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

2026-03-05

AI Summary

This study addresses the challenge of scaling personalized training for 9-1-1 dispatchers, who must master over a thousand interrelated skills—a task infeasible with conventional instructional methods. The authors propose the first intelligent tutoring system that integrates a structured skill graph with a contextual multi-armed bandit framework. By probabilistically modeling learners’ skill states and their learning-forgetting dynamics, the system dynamically recommends training scenarios that balance acquisition of new competencies with reinforcement of previously learned ones. This approach uniquely combines skill graph reasoning with adaptive curriculum planning to deliver high-accuracy, low-latency personalized decision support. Experimental results demonstrate a 19.50% reduction in time-to-proficiency and a 10.95% increase in final mastery rates. Moreover, the system’s recommendations align with expert judgments 95.45% of the time while reducing decision-making time by 95.08%.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• 9-1-1 call-taker training
• personalized curriculum
• skill mastery
• training scalability
• adaptive learning

Innovation

• personalized adaptive learning
• skill graph
• contextual bandits
• learning and forgetting dynamics
• intelligent tutoring system