I’ve been in this industry for 11 years. I’ve seen the rise and fall of Flash, the transition to responsive design, and now, the absolute explosion of AI in the instructional design workflow. If there is one thing I’ve learned—and my running "gotchas" doc can confirm this—it’s that machines are brilliant at syntax but terrifyingly bad at context.
If you’re currently letting AI generate your training scripts or assessment questions without a rigorous, risk-based validation process, you are essentially flying a plane while the autopilot is drunk. AI is a fantastic intern, but it is a terrible subject matter expert. It will lie to your learners with the confidence of a CEO in a boardroom, and it will do it with perfect grammar.
So, how do we integrate AI without losing our minds or, more importantly, without violating compliance or confusing our users? The answer is risk-based validation.
What Does "Validation" Actually Mean in an AI-Assisted Workflow?
Validation isn't just "proofreading." It’s an intentional, systematic approach to verifying that the output matches the reality of your organization. When AI drafts content, it draws from a generalized internet-wide dataset. Your learners need company-specific, policy-driven, and task-accurate information. Validation is the gap-fill between "general knowledge" and "organizational truth."
To me, validation is three-fold:
- Accuracy: Is the information factually correct according to our internal, verified sources? Compliance: Does the content adhere to regulatory, legal, and company-wide policy standards? Pedagogy: Does the output actually facilitate learning, or is it just fluff disguised as a learning objective?
The Risk Assessment Matrix
You cannot (and should not) treat every piece of content the same way. If you spend five hours QAing a 30-second micro-learning on "How to use the coffee machine," you’re failing at your job. If you spend five minutes QAing a compliance module on "Data Privacy and GDPR," you should probably be fired. You need a content risk assessment strategy.
Risk Level Content Type QA Focus Validation Intensity Low Stakes Tone-setting videos, general soft-skills refreshers, office culture tips. Brand voice, brevity, grammatical consistency. Automated spell-check + light human review. Medium Stakes Process walk-throughs, basic systems training (non-critical). Accuracy of steps, logical flow, ease of use. SME review of workflow, manual verification of screen steps. High Stakes Compliance, safety, legal policies, financial reporting, medical protocols. Regulatory alignment, total fact-checking, source attribution. Deep-dive SME audit + "Breaker" testing for assessments.High-Stakes vs. Low-Stakes: Defining the Boundaries
Distinguishing between low and high stakes is the most important decision you’ll make in your project plan.
Low-stakes training covers content where the penalty for a slight inaccuracy is "someone corrected the typo later." If the AI hallucinates a synonym or adds a slightly weird metaphor for "empathy," the world keeps spinning. Focus your energy here on ensuring the output sounds like your company culture—remove the over-formal "corporate robot" voice that AI loves to default to.
High-stakes training is a different beast. Here, hallucinations are not just annoying; they are liabilities. If your AI writes a safety procedure and skips a step, that is a legal risk. For high-stakes content, the AI is merely a drafting tool—it should never be a source of truth. Every claim must be tied to a specific internal document, policy, or regulation.
Fact-Checking and Source Tracking: The "Gotcha" Defense
I have a document I’ve been keeping for years. It’s filled with "gotchas"—times we pushed training to production and found reddit.com mistakes later. AI has added a new chapter to this doc. I’ve seen AI invent acronyms that don't exist and confidently cite "industry standards" that were actually defunct five years ago.
Here is my golden rule: If the AI cannot point to the source, the content does not go in the module.
When you use AI, you must enforce a "source-first" workflow. If I’m asking an LLM to generate a draft about our new remote work policy, I don't just ask it to "write a module." I feed it the PDF of the policy and say, "Using only the provided text, summarize the remote work policy into a three-step guide." Then, I cross-reference. If it deviates by even a word, I rewrite the sentence. My policy is: if I have to rewrite a sentence five times to remove ambiguity, I do it. Ambiguity is where learners get confused, and confusion is where mistakes happen.
SME Review: Stop Wasting Their Time
One of the biggest mistakes I see in L&D teams is sending a generic "What do you think?" email to a Subject Matter Expert. SMEs are busy. If you send them a 50-page PDF of AI-generated junk, they will give you the dreaded "looks good to me" feedback—even if it’s wrong—just to clear their inbox.
To get targeted, efficient feedback, you have to be the buffer. Here is how I structure SME validation:
The Pre-Filter: I review the content first. I delete the fluff, fix the tone, and check for obvious hallucinations. The Specific Ask: Instead of "please review," I send: "I’ve drafted the policy section. Specifically, please verify that the eligibility criteria on slide 4 match the HR handbook page 12. You don’t need to worry about the tone, just the accuracy of those specific metrics." The Feedback Loop: Keep a record. When the SME validates a fact, document it. If a disaster happens later, you need an audit trail showing that the expert confirmed the accuracy.The "Learner-as-Breaker" Assessment Test
Finally, we have to talk about assessments. AI is notoriously bad at creating "distractor" answers that are fair. It tends to make the correct answer glaringly obvious or creates distractors that are technically correct but contextually irrelevant.
My advice? Test your assessments like a learner who is trying to break the course. Ask yourself:
- Can I select the right answer without reading the content? If I am a "smarter-than-average" learner, can I argue that one of the distractors is actually correct based on the wording? Does the question actually assess understanding, or does it just test short-term memory of a specific phrase from the screen?
If you can’t answer "no" to those, go back and rewrite. Don't be lazy with your assessment logic. Your learners aren't stupid, and if they feel the assessment is a "gotcha" game rather than a learning validation, they will disengage immediately.
Conclusion: The Human Remains the Lead
AI is a tool for speed, but the quality of your training remains a human responsibility. By using a risk-based validation framework, you stop trying to check everything with the same intensity and start focusing your brainpower where it matters most: ensuring that your learners are getting accurate, compliant, and actionable information.
Stop settling for "looks good to me." Stop accepting overconfident, vague AI outputs. Be the person who finds the mistake before the learner does. Your stakeholders will respect the accuracy, and your learners will thank you for the clarity.