PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training
#PACE #9-1-1 #call-taker #training #adaptive curriculum #personalized learning #emergency response
📌 Key Takeaways
- PACE is a new adaptive training system for 9-1-1 call-takers.
- It personalizes the curriculum to match individual learner needs.
- The system aims to improve training efficiency and effectiveness.
- It is specifically designed for high-stakes emergency response roles.
📖 Full Retelling
arXiv:2603.05361v1 Announce Type: new
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 Emer
🏷️ Themes
Emergency Training, Adaptive Learning
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--> Computer Science > Artificial Intelligence arXiv:2603.05361 [Submitted on 5 Mar 2026] Title: PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training Authors: Zirong Chen , Hongchao Zhang , Meiyi Ma View a PDF of the paper titled PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training, by Zirong Chen and 2 other authors View PDF HTML 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 , 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.05361 [cs.AI] (or arXiv:2603.05361v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv...
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