top of page
Search

Measuring Training Effectiveness With AI: How to Get Past Level 1 Smile Sheets

Writer: Odin Training
Odin Training
Sep 9
6 min read

Most training units can tell you how many people attended, how many passed the test, and how the room felt on the way out. Far fewer can tell you whether anything changed on the job. One 2026 review of L&D measurement practice reported that up to 67 percent of organizations cannot demonstrate the business impact of their training programs, and only 8 percent of L&D professionals feel highly confident in their ability to measure it.

The problem is not a shortage of frameworks. Kirkpatrick has been in print since 1959 and almost every training developer can recite the four levels. The problem is that Levels 3 and 4 require instrument design, data collection over time, and qualitative analysis that nobody has hours for once a course ships. AI does not solve the measurement problem, but it removes a large part of the labor that causes teams to stall at Level 1. This post covers where AI genuinely helps with training evaluation, where it should not be trusted, and how to build measurement into a project before delivery instead of after.

Why Evaluation Stalls at Reaction and Recall

Level 1 (Reaction) and Level 2 (Learning) survive because they are cheap. A post-course survey and a knowledge test can both be administered in the classroom on the day, while participants are still in the room and still willing. Level 3 (Behavior) requires you to go back weeks later, observe performance, and compare it to a documented baseline. Level 4 (Results) requires you to isolate the training effect from every other variable acting on the organization at the same time.

Kirkpatrick Partners has updated the model to make the performance environment an explicit part of Level 3, which is a useful correction: officers and employees often fail to apply training because supervisors, workload, or equipment prevent it, not because the training was poor. That correction also raises the analysis burden, because you now need to capture what people did and what conditions they did it under. This is where the measurement plan usually dies, not because training developers do not know what to do, but because the next project has already started.

What AI Does Well in Training Evaluation

Three evaluation tasks map cleanly onto what current language models are actually reliable at.

  • Qualitative analysis at scale. Open-ended survey comments are the most useful and least used evaluation data most training units collect. Two hundred free-text responses take hours to code by hand, so they get skimmed and forgotten. An AI model will theme them, count the frequency of each theme, and flag outliers in minutes. The recurring finding in practice is not sentiment, it is specificity: participants understood the content but had no time to practice it, or the examples came from the wrong context.

  • Test item analysis. Feed an AI your item-level results and it will identify questions that everyone passed (too easy to be diagnostic), questions that everyone failed (usually a flawed item, not a knowledge gap), and distractors that no one ever selects. This is standard psychometric work that most training units skip entirely.

  • Instrument drafting. Writing a behavior-focused observation checklist or a 60-day follow-up survey from a set of learning objectives is exactly the kind of structured drafting task AI handles well. You still edit it, but you start from a draft instead of a blank page.

Notice what these have in common. AI is doing the analysis and drafting labor. It is not deciding what matters, and it is not generating the data.

Build the Measurement Instrument Before You Build the Course

The most useful shift is one of sequencing. Evaluation is treated as a post-delivery activity in most projects, which guarantees you have no baseline to compare against. If you design the Level 3 instrument during analysis, the instrument tells you what the course has to accomplish.

A workable prompt structure for this looks like the following: give the AI your performance gap statement, your terminal objectives, and the job context, then ask for a three-part evaluation plan covering what observable behaviors indicate transfer, what existing organizational data could serve as an indicator, and what a 60-day supervisor follow-up should ask. Require it to distinguish between behaviors that can be directly observed and behaviors that can only be inferred from documentation.

The output will need work. AI tends to propose indicators that sound rigorous but are not collectable in your environment, and it will suggest metrics your agency does not track. That editing pass is the point: you use the model to produce a wide first draft, then narrow it with knowledge of what your organization can actually measure.

The Transfer Problem in Law Enforcement Training

Policing has a well documented version of this gap. Analysis published through CrimRxiv notes that firearms qualification pass rates commonly exceed 90 percent, while synthesized data on officer-involved shootings shows a weighted average incident hit rate near 46 percent and a per-round hit rate near 25 percent. That is a Level 2 score with no Level 3 relationship to it. The training measured something, but it did not measure the thing that matters under stress.

The National Policing Institute has been running a national scan of field training practices specifically because program models and evaluation approaches vary so widely between agencies. Agencies do hold usable Level 3 and Level 4 data: field training officer evaluations, use of force reports, complaint records, supervisor after-action notes, and body worn camera review findings all exist. They are unstructured, scattered across systems, and rarely analyzed against training records. Reviewing anonymized after-action summaries against a specific set of trained behaviors is a legitimate AI use case, and it is closer to reach than most training units assume.

Where AI Should Not Touch Your Evaluation Data

Three limits are worth stating plainly.

  • Personnel data does not go into a public model. Named performance evaluations, complaint files, and disciplinary records require an enterprise tool with a data processing agreement, or they require de-identification before analysis. This is not a technicality in a law enforcement context.

  • AI cannot establish causation. It will describe correlation in confident language if you let it. A model that tells you training reduced incidents has told you nothing, because it has no access to the confounding variables and no way to test them.

  • Do not confuse faster with better. A themed summary of survey comments in 90 seconds is a real gain only if the underlying instrument asked useful questions. AI applied to a weak evaluation design produces a polished summary of weak data.

A Practical Starting Point

If you want a first application that produces value this quarter, take the last three courses you delivered and run their open-ended feedback through a single analysis. Ask for recurring themes across all three, ranked by frequency, with the specific comments supporting each theme. Cross-course patterns are more actionable than any single course report, and they are almost never surfaced because nobody has time to read 600 comments.

From there, pick one high-consequence course and build the Level 3 instrument for it before the next delivery. One course with real transfer data is worth more to your credibility than a full year of satisfaction scores.

Want to Build These Skills With Your Team?

If you want your training team to start applying AI to evaluation design and analysis, I offer private 4-hour virtual workshops designed specifically for training developers. We work through your department's actual projects using multiple AI platforms, so participants leave with practical skills and working materials, not just theory.

Format: Private virtual sessions for up to 20 participants

Investment: $2,000 USD / $2,500 CDN per workshop

To book a session or ask a question, email kerry.avery@shaw.ca or visit the workshops page on this site.

Sources

A Note on AI Use

This post was researched and drafted with AI assistance, then reviewed and edited for accuracy and voice. All practical recommendations reflect my own instructional design experience.

 
 
 

Comments

bottom of page