{"type":"ScholarlyArticle","title":"PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training","authors":[{"name":"Zirong Chen","slug":"zirong-chen","given":"Zirong","family":"Chen","affiliation":""},{"name":"Hongchao Zhang","slug":"hongchao-zhang","given":"Hongchao","family":"Zhang","affiliation":""},{"name":"Meiyi Ma","slug":"meiyi-ma","given":"Meiyi","family":"Ma","affiliation":""}],"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.\r\nPartnering 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.\r\nPACE 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. \r\nEmpirical 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.","doi":"10.24963/ijcai.2026/789","url":"https://ijcai.org/proceedings/2026/789","pdf":"https://ijcai.org/proceedings/2026/0789.pdf","markdown":"https://ijcai.org/proceedings/2026/789.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/789","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":789,"pages":{"first":7093,"last":7101},"keywords":["Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}