Abstract
Training for 911 call-takers requires mastery of over a thousand interdependent skills, covering diverse incident types and protocol-specific nuances. A nationwide labor shortage is straining training capacity, yet effective instruction still demands that trainers tailor objectives to each trainee's evolving competencies. This personalization burden is one that current practices cannot scale.
Partnering with the Metro Nashville Department of Emergency Communications (MNDEC), we propose PACE (Personalized Adaptive Curriculum Engine), a co-pilot system that enhances trainer decision-making by:
- Maintaining probabilistic beliefs over trainee skill states;
- Modeling individual learning and forgetting dynamics;
- 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, achieving up to 95.08% reduction.
Blogger's Review: The PACE system significantly enhances the training effectiveness for 911 call-takers by integrating personalized learning with efficient decision support. This innovative approach not only addresses the labor shortage issue but also validates its superior teaching outcomes in practical applications, making it worthy of broader adoption in training fields.