I’ve spent the last 18 months living in the messy intersection of generative AI and instructional design. Like many of you, I was initially seduced by the speed. Need five knowledge checks for a microlearning module on data privacy? AI can spit them out in seconds. But as someone who has spent 11 years as a QA lead, I’ve learned that speed is rarely the metric that matters when your learners are actually trying to apply that information on the job.
If you rely on AI to generate your assessments, you aren’t a content creator anymore—you’re an editor, a fact-checker, and a skeptic. In this post, I want to pull back the curtain on how to validate AI-generated content so it actually serves the learner, rather than just filling space in your LMS.
What Does "Validation" Actually Mean in the AI Era?
In traditional instructional design, validation is about alignment—does the assessment measure the stated learning objective? With AI-generated content, the definition expands. It now includes hallucination detection and pedagogical integrity. Simply put: Is the AI pulling facts from a reputable source, or is it gaslighting your learners with "hallucinated" policy updates?
Validation for AI-assisted work is a two-fold process: structural validation (is the question good?) and empirical validation (is the information accurate?). If you aren't doing both, you’re just pushing content into a vacuum.
The Risk-Based QA Framework
https://fire2020.org/how-to-validate-ai-generated-training-visuals-a-10-year-ld-veterans-guide/I keep a running "gotchas" document on my desktop. One of the most common entries is "over-validating trivial content." If you treat a quiz on the office holiday party policy the same way you treat a quiz on cybersecurity incident response, you will burn out your SMEs and your own brain. Use a risk-based approach to determine how much scrutiny an assessment needs.
Risk Level Content Type Validation Strategy Low Company culture, soft skills, fun facts Automated grammar check + basic SME spot check. Medium Standard operating procedures, common tools Manual review + distractor analysis + alignment check. High Compliance, safety, legal, medical, security Triple-blind SME review + source linking + "break-it" testing.Fact-Checking and Source Tracking: The Death of "Trust Me, Bro" AI
One thing that drives me absolutely up the wall is when I see L&D pros accept AI output without a source trail. If an AI generates a knowledge check about a regulatory requirement, your first response shouldn't be to check the grammar; it should be to ask, "Where did this information come from?"
To properly validate, every AI-generated fact must have a "Source Link" attached to it in your storyboard. If the AI cannot cite a specific internal policy, documentation, or verified version control training content external source, it is effectively a hallucination. If you don't know the source, you can't defend the answer key when a learner inevitably challenges it.
The "Break-it" Mentality: Distractor Quality and Answer Key Validation
This is where my "learner trying to break the assessment" quirk comes in. AI is notorious for creating distractors that are either painfully obvious or completely nonsensical. A bad distractor isn't just a waste of time; it’s a pedagogical failure that teaches learners to look for the "odd one out" rather than the correct application of a concept.
When you are reviewing a knowledge check review, ask yourself these three questions:
Is the correct answer objectively correct? (And can it be proven via your source material?) Are the distractors "plausible but wrong"? A good distractor should be a common misconception or a real-world mistake. If the distractor is obviously wrong, the learner learns nothing. Does the question stem have ambiguity? I rewrite questions until they are surgically precise. If a sentence can be interpreted in two ways, a learner will interpret it in the way that makes them fail.Targeted and Efficient SME Review
Nothing annoys me more than receiving a generic "Looks good to me" from a Subject Matter Expert. It’s lazy, and it’s dangerous. To get high-quality feedback, you must stop sending them open-ended requests.
Use a targeted SME review template to ensure their time is spent on the *accuracy* of the content, not the structure. When sending an assessment to an SME, include:
- The Learning Objective: "This question intends to measure X." The Source Material: "Based on the Q3 Policy update, page 4." The Answer Key: Explain *why* the correct answer is correct and *why* the distractors are wrong.
By framing the review this way, you turn the SME from a passive reader into an active auditor. If they can’t explain why the distractor is incorrect, then the question is flawed.
Final Thoughts: Your Role as the Last Line of Defense
We are currently in a "Wild West" phase of AI in L&D. It’s easy to get caught up in the productivity metrics—how many questions you generated, how much faster the module was finished. But the metrics that actually matter—the ones that keep you in your job and keep the learners safe—are the quality metrics.
My advice? Treat every AI output as if it were written by a very confident, very helpful, but occasionally hallucinating junior intern who has never read your company’s internal documentation. Use your "gotchas" doc. Test the assessments like a learner who *wants* to find a loophole. And for the love of all that is professional, if you see an assessment that uses corporate jargon that doesn't actually mean anything, delete it and rewrite it until it sounds like a human being wrote it.
The goal isn't to use AI to work less; it’s to use AI to work smarter so you can spend your time on the things that really move the needle for your learners.