Beyond the Prompt: A QA Checklist for AI-Generated eLearning
The shift from creator to editor
AI has fundamentally changed the speed of course development. What used to take weeks of drafting now takes minutes of prompting. However, the speed of production often creates a bottleneck in the review cycle. When a course is built primarily by AI, the role of the instructional designer shifts from creator to high-stakes editor.
AI tools are excellent at structure but notoriously poor at nuance. They prioritize plausibility over accuracy. If your team is using AI to generate content, scenarios, or quiz questions, your QA process must evolve to catch the specific types of errors these models introduce.
To maintain quality, your review cycle should focus on these four critical areas.
1. The Hallucination Hunt
AI models are word-prediction engines, not databases of facts. They can confidently invent compliance regulations, software features, or company policies that do not exist. This is the most dangerous risk in AI-generated eLearning.
Reviewers must verify every statistic, legal citation, and technical procedure. Never assume that because a paragraph looks professional, it is accurate. Cross-reference AI-generated content against your original source material or SME documentation. If the AI synthesized three documents into one summary, check that it didn't blend conflicting facts into a single, incorrect statement.
2. Tone and Cultural Context
AI tends to default to a "generic corporate" voice. It is often overly formal, uses passive voice, or relies on tired clichés like "In today's fast-paced environment." While this isn't always a dealbreaker, it can alienate learners who are used to a specific brand voice or internal culture.
During your review, look for "robotic" phrasing. Does the content sound like your company? Does it use the specific terminology your employees use on the job? AI often misses the subtle linguistic markers that make a course feel authentic to a specific workforce.
3. Instructional Depth and "The So-What"
AI is remarkably good at summarizing information but often struggles with application. It loves to create lists of five tips, but it rarely explains the "why" behind a complex decision. This leads to "shallow learning"—content that covers the surface but fails to build actual skill.
Evaluate the Bloom’s Taxonomy level of the AI content. If the goal is for learners to analyze or evaluate, but the AI provided a simple multiple-choice identification quiz, the course will fail to meet its learning objectives. You must manually inject complexity, nuance, and realistic consequences into scenarios that AI tends to resolve too cleanly.
4. Accessibility and Technical Integrity
Just because an AI tool generated the content doesn't mean it correctly formatted the output for accessibility. AI-generated images often lack descriptive alt-text, and AI-written scripts may not translate well to screen readers if the structure is logic-heavy or utilizes complex tables.
Check the navigation flow. AI-generated outlines can sometimes lead to circular logic or missing links between modules. A manual pass is required to ensure that the user experience remains cohesive and that the technical infrastructure of the course hasn't been compromised by automated shortcuts.
The Human-in-the-Loop Requirement
QA is no longer just about catching typos; it is about ensuring instructional integrity in an era of automated assembly. AI can provide the first 80% of a course, but the final 20%—the part that ensures the training actually works—requires a disciplined, human-led review process.
A structured review platform can help bridge this gap by allowing SMEs and stakeholders to flag AI-generated inaccuracies in real-time. Without a rigorous QA framework, you aren't scaling your training; you are only scaling your mistakes.
