PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training
Revolutionizing 9-1-1 Call-Taker Training with Adaptive AI
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 evolv-ing 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 train-ing scenarios that balance acquisition of new com-petencies with retention of existing ones. PACE propagates evidence over a structured skill graph to accelerate diagnostic coverage and applies con-textual bandits to select scenarios that target gaps the trainee is prepared to address. Empirical re-sults show that PACE achieves 19.50% faster time-to-competence and 10.95% higher terminal mas-tery compared to state-of-the-art frameworks. Co-pilot studies with practicing training officers fur-ther 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.
Authored by Zirong Chen, Hongchao Zhang, Meiyi Ma - 2026
Unlocking Efficiency in High-Stakes Training
The current landscape of 9-1-1 call-taker training is plagued by scalability issues and a lack of personalized instruction. Human trainers, despite their expertise, struggle to tailor objectives to individual trainee competencies across over a thousand interdependent skills. This results in uniform curricula that overlook diverse learning patterns, hindering competency development and delaying critical skill acquisition. PACE directly confronts these challenges.
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Faster Time to Competence
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Higher Terminal Mastery
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Trainer Turnaround Time Reduction
Deep Analysis & Enterprise Applications
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Education Technology
Adaptive Learning
AI in Public Safety
Education Technology: Optimizing High-Stakes Training
Comparing PACE with traditional training methods highlights its transformative potential. PACE offers unparalleled precision in skill tracking, adapting to each trainee's unique learning and forgetting patterns. Its AI-driven recommendation system replaces subjective manual selections with data-informed, optimal scenario choices, leading to significantly faster competency development and a massive reduction in the administrative burden on trainers. This paradigm shift makes high-stakes training more efficient, effective, and scalable.
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| Learning Dynamics |
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| Recommendation Strategy |
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| Time-to-Competence |
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Adaptive Learning: Tailoring Education for Optimal Outcomes
PACE's core methodology involves a multi-faceted approach to adaptive curriculum optimization. It starts by building a robust understanding of each trainee's current skill levels and predicting how they learn and forget information. This data is then used to intelligently propagate knowledge across related skills using a structured graph, and finally, contextual bandits are employed to select the most effective training scenarios that target specific gaps while optimizing for retention and new skill acquisition.
Enterprise Process Flow
Probabilistic Beliefs over Skill States
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Individual Learning & Forgetting Dynamics
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Structured Skill Graph Propagation
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Contextual Bandits for Scenario Selection
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Personalized Training Recommendations
AI in Public Safety: Enhancing Emergency Response
The real-world co-pilot study at a local 9-1-1 call center provided compelling evidence of PACE's practical benefits. By reducing the debriefing turnaround time by over 95%, PACE significantly alleviated the burden on human trainers, allowing them to manage more trainees or focus on more complex cases. The high alignment rate with expert decisions underscores the system's ability to integrate seamlessly into existing pedagogical frameworks, providing reliable and effective support where it's most needed. This case study demonstrates how PACE directly contributes to improving public safety outcomes by ensuring 9-1-1 call-takers are trained more efficiently and to a higher standard.
95.45%
Alignment with Expert Judgment
PACE's recommendations achieved a remarkable 95.45% agreement with experienced training officers, validating its effectiveness in real-world scenarios and demonstrating that its learned policy captures domain-relevant pedagogical principles. This high alignment ensures that the AI's suggestions are trustworthy and practical for human trainers.
Real-World Impact: Enhancing 9-1-1 Operations
A local 9-1-1 call center partnered with Vanderbilt University to develop PACE. The system was integrated into existing training infrastructure, observing trainee interaction logs, conversation transcripts, and debriefing results. During the adaptive phase, PACE actively selected scenario batches. The results were dramatic: a 95.08% reduction in turnaround time for debriefing, from 11.58 minutes to merely 34 seconds. This efficiency gain saved up to 11.01 minutes per call, freeing up significant trainer time. This freed-up time can be reinvested into higher-value activities, addressing critical staffing shortages and improving overall training quality. The 95.45% alignment with expert judgment further solidified PACE's practical utility and trustworthiness, demonstrating its ability to capture complex pedagogical principles. Call-takers, as first points of contact in life-threatening situations, directly benefit from this enhanced, precise, and rapid skill development.
The real-world co-pilot study at a local 9-1-1 call center provided compelling evidence of PACE's practical benefits. By reducing the debriefing turnaround time by over 95%, PACE significantly alleviated the burden on human trainers, allowing them to manage more trainees or focus on more complex cases. The high alignment rate with expert decisions underscores the system's ability to integrate seamlessly into existing pedagogical frameworks, providing reliable and effective support where it's most needed. This case study demonstrates how PACE directly contributes to improving public safety outcomes by ensuring 9-1-1 call-takers are trained more efficiently and to a higher standard.
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Your AI Implementation Roadmap
A structured approach to integrating AI into your enterprise, ensuring maximum impact and seamless adoption.
Phase 1: Discovery & Strategy
In-depth analysis of your current training workflows, skill dependencies, and learning objectives. Define clear, measurable goals for AI integration and tailor the PACE framework to your specific operational needs.
Phase 2: Data Integration & Model Training
Securely integrate existing training logs, performance data, and protocol manuals. Train PACE's probabilistic belief models and learning dynamics estimators using your historical data to ensure accurate personalization.
Phase 3: Pilot Deployment & Validation
Deploy PACE in a controlled pilot environment with a subset of trainees and trainers. Continuously monitor performance, gather feedback, and validate AI recommendations against expert pedagogical judgment.
Phase 4: Full-Scale Rollout & Continuous Optimization
Scale PACE across your entire training program. Implement feedback loops for continuous model refinement, ensuring the system adapts to evolving protocols and trainee populations for ongoing efficiency gains.
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