After 11 years in Learning and Development—spanning the trenches of instructional design, LMS administration, and QA lead roles—I’ve seen plenty of "shortcuts." Usually, they end up costing more time in the long run. When I started piloting AI tools in our workflow 18 months ago, I approached it with the same skepticism I reserve for a bloated 60-slide PowerPoint deck that claims to be "engaging."
The reality? AI is a powerful assistant, but it’s a terrible final editor. It is confidently incorrect, prone to corporate fluff, and utterly incapable of understanding your organization’s unique context. If you want to validate AI training content effectively, you have to move past the "does it look good?" stage and into the "can I break this?" phase.
Here is how I’ve built a robust, sustainable training content QA process that leverages AI without sacrificing quality or credibility.
What Validation Means for AI-Assisted L&D
Validation isn't just proofreading for typos. When we talk about AI, validation is about checking for hallucinations, bias, and context decay. AI models are trained on the public internet, which means they love to hallucinate legal policies, generic leadership advice, and outdated software procedures.
In my personal "Gotchas" doc—a living repository of every mistake I’ve caught before it went live—most AI errors fall into three buckets:
- The "Confidence Trap": The AI states an incorrect procedure with complete authority. The "Tone Deafness": The content sounds like a robotic HR manual rather than something a human would actually say. The "Assessment Failure": Multiple-choice questions with two "correct" answers or ambiguous distractors.
To validate successfully, you must treat AI output as a rough draft from a junior intern who is eager to please but needs adult supervision.
Risk-Based QA: The Low vs. High Stakes Framework
Not all training requires the same level of scrutiny. If you are drafting a quick email announcement or a low-stakes refresher, you don't need a three-round SME review. However, if you are designing compliance training or technical safety procedures, the stakes are high. My approval workflow for training depends entirely on the risk level.
Content Type Risk Level Validation Strategy Refresher quizzes Low Spot check for logic; self-review of grammar. Internal best practices Medium Full fact-check against existing policy docs. Compliance / Legal / Safety High SME review, source cross-referencing, "break-it" testing. https://essaymama.org/how-do-i-validate-ai-content-for-regulated-training-topics/By classifying your content before you start the QA process, you avoid the trap of "over-validating" simple assets and "under-validating" critical ones.
Fact-Checking and Source Tracking
Never accept an AI output as the source of truth. If the AI claims that "the company policy on remote work is X," you must be able to point to the specific PDF or wiki page that supports that claim. My rule is simple: No source, no go.
The "Sourcing" Workflow:
Grounding the AI: Before the AI writes a word, feed it the source text. Use tools that allow for custom knowledge bases. Reverse Fact-Checking: Once the draft is generated, highlight every factual claim. If you cannot highlight the exact sentence in your source document that matches the AI’s claim, the AI is hallucinating. Delete it. The "Gotchas" Log: Keep a running document of instances where your AI-preferred model got a specific company term wrong. Use this to refine your system prompts.Stop trusting the AI to be your researcher. Use it as your synthesizer. Your job is to ensure the synthesis remains tethered to reality.
Targeted SME Review: Respecting Your Expert's Time
Nothing annoys me more than sending a Subject Matter Expert (SME) a 40-page document and asking, "What do you think?" That is lazy instructional design. If you want a smooth approval workflow for training, you must make the review process frictionless.
Instead of general feedback, give your SMEs a targeted checklist:
- The Accuracy Check: "Are there any inaccuracies in the technical workflow described on page 3?" The Context Check: "Does the tone reflect our current internal culture?" The "What’s Missing" Check: "I’ve synthesized the basics, but what is the 'unspoken' rule that learners usually get wrong?"
By framing the review as a specific inquiry rather than a general critique, you get better feedback, and your SMEs will actually thank you for it.
Testing Assessments Like a Learner Trying to Break Them
This is where I spend most of my QA time. I have a reputation for being the person who intentionally fails an assessment to see if the feedback is helpful or condescending. AI-generated questions are notoriously bad at nuance. They often create "trick" questions that rely on semantics rather than understanding.
When you validate an assessment:
- Attempt to pick the "wrong" answer that makes sense: If you can justify why a wrong answer is actually correct, your question is poorly written. Rewrite it. Check for "Always/Never": AI loves absolute statements. If your content uses these, it’s usually factually weak. Test the Feedback: Does the learner get a specific explanation of *why* they got it wrong, or just a generic "Sorry, try again"? If it's the latter, the AI has failed the assignment.
I usually rewrite every single multiple-choice question generated by AI at least three times. Once for clarity, once for removing ambiguous distractors, and once to ensure it connects directly to a learning objective.
Avoiding the "Corporate Robot" Voice
Another symptom of AI-generated content is a sterile, overly formal, and vague corporate voice. It’s the kind of writing that says "leveraging synergies to facilitate optimal outcomes" while meaning absolutely nothing. This is not how people learn. People learn through stories, clear examples, and direct language.
When I review, I use the "Read Aloud" test. If I wouldn't say the sentence to a colleague in the breakroom without feeling ridiculous, I cut it. You need to strip away the AI's tendency toward padding. If a sentence has five adjectives, cut it to two. If the paragraph could be a bulleted list, make it one. Validation is as much about removing the fluff as it is about verifying facts.
Final Thoughts: The Human remains the QA Lead
AI is a phenomenal tool for scaling L&D content, but it is not a replacement for a QA lead. You are the final barrier between a confused learner and a successful training program. Never hit "publish" just because the AI told you it looks professional.
Establish a rigorous training content QA process that treats every piece of AI output with healthy skepticism. Build your own "gotchas" list, keep your SMEs focused, and never stop testing your assessments until they are airtight. At the end of the day, our learners don't care that we saved three hours by using AI to write the Continue reading draft—they care that the training is accurate, useful, and worth their limited time.
Stay curious, stay skeptical, and keep rewriting those sentences until they sing.