Analysis of a Northern Virginia Law Enforcement Academy Recruits Using Interview Skill Improvement Software

Abstract

Charles J. Oakley

This study assessed the impact of an artificial intelligence-based interview training tool on the communication and interviewing skills of law enforcement recruits at a large U.S. criminal justice academy. Utilizing a mixed-methods design and coding of 813 interview transcripts, the research examined changes in the use of de-escalation language, empathy, open- and closed-ended questioning, filler words, and jargon across repeated training attempts. Statistically significant gains were observed in de-escalation, empathy, and open-ended questioning, suggesting that AI-driven simulation can reinforce essential communication competencies in police recruits. However, no significant reduction was found in negative verbal patterns, indicating a need for blended training approaches. These findings highlight AI’s potential as a supplement to traditional instruction in developing the interpersonal skills critical to modern policing.

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