1 call-taking training requires mastery of over a thousand interdependent skills, covering diverse incident types and protocol-specific nuances. A nationwide labor shortage is already straining training capacity, but effective instruction still demands that trainers tailor objectives to each trainee’s evolving competencies. This personalization burden is one that current practice cannot scale. Partnering with Metro Nashville Department of Emergency Communications (MNDEC), we propose PACE (Personalized Adaptive Curriculum Engine), a co-pilot system that augments trainer decision-making by (1) maintaining probabilistic beliefs over trainee skill states, (2) modeling individual learning and forgetting dynamics, and (3) recommending training scenarios that balance acquisition of new competencies with retention of existing ones. PACE propagates evidence over a structured skill graph to accelerate diagnostic coverage and applies contextual bandits to select scenarios that target gaps the trainee is prepared to address. Empirical results show that PACE achieves 19 .50% faster timeto-competence and 10.95% higher terminal mastery compared to state-of-the-art frameworks. Copilot studies with practicing training officers further demonstrate a 95.45% alignment rate between PACE’s and experts’ pedagogical judgments on real-world cases. Under estimation, PACE cuts turnaround time to merely 34 seconds from 11.58 minutes, up to 95.08% reduction. 1 Introduction 9-1-1 call-takers are first points of contact in life-threatening situations, and their guidance makes differences between life and death. Training them for this role is notoriously difficult [Chen et al., 2025b] . A car crash, for instance, may begin as a routine law enforcement matter requiring only traffic blockage and vehicle description inquiries, but escalatesto paramedic involvement if injuries are reported; further if unknown fluid leakage is present, fire-related safety checks become necessary as the fluid might be flammable or even explosive. Traditional training relies on human trainers who review past performance and select subsequent training objectives accordingly [Chen et al., 2025a] . As trainee cohorts grow and experienced trainers remain scarce, this model struggles to scale. In addition, trainees also differ in how they learn: some acquire skills quickly but forget without reinforcement, while others need repeated exposure but retain knowledge longer [Radvansky et al., 2022] . Tailoring instruction to these differences improves outcomes substantially, but adds burden for trainers already monitoring progress across many trainees. As a result, most programs default to uniform curricula, overlooking individual learning patterns that could accelerate or hinder competency development. Despite recent advancements in personalized learning systems, we identify following challenges: (1) EngagementLearning Discrepancy. Educational recommendation systems often inherit objectives from information retrieval ande-commerce, such as maximizing click-through rates or session duration [Covington et al., 2016; Koren et al., 2009], which do not necessarily align with learning goals. While recent work incorporates learning signals [Verbert et al., 2012], fundamental limitations remain. Trainees may “prefer” scenarios they can complete easily, but skill acquisition requires practice near the edge of competence. In consequences, calltakers may perform well in training simulations yet remain unprepared for high-stress real-world calls involving unfamiliar incident combinations.

FAQs

What problem does PACE address in 9-1-1 call-taker training?

PACE addresses the scalability gap caused by needing personalization of training objectives while instructors remain scarce and trainees’ competence and forgetting dynamics differ over time.

How does PACE represent and update trainee competence?

PACE maintains probabilistic beliefs over trainee skill states and propagates evidence over a structured skill graph to support scalable diagnostic coverage and mastery estimation.

What training scenarios does PACE recommend and how does it balance goals?

PACE uses contextual bandits to select scenarios that target competency gaps while balancing the acquisition of new skills with retention of already-learned ones.

Jul 16, 2026

Upload by Finn