The Future of Crisis Response Training: AI-Generated Feedback for Incident Commanders?

Abstract

In today’s complex crisis landscape, effective incident command training relies on dynamic decision-making assessment frameworks. However, the Effective Command Behavioral Marker Framework (EC) momentarily demands a lot from human assessors, who must evaluate 72 criteria across a 5-point scale, leading to excessive cognitive load. This study explores whether AI-generated written feedback can support in the future assessors and possibly enhance learning outcomes by providing structured, data-driven insights of command training. We examine the assessment results from 85 incident commanders solutions to a virtual simulation ”School Fire” scenario. Key challenges brought out in the feedback included delayed decision-making, inter-agency coordination gaps, and situational awareness deficits. While expert feedback is valuable, the time constraints for compiling the written feedback create a discrepancy in the length and quality of the provided feedback. This study explores how AI can complement human assessor’s by reducing cognitive overload and enhancing incident command training through structured, data-driven feedback that supports assessors s expert judgment. Dynamic decision-making feedback systems using Human-AI collaboration may redefine training methodologies for next-generation incident commanders.