Artificial intelligence may be creating a new challenge for police training, one that has little to do with whether AI can write a better report.
What happens when the quality of an officer’s documentation exceeds the officer’s ability to explain or defend it?
Imagine two police recruits responding to similar incidents.
The first independently organizes observations, identifies relevant facts, and writes a report reflecting their understanding of the investigation.
The second uses AI to transform fragmented notes into a polished, professional narrative.
Both reports may appear equally competent.
But are the officers equally prepared?
That is the question police academies and field training officers should be asking.
The Competency Illusion
AI can improve sentence structure, organization, and readability. However, professional-looking documentation does not necessarily establish that the person submitting it understands every statement.
Researchers have identified automation bias, a phenomenon in which individuals place excessive confidence in automated assistance and may overlook errors or contradictory information (Parasuraman & Manzey, 2010).
In law enforcement, this raises a concern.
An inexperienced officer might approve a technically impressive narrative without recognizing that important facts were omitted, assumptions were introduced, or investigative conclusions exceeded the available evidence.
The danger is not necessarily that AI writes poorly.
It is that AI may write convincingly enough to conceal weaknesses in the officer’s understanding.
The Training Gap
Learning transfer research demonstrates that applying knowledge across different situations requires more than performing well in the original learning environment (Barnett & Ceci, 2002).
For police recruits, that distinction becomes increasingly important when technology assists with documentation.
A recruit should demonstrate two separate competencies:
- Independent documentation: Can the officer identify, organize, and accurately explain relevant facts without AI assistance?
- AI-assisted verification: Can the officer recognize unsupported statements, factual omissions, and misleading conclusions in an AI-assisted draft?
Neither competency should substitute for the other.
A Different Approach to Police Training
Rather than prohibiting AI or allowing unrestricted dependence, academies could introduce structured assessments that distinguish independent writing ability from technology-assisted performance.
Recruits could complete documentation exercises independently, evaluate AI-assisted versions, identify discrepancies, and explain their decisions to instructors.
The National Institute of Standards and Technology (2023) emphasizes human oversight and clearly defined responsibilities in AI risk management.
Those principles matter most when officers learn skills that may later influence criminal investigations and judicial proceedings.
The future of police documentation training should not be measured solely by how professionally an officer’s report reads.
It should also be measured by whether the officer understands, verifies, and can defend what the report says.
A polished report may demonstrate what AI can produce.
But professional competence must demonstrate what the officer actually knows.
–Klyvorek & OpenAI
KLYVOREK helps law enforcement professionals strengthen report-writing skills through realistic fictional scenarios, self-paced practice, and AI assistance. The officer provides the facts. KLYVOREK builds the draft. The officer reviews, refines, and improves it.
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References
Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637. https://doi.org/10.1037/0033-2909.128.4.612.
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1.
Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055.
