AI Enhancement, Psychometric Examination, and Adaptation of a K-12 Risk Assessment
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Project Summary
Soft targets like K–12 schools face growing physical, cyber, and natural hazards but often lack assessors, staff, and funds to manage risk. This project follows on from the NCITE's previous work developing the Soft Target Risk Assessment Engine, an AI-enhanced tool built for K-12 schools under prior DHS and State of Nebraska funding.
Purpose/Objectives
The project seems to address three questions: First, does the tool produce valid, reliable, and actionable risk characterizations across different school environments? Second, does an AI-enhanced interface improve completion rates, data quality, and user satisfaction relative to the existing version? Third, what would it take to adapt the tool to another category of soft target?
Method
Reliability and validity testing uses multiple assessors evaluating the same school, repeated assessments by a single assessor over time, retrospective application to schools that experienced security incidents, and comparison against a measure of physical security culture. Usability testing compares the AI-enhanced interface with the existing tool on completion metrics, satisfaction surveys, and think-aloud protocols, tracking how users revise responses following AI suggestions. Cross-sector feasibility uses subject matter expert focus groups and comparative surveys against parallel K-12 data.
Outputs and Impact
This project delivers CISA and the broader school safety community an empirically validated, self-administrable K-12 risk assessment tool with an AI-enhanced interface, extending rigorous vulnerability assessment to schools that cannot access a Protective Security Advisor and establishing a first evidence-based pathway for adapting the instrument to other soft target sectors.
Research Team
Sydney Reichin, Ph.D.- North Carolina State University
- Assistant Professor, I-O psychology program
- Expertise: predicting, understanding, and addressing workplace deviance