A Practical Checklist for Evaluating AI-Generated Content

So you’ve drafted a course using AI—now what?

It’s amazing what’s possible with generative AI tools these days. Let’s say you need to create a full e-learning course for workplace training needs. So you input information in your AI tool about what should be included, set a few parameters, and voila! In the time it took you to refill your coffee, it generated a full educational course that’s ready to deliver to employees.

But before you go hitting “publish,” let’s take a step back. There’s no question that artificial intelligence can save weeks or even months on content creation. It can also generate some truly impressive first drafts, complete with structure, headings, objectives, images, and knowledge assessments.

That strong start doesn’t mean you should skip to publishing. A fact check step is critical, because AI may unintentionally reinforce certain biases, hallucinate information, and pull out-of-date information. It takes a human touch to make sure the content meets your learners’ needs and your organization’s standards. That’s where you come in.

In this blog post, we’ll walk you through a simple checklist for reviewing and improving AI-generated text and offer tips for ensuring your training is accurate, inclusive, and aligned with learners’ needs.

Step 1: Fact-check and validate all content

Consider a workplace course on cybersecurity. In the section your AI tool creates on password security, it states something like, “Be sure to change your password every 30 days to protect against online threats.” It might sound good, but the advice is outdated and incomplete. Most experts, including your internal IT team, recommend that employees use multi-factor authentication for the best possible protection.

And it doesn’t stop there. Even the very best generative AI models can produce convincing fake citations, present untrue statements, or include obsolete information. To make sure your course stands up to scrutiny, conduct a thorough fact-check.

Your fact-checking checklist:

• Check output against internal knowledge. Make sure the content matches up with your source materials and accurately represents your company’s processes, policies, and terminology.
• Cross-reference facts. Validate facts using reputable, up-to-date sources such as academic publications, professional journals, and trusted news articles.
• Double-check citations. Just because a citation looks real doesn’t mean it is. Look them up to confirm the existence and legitimacy of any listed references.
• Use your own research. Don’t rely solely on the links or citations AI provides. Supplement AI sources with research of your own via trusted search engines, reliable online sources, and professional databases.

AI-generated “facts” are a starting point, not the final word. Even if it sounds good, it’s up to you to verify that it’s correct, current, and true to your training data and needs.

Step 2: Watch for bias, gaps, and generic advice

Imagine you’re creating a training module on workplace communication. You ask your AI tool to give you a draft, and in one section, it says, “maintaining eye contact is key to active listening.” Sounds reasonable, right? But what about learners who are neurodivergent, seeing impaired, or from a culture where eye contact is perceived as aggressive or intrusive?

These are the types of biases that can creep in if you don’t critically evaluate AI-generated output carefully. Use the checklist below to spot and fix the most common issues.

What to watch for (and how to improve it):

• Biases. Look for common stereotypes or narrow perspectives; for example, assuming that leaders are always male or extroverted. Use inclusive language and scenarios that don’t make broad generalizations or reduce complex individuals to tropes or types.
• Missing context. When you see general advice like “improve communication skills,” add a specific example and/or detailed explanation, e.g., “Use active listening techniques and hold regular one-on-ones.”
• Gaps in role-relevance. Remember that the marketing team has different needs from the sales team. Take one-size-fits-all training and tailor it to individual roles to make it feel personally relevant.
• Lack of cultural awareness. Watch for scenarios or language that are too focused on your culture, region, or experiences. Reframe them for a global audience and review them for accessibility and cultural sensitivity.

Even if issues like these are unintended, they can still lead to real consequences, especially in professional settings where relevance and inclusion matter.

Step 3: Check for clarity, flow, and learner experience

Put yourself in the learner’s shoes. You open up a new employee training course on cybersecurity basics, and it launches right into a bunch of terms and definitions, like “Advanced Persistent Threat” and “Cloud Computing,” without any context or explanation of why you should care. There’s no welcome or introduction, no directions for navigation, no learning objectives, and no clear structure—only jargon.

No one could blame the learner for feeling frustrated.Even if AI generated information is correct, if it’s hard to navigate or understand, learning retention will suffer.

Here’s how to ensure your AI content is clear, cohesive, and learner-centric:

• Choose the right AI tool for the job. Pulling from multiple generic AI tools may be easy at first, but it may make work harder in the longer run. Tools trained on best practices for training, like Articulate 360 with AI have learning science built in for a better authoring and learner experience.
• Choose an approachable tone. No one wants to read a technical manual cover to cover. Edit the content, or prompt AI Assistant, to keep the tone professional yet relatable. Simplify complex or formal language, and add real-world examples relevant to learners.
• Follow a logical flow. Prompt your course draft around your learning objectives, and make sure each section builds on the previous one.
• Avoid information overload. Break up longer sections into smaller chunks, and use headings, lists, bullets, and numbers to make the text easily scannable.
• Make it interactive. Not all AI tools integrate interactive elements. Make sure you introduce knowledge checks, self-reflections, and branching scenarios to keep learners engaged.

Don’t just assume AI will create a human tone and logical flow. Take the time to mold and shape the content into something learners actually can and want to engage with.

AI can build it, but you can make it great

AI works best when it’s used as a collaborative tool. It offers you a great jumping off point, but then it’s up to you to take the baton and carry it across the finish line. Ultimately, it’s your responsibility to critically evaluate the content.

That means taking the time to verify facts and sources, edit and revise sections that are awkward or overly formal, and create an overall user experience that is logical, easy to understand, and memorable. It also means eliminating potential biases and insensitivities that not only make you look bad but can also cause harm to others.

When you combine the speed and power of AI with your uniquely human insights and understanding, you’ll create top-notch learning experiences that lead to real business results.

Want to get better at creating online courses with AI without losing your voice or your standards? Check out our blog post, How to Easily Create Online Training With AI.