After 11 years in Learning and Development, I’ve seen every iteration of "the next big thing." From the death of Flash to the rise of experience platforms, the industry loves a shortcut. Right now, we’re obsessed https://dlf-ne.org/ai-drafts-are-wordy-why-your-copy-paste-workflow-is-hurting-learner-engagement/ with AI. And look, I get it. I’ve been piloting AI tools for 18 months, and the ability to draft a storyboard in seconds is a superpower. But here is the reality check: AI is an excellent intern, but a terrible subject matter expert. It’s confident, hallucination-prone, and fundamentally doesn't understand the nuance of your specific learner audience.
If you are treating AI output as "ready to launch," you are setting yourself up for a PR nightmare. I maintain a running "gotchas" document—a collection of the most absurd errors I’ve caught in AI-generated drafts. It includes everything from invented compliance laws to training that literally teaches the opposite of our company’s policy. To survive the era of AI-generated microlearning, you need a rigorous, surgical approach to QA. Here is how I do it without losing my mind.
1. Define Your Risk-Based QA Strategy
If you treat every piece of content with the same level of intensity, you’ll burn out in a week. I use a simple Risk-Based QA approach. Not all microlearning is created equal, and not all errors carry the same cost. You need to triage your content before you even open the file.
Before reviewing, ask yourself: Does this impact regulatory compliance, safety protocols, or legal liability? If the answer is yes, you are in the "High-Stakes" bucket. If it’s just a "Tip of the Day" on using Excel shortcuts, that’s "Low-Stakes."
Feature Low-Stakes (e.g., Soft Skills) High-Stakes (e.g., Compliance) Review Focus Tone and Engagement Accuracy and Legal Compliance Fact-Checking General sanity check Source-trace to primary documents SME Review Minimal / Optional Mandatory and documented QA Speed Fast (Spot check) Slow (Word-for-word)2. Stop Using "Looks Good to Me" (And Other Vague Feedback)
One of my biggest professional pet peeves is the "Looks good to me" feedback loop. It tells me nothing about whether the content is effective or accurate. If you are lead-testing a workflow, you need to be precise. If I see a colleague leave a comment like "needs more energy," I send it back. Tell me what needs more energy. Is the call to action weak? Is the voice too passive?
When you spot an error, document it. Why did the AI fail here? Did it misinterpret a complex policy? Did it use an outdated acronym? Tracking these "gotchas" in a spreadsheet helps you realize where your AI prompts are failing. If the AI keeps messing up your brand’s tone, you don’t need to rewrite the text manually—you need to sharpen your system prompt.
3. The "Broken Learner" Assessment Method
My favorite part of my job is breaking assessments. I don’t just read the questions to see if they make sense; I try to answer them incorrectly on purpose to see if the distractor is actually a valid answer. AI is notorious for creating bad distractors.
Here is my quick accuracy check for assessments:
- The "Technically Correct" Test: Can I argue that a distractor is true? If yes, the question is broken. The Context Vacuum: Does the question make sense if the learner skipped the video/text? If it does, the assessment is testing general knowledge, not the actual training content. The AI Loophole: Does the question rely on information not provided in the microlearning unit? AI often pulls from its training data, ignoring the specific context of your lesson.
If you can break your own assessment, the learner definitely will. This is where I spend the most time because this is where the most significant learning gaps are created.
4. Source Tracking: The Only Way to Fact-Check
Never take AI’s word for it. When an AI generates a snippet about a corporate procedure, don’t just ask, "Is this true?" Ask, "Where is the source for this?" and then force the AI to cite the document. If it can't, or if the citation looks suspicious, verify it against your internal knowledge base.
I use a "Two-Pass" editing workflow for AI content:
The Structural Pass: Does the microlearning follow the 3-minute limit? Is the flow logical? Is the formatting (bullet points, headers) readable on mobile? The Truth Pass: I take every claim made in the content and map it to a specific slide, policy document, or SOP. If a claim cannot be mapped, it gets deleted. Period.5. Making SME Review Efficient
Your Subject Matter Experts (SMEs) are busy. If you send them a 10-page document and ask them to "check it," they will either ignore it or give you superficial feedback. To get the most out of them, you need to restrict their scope.
When I send AI-generated drafts to an SME, I use a specific template:
- The Goal: "I need you to verify the accuracy of the statements in the 'Safety Procedures' section only." The Limit: "Please do not edit the tone or structure; I have already finalized the instructional flow. Only flag factual errors." The Grid: Provide a table with three columns: Statement, Is it Accurate? (Y/N), and Corrected Fact (if N).
By keeping the SME review targeted, you reduce the time they spend in the document and keep them from "fixing" things that aren't actually broken. You are the instructional designer; the SME is the content owner. Keep those roles distinct.
The Hard Truth About "Fast" QA
Is there a "fast" way to spot errors? Yes. It’s called being prepared. If your prompt engineering is sloppy, your QA will be painful. If you are clear about your constraints—specifically telling the AI, "Do not invent facts, use only the provided source text"—you will spend significantly less time fixing hallucinations.
I still rewrite one sentence five times to documenting ai in training design remove ambiguity, even after the AI has done its best work. Why? Because clear writing is the foundation of effective learning. If the AI’s sentence is "vague-but-technically-okay," it gets rewritten. Learners don't have the time to decipher our corporate fluff. They need direct, actionable, accurate information.
Don't be the L&D professional who lets a hallucination reach the learner because you wanted to save an hour of review time. You are the last line of defense between corporate nonsense and your learners. Treat the content like your reputation depends on it—because, in the long run, it does.