When police reports are poorly written, the problem is often treated simply as a writing problem.
This diagnosis is incomplete.
An officer can know exactly what happened yet struggle to convert observations, statements, evidence, and actions into a narrative that another person can reconstruct months later.
The real skill is turning facts into documentation without changing the facts.
Law enforcement training has recognized this challenge for decades. California’s investigative report-writing curriculum specifically addressed note-taking, narrative organization, facts versus inferences, report content, and clear writing. The U.S. Department of Justice’s Police Training Officer model likewise identifies report writing as a core competency and uses problem-based learning that requires recruits to identify known facts, learning issues, and potential actions.
That suggests a better training model.
Give recruits a realistic fictional incident, not a blank report form.
Let them interview, take notes, identify relevant facts, distinguish observations from assumptions, and construct the report.
Then challenge the result.
What fact supports that sentence?
Did the officer actually observe that?
What information is missing?
Could a prosecutor understand what happened without asking the officer to rewrite it?
Then repeat the exercise with increasingly complicated scenarios.
Artificial intelligence could make this approach even more interesting, but not by writing the complete report for the recruit.
AI could become a training mirror.
A recruit could enter approved fictional facts, compare how those facts were structured, identify omissions, challenge unsupported language, revise the narrative, and explain every correction to a training officer.
That matters because AI introduces its own risks. The National Institute of Standards and Technology (NIST) emphasizes clearly defined human responsibilities and oversight when humans and AI interact, including training for people who operate and oversee AI systems.
The lesson, therefore, becomes more than grammar.
The recruit learns two skills simultaneously: How to transform facts into professional documentation and how to recognize when technology transforms them incorrectly.
This is where AI-assisted law enforcement documentation could play an important role in training.
Don’t use AI to help recruits avoid learning how to write.
Use it to create more opportunities to practice thinking before they write.
Ultimately, the most important question isn’t whether a police report sounds professional.
It’s whether every important sentence can be traced back to something the officer actually knows.
–Klyvorek & OpenAI
KLYVOREK, a division of the American Academy of Advanced Thinking, LLC, is an emerging AI-assisted law enforcement documentation platform currently focused on incident reports and structured search and arrest warrant application drafts. The officer provides the facts, AI assists with documentation, and law enforcement personnel review the result and remain in control.
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References
California Commission on Peace Officer Standards and Training. (1994). Basic course instructor unit guide 18: Investigative report writing. Office of Justice Programs, U.S. Department of Justice. Office of Justice Programs.
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. NIST AI Risk Management Framework.
Office of Community Oriented Policing Services. (2006). Police Training Officer (PTO) program: A contemporary approach to post-academy recruit training. U.S. Department of Justice. Office of Justice Programs.
