If there is one thing I’ve learned in my 11 years in Learning and Development, it’s that "looks good to me" is the most dangerous phrase in our industry. I keep a "Gotchas" document on my desktop—a running list of every disastrously ambiguous question, hallucinated fact, and broken assessment logic I’ve had to scrub before creating a training content audit plan a launch. Lately, a significant portion of those entries starts with, "Draft generated by AI."
Don't get me wrong; I’ve been using LLMs for 18 months to speed up storyboard development. But I’ve also spent years treating learners as if they are actively trying to break my assessments. When you move to AI-assisted microlearning, the role of the instructional designer shifts from "writer" to "editor-in-chief and professional skeptic." If you aren't validating your AI-generated knowledge checks, you aren't just lazy—you’re setting your learners up for frustration and your organization up for compliance risks.
What Validation Really Means in the AI Era
In the past, we wrote questions from scratch. We knew why we chose a specific distractor because we drafted it to test a common misconception. When we use AI to generate these items, we lose that intentionality. Validation, therefore, isn’t just about proofreading for typos. It is the systematic verification that the AI hasn't hallucinated a fact, introduced unintended ambiguity, or—my personal nemesis—created a "correct" answer that is actually debatable based on company policy.
Validation for AI-assisted work requires a three-tiered approach:
Logical Validation: Does the question actually align with the microlearning objective? Fact Verification: Are the premises of the question grounded in provided, verified source material? Psychometric Rigor: Are the distractors effective, or are they just filler?The Risk-Based QA Framework
Not every knowledge check requires the same level of scrutiny. If you are teaching a team how to use a new coffee machine in the breakroom, a slightly off-kilter question is a minor inconvenience. If you are training them on data privacy protocols or medical safety, an AI hallucination is a liability.
I use a simple risk-based matrix to decide how much time to sink into QA for a specific module:
Fact-Checking and Source Tracking: The "No-Source, No-Trust" Rule
AI is a brilliant synthesizer, but it is a terrible researcher. It loves to sound confident while being factually incorrect. I have a strict policy: If I cannot trace the question and its answer back to a specific sentence in our source documentation, the question is deleted.
When you generate a knowledge check, force your AI (or your process) to provide the reference. I prompt my tools with: "Generate 3 multiple-choice questions based on the provided text. For every question, include the specific paragraph number where the answer is found."
If the AI can't point to the source, it's hallucinating. If the source material doesn't support the answer, your prompt was too loose. By requiring source tracking, you eliminate the guesswork and force the AI to stay within the boundaries of your verified company knowledge base.
Distractor Quality: Moving Beyond Filler
The biggest giveaway of lazy AI-generated content is weak distractors. You know the ones: they are so obviously wrong that the "correct" answer is glaringly apparent even to a learner who hasn't read the content. This doesn't measure learning; it measures common sense.
To validate distractor quality, I apply my "Test-Taker’s Paradox":
- Plausibility: Could a high-performing employee plausibly think this distractor is correct? If the answer is no, rewrite it. Avoid "None of the Above": AI loves this, and it is almost always a sign of a lazy question. The "One Sentence Rule": If a distractor is four words long and the correct answer is a complex sentence, the learner will guess the correct answer just by looking at the structure. Keep the length consistent. Removal of Absolute Language: AI tends to overuse words like "always" or "never." These act as triggers for test-takers to rule out distractors immediately. Look for them and prune them.
SME Review: Targeted and Efficient
Stop sending your SMEs a 20-page document and asking them, "Does this look right?" You are wasting their time and guaranteeing they will just skim it and say "Looks good to me."
Your job as the L&D practitioner is to do the heavy lifting *before* it hits their desk. Your review checklist for SMEs should look like this:
Fact Accuracy: "Is the answer provided truly the only correct answer according to our current process?" Ambiguity Check: "Are there any terms in the question that could be interpreted in two different ways by someone in the field?" Contextual Relevance: "Does this reflect how we actually talk about this topic in the workplace?"By framing the review in terms of accuracy and practical application rather than "how does this flow," you get significantly higher-quality feedback. If an SME flags an item, ask: "What was the specific scenario where this would be misinterpreted?" That feedback goes straight into your "Gotchas" doc to improve your future prompting.
The Art of Removing Ambiguity
I rewrite every sentence I draft at least five times. Why? Because language is messy. AI-generated text often suffers from a corporate-sounding, vague tone that sounds professional but means nothing. In a microlearning assessment, vague language is a ticking time bomb.
Consider this AI-generated question: "How should you handle a customer complaint regarding shipping delays?"
That is a terrible question. What type of shipping? What is the company policy on shipping delays? Is there a specific compensation tier? A learner could easily answer this based on their own personal experience rather than company policy.
The Fix: "Per the 2024 Logistics Handbook (Section 4.2), which of the following is the required first step when a customer initiates a ticket regarding a shipping delay exceeding 48 hours?"
By defining the scope and linking it to a source, you transform a vague, conversational question into a precise, valid measurement of knowledge.
Final Thoughts: The Human as the Gatekeeper
The AI is your intern. It is fast, eager, and occasionally makes things up because it wants to please you. You are the senior manager. Your value in the L&D workflow is no longer in the *creation* of content, but in the *validation* of it.
If you want to move from "content creator" to "strategic learning partner," stop trusting your AI drafts implicitly. Test them, break them, and track your mistakes. Your learners deserve assessments that actually test their understanding, not their ability to spot a hallucinated fact or a poorly written distractor.
Keep your "Gotchas" doc updated, keep your SME reviews targeted, and for the love of all that is holy, stop accepting "looks good to me" as a QA standard. We are in the business of building competence, not just filling screens.