I’ve spent the last 11 years in the trenches of Learning and Development. I’ve seen the rise and fall of "e-learning silver bullets" that promised to save us time and money. When AI tools started showing up in our workflow about 18 months ago, I felt the familiar urge to roll View website my eyes. We’ve been burned before by tools that promised to build courses in minutes, only to spend weeks in manual cleanup. However, I’ve learned that AI *is* different—provided you treat it like a well-meaning, hyperactive junior intern who sometimes hallucinates facts.
If you’re ready to speed up your production, you need a process for validation. You cannot just prompt, paste, and publish. If your QA process still consists of a stakeholder saying, “Looks good to me,” you are setting yourself up for a compliance nightmare or, at the very least, a very frustrated learner base. Let’s talk about how to run a training pilot that actually validates your AI-assisted work without turning your life into a perpetual QA loop.
What Validation Actually Means in an AI World
Validation for AI-assisted L&D isn’t just about catching typos. It’s about verifying intent and accuracy. AI excels at structure and syntax, but it often fails at context. When we talk about validating AI content, we are looking for three things:
- Factuality: Does the AI’s output align with verified internal documentation? Tone Consistency: Did the AI accidentally lapse into that "soulless corporate drone" voice that makes learners click away? Structural Integrity: Did the logic follow a sound pedagogical path, or did it just throw buzzwords at the screen?
Validation means we are testing the output against the "ground truth" of your organization. If your pilot doesn't explicitly check these three vectors, you aren't validating; you're just proofreading.
Risk-Based QA: Don’t Treat Everything the Same
One of the biggest mistakes I see teams make is applying the same level of scrutiny to a "How to fix your printer" job aid as they do to "Cybersecurity and Data Privacy Compliance." Stop that. You need a risk-based approach to your beta test learning content. Here is the framework I use to decide where to focus my QA energy:
For low-stakes content, I spend 10 minutes on an automated grammar check and a quick read-through. For high-stakes content, every paragraph generated by an AI must be cross-referenced with internal policy documents. If the AI cites a policy page, I don’t just read the summary; I open the source PDF and verify the clause myself. Never trust a "citation" from an LLM without clicking the link.
Fact-Checking and Source Tracking
If you are using AI, you must have a "Source Tracking" document. This is one of my "gotchas"—a lesson learned the hard way when an AI confidently explained a workflow that hadn't been updated since 2019.
Every piece of AI-assisted content should be mapped back to a human-verified source. If you can’t point to the source document, regulation, or SME interview that supports the claim, cut it. Your validation cycle should look like this:
Drafting: AI generates the initial storyboard based on your source files. Mapping: You tag every key learning objective back to a specific paragraph in your source document. Verification: An L&D professional (not the AI) validates that the AI didn't hallucinate a "best practice" that contradicts the source.Targeted SME Review: How to Stop Wasting Everyone’s Time
SMEs hate reviewing content because they feel like they’re being asked to do our job. They see a 40-slide deck and their eyes glaze over. To run an efficient pilot, you have to change how you request feedback. Stop sending the whole course. Send targeted questions.
Instead of "Please review this for accuracy," send your SMEs a specific checklist:
- "In Slide 4, the AI claims X is the process. Does this match the current workflow on the shared drive?" "In the assessment section on slide 12, is this distractor answer technically plausible but incorrect, or is it confusing?" "Does the tone on slide 7 feel too formal, or does it sound like how our team actually talks?"
By framing the review as "help me verify this specific point," you turn the SME into a partner in the validation process, rather than a judge of your work product. It makes the feedback collection process much faster and ensures they actually look at the details that matter.
Designing the Training Pilot: Testing Like a Learner
When you move to the pilot phase, you need a diverse group of testers. I look for three types of people to include in my beta group:
The Skeptic: This person hates training. If there is a bug or an ambiguous instruction, they will find it. The New Hire: They have no context. If they can’t follow the logic, the course is failing. The Power User: They will test the limits of your assessment questions.I mentioned in my quirks that I test assessment questions like a learner trying to https://fire2020.org/how-to-validate-ai-generated-training-visuals-a-10-year-ld-veterans-guide/ break them. Here is how I do it in a pilot: I don’t try to answer correctly. I try to answer in the way a slightly distracted, frustrated learner would. I look for ambiguous wording where two answers could technically be correct. I look for "trick" questions that rely on hidden assumptions rather than knowledge of the material. If a learner can guess the right answer without reading the content, the assessment is broken. Fix it before launch.
The Iteration Cycle: Closing the Loop
A pilot is useless if you don’t have a defined iteration cycle. Do not launch a pilot and just "see what happens." Set a firm timeline. I typically give a pilot group 48 hours to provide feedback, followed by a 24-hour window for me to implement changes. If you let a pilot linger for two weeks, your feedback will be stale, and the energy around the project will dissipate.
When collecting feedback, use a simple rubric. I prefer to use a structured survey rather than open-ended comments:
Question Type The content was easy to understand. Likert Scale (1-5) I learned at least one thing I can use immediately. Yes/No Was there any point where you felt confused or misinformed? Open Text If you had to change one thing about this course, what would it be? Open TextThe "If you had to change one thing" question is gold. It forces the learner to prioritize their frustrations. If three people say, "The jargon on slide 5 was confusing," you know exactly where the AI failed you and where the human touch needs to come in.
Final Thoughts: The Human-in-the-Loop Advantage
The danger of AI isn't that it will replace us; the danger is that we will stop thinking critically because the output looks "polished." After 11 years, I’ve learned that the secret to great L&D is empathy for the learner. AI can mimic structure, but it can’t mimic empathy. It can’t understand why a learner is stressed or why they might be skimming a page in a hurry.
Use AI to move faster. Use your validation process to ensure you’re moving in the right direction. Keep your "gotchas" doc updated, keep your SME requests laser-focused, and for heaven's sake, stop saying "looks good to me" during QA. Look for the friction, look for the ambiguity, and look for the parts where the AI tried to sound smart but missed the point.
Your learners will thank you for the clarity, and your stakeholders will thank you for the results. Now, go break your own content before someone else does.