AI-Based Assessment Models for Professional Training in Police and Security Agencies

Professional training in police and security agencies requires dependable, objective and context-specific assessment mechanisms that can assess complex competencies, cognitive, behavioral, and ethical, respectively. Traditional types of assessment are frequently choked by subjectivity, inconsistency, and unsure feedback to be less than effective in a trained, high stakes situation. This paper proposes an Artificial Intelligence (AI) based assessment framework that utilizes multi-modal training data, i.e., video, audio, textual reports, simulator telemetry, and metadata of the scenarios, to provide a continuous, competency-driven evaluation. The methodology includes approaches to calibrate the difficulty of scenarios used, approaches to make learning fair, approaches to estimate the uncertainty with human-in-the-loop review, and explainable AI to be transparent, accountable, and ethical. Experimental results show a significant increase in assessment accuracy, inter-rater reliability, bias reduction, and feedback granularity as compared to conventional and baseline machine learning methods. The proposed framework promotes customized feedback and skill building to improve the effectiveness and readiness of the training and to address skill development more effectively. Overall, the contribution of this research to the field is providing an end-to-end scalable, ethically sound evaluation solution designed to the requirements of the evolving nature of modern police and security training systems.

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