After eleven years in this industry—moving from the trenches of LMS administration to the meticulous world of QA leadership—I have learned one fundamental truth: AI is a fantastic intern, but a terrible subject matter expert. It is brilliant at formatting, decent at brainstorming, and dangerously prone to hallucinating facts and mirroring systemic biases.
If you are currently integrating AI into your instructional design workflow, you aren't just an ID anymore; you are a content moderator. Over the last 18 months of piloting these tools, my "gotchas" documentation has grown significantly. I’ve seen AI generate training that defaults to male-dominated leadership examples, assumes Western cultural norms as the "standard," and fails to acknowledge neurodivergent learning needs. If you’re pushing "generate" and hitting "publish" without a rigorous bias language check, you are putting your learners—and your organization—at risk.
1. The Validation Mindset: Shifting from Author to Editor
In L&D, we often talk about "authoring" content. When AI enters the mix, that vocabulary needs to change. You are now a validator. Validation means you are not looking for whether the content "sounds good"—because AI is engineered to sound confident, even when it’s wrong. You are looking for proof of accuracy, tone alignment, and inclusivity.
When I review an AI draft, I stop looking at the flow and start looking for the "seams." Where did the AI make a leap in logic? What assumptions did it make about the learner's background? If you receive an AI-generated draft and your internal response is "looks good to me," you haven't done your job. "Looks good" is not a QA process; it’s a failure to scrutinize.
2. Risk-Based QA: Not All Content is Created Equal
We don’t have time to perform a forensic audit on every micro-learning module. This is where a risk-based approach to sensitivity review saves your sanity. I categorize every piece of content before I even open the file.
Risk Level Content Type QA Focus High Risk DEI, Leadership, Compliance, Performance Management Extreme scrutiny on inclusive writing, power dynamics, and potential legal/HR implications. Medium Risk Technical Skills, Software Training, Sales Scripts Focus on accessibility, accessibility and inclusion, and technical accuracy. Low Risk Onboarding logistics, scheduling, event reminders Standard grammar and style guide consistency.For high-risk content, I perform a "break-the-content" https://dlf-ne.org/ai-drafts-are-wordy-why-your-copy-paste-workflow-is-hurting-learner-engagement/ test. I intentionally try to find a way to interpret a sentence that would offend or alienate a specific demographic. If I can find it, a learner will find it too.
3. Identifying Hidden Bias in Your Drafts
Bias isn't always overt. In fact, the most dangerous bias in AI-generated training is the "invisible default." Here is what you should be looking for during your bias language check:
- The "Generic" Masculine: Does the AI use "he" or "him" when describing a manager, or "she" when describing an administrative assistant? This reinforces dated stereotypes that have no place in modern training. Western-Centric Examples: Does the training assume everyone lives in a suburban environment, owns a home, or shares the same cultural holidays? Inclusive writing requires us to broaden our examples to ensure they resonate with a global, diverse workforce. The "One-Size-Fits-All" Assessment: I often take assessment questions generated by AI and try to "break" them. Does the question rely on cultural idioms that a non-native speaker might not understand? Is the distracter option logically sound, or is it just a "gotcha" designed to trick the learner? If the assessment creates an unfair barrier, it fails on accessibility. Pathologizing Language: When covering soft skills, does the AI frame certain behaviors as "wrong" rather than "ineffective"? This can often come across as condescending or insensitive to neurodivergent learners.
4. The Truth About Fact-Checking and Source Tracking
The cardinal sin of using AI is accepting its citations at face value. AI models are probabilistic, not factual. They are designed to predict the next likely word, not to pull from a verified database of truth.
If your AI-generated script cites a "study by the Harvard Business Review," stop. Use a browser plugin or a search engine to verify the actual source. I keep a running log of "AI Hallucination Hotspots" in my drafts folder. These are usually:
If you cannot find the source, the content does not go into the storyboard. Period.
5. SME Review: Making it Targeted and Efficient
One of the biggest productivity killers in L&D is sending a 50-page storyboard to an SME with a vague request like "Can you review this?" Your SMEs are busy. If you send them a mess, they will give you a messy review.
Because you’ve already done your sensitivity review and https://fire2020.org/risk-based-qa-for-ai-training-content-how-do-you-decide-what-to-check/ bias language check, your review request should be surgical. Instead of a blank check, provide targeted questions:
- "I’ve used this scenario to illustrate empathy in feedback—does this align with our current company culture?" "I’ve adjusted the language here to be more inclusive. Does this accurately reflect our current stance on X policy?" "I've checked the facts against [Source A] and [Source B]. Please confirm if these remain our standard operating procedures."
By framing the review, you force the SME to engage with the *substance* of your work, rather than just pointing out typos or, worse, ignoring it entirely.
Final Thoughts: The Human Element
Ultimately, AI is just a mirror of the data it was trained on—and that data is historically skewed. Our job as L&D professionals is to scrub that mirror. We are the ones responsible for the learner experience, and "the AI wrote it" is not an excuse for insensitive or biased content.
My advice? Don't stop at the first draft. Don't be afraid to rewrite the same sentence five times until it’s perfectly clear and free of coded language. Take pride in the fact that your training is accessible, inclusive, and, most importantly, honest. The machines might be doing the typing, but you’re the one doing the thinking. Keep it that way.