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

TL;DR

PACE is an adaptive training system for 9-1-1 call-takers that personalizes learning paths, accelerates skill mastery, and reduces training time by leveraging probabilistic models and contextual decision-making.

Contribution

It introduces PACE, a novel co-pilot system that models trainee skills and dynamically recommends training scenarios to improve efficiency and effectiveness in emergency call-taker training.

Findings

Achieves 19.50% faster time-to-competence

Attains 10.95% higher mastery levels

Reduces training turnaround time to 34 seconds

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 time-to-competence and 10.95% higher terminal mastery compared to state-of-the-art frameworks. Co-pilot 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.