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AI Quality Assurance: Catching Hallucinations, Bias, and Brand Drift
Description
AI can write training content that looks flawless, sounds professional, and still quietly mislead your learners. That’s the problem we tackle today, along with a practical fix you can use immediately: a fast QA scan that keeps AI speed while protecting trust, accuracy, and credibility.
In this episode, Jackie walks through three failure modes that show up again and again in AI-generated eLearning and microlearning drafts: accuracy issues like invented details or wrong policy claims, bias that slips into scenarios through assumptions or stereotypes, and brand drift where the tone turns generic, overly corporate, or inconsistent with your organization’s voice. If you design learning for compliance, safety, HR, legal, or any high-stakes topic, these risks aren’t theoretical; they can impact people’s well-being, employment, and your organization’s reputation.
You’ll leave with a simple, repeatable method: run three quick passes on any AI draft facts, fairness, and voice. I share the exact questions to ask, the red flags to watch for, and an easy checklist to keep next to your keyboard.
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