AI is now the new normal in many industries, and eLearning is no exception. Among its most powerful tools is generative AI, a type of artificial intelligence that creates entirely new content from an initial input or prompt.

Here’s the difference: traditional AI works by analyzing existing data, making predictions, or answering questions based on what’s already in a database. For example, if you ask, “What are the best practices for remote onboarding?” traditional AI might give you a list of tips or pull information from existing resources.

Generative AI takes it much further. With just an initial input—like “Create a remote onboarding program for new hires”—it can produce brand-new, customized content. This might include a full course structure, lesson outlines, videos, quizzes, and visuals, all tailored to the input you’ve provided.

In the eLearning context, it’s like having a virtual assistant that doesn’t just find answers but builds entire training programs from scratch.

This ability to generate custom content quickly and efficiently has made generative AI a game-changer in eLearning. Corporate eLearning teams now use generative AI-based tools to develop courses faster than ever. It can handle repetitive tasks like creating graphics, drafting scripts, or generating assessments, freeing up instructional designers to focus on higher-level strategies.

However, speed alone doesn’t guarantee success. While generative AI offers incredible potential, it also comes with risks. Misusing it can lead to low-quality courses, disengaged learners, and even costly compliance issues. To maximize its benefits and avoid pitfalls, it’s essential to know what not to do. Below are the critical “don’ts” every eLearning team should keep in mind when using generative AI to develop courses.

1. Don't Solely Depend on AI for Content Accuracy 

Generative AI can create large amounts of instructional content quickly, but it doesn’t guarantee accuracy or alignment with your business or industry standards. For example, if you use AI to generate a compliance course, it might miss recent regulatory updates or misinterpret complex laws, resulting in outdated or incorrect information. This can lead to legal risks, compliance issues, or misinformation being shared within your organization.

What to Do Instead: Always involve subject matter experts (SMEs) in the review process. After AI generates the content, have SMEs verify the accuracy and ensure it reflects current regulations, business needs, and industry standards. This extra layer of oversight ensures the final course is not only fast to create but also accurate, relevant, and reliable.

Also read: Mistakes to Avoid When Using AI to Create eLearning Courses

2. Don’t Let AI Take the Driver’s Seat

Imagine building a house without a blueprint—you might end up with walls and a roof, but it won’t be livable. The same goes for using AI in training programs. Generative AI can churn out modules, videos, and quizzes in minutes, but without a solid instructional design framework, the content might lack direction, relevance, and impact. For instance, AI might create an entire leadership course, but if it doesn’t address the unique challenges your managers face, it’s just noise.

What to Do Instead: Begin with a blueprint—your instructional design framework. Identify the learners’ needs, business goals, and learning objectives first. Then, use AI as a tool to bring those plans to life. Think of AI as your skilled builder, but you’re the architect guiding the project. Always review and tweak the AI’s output to ensure it stays aligned with your vision and delivers meaningful learning outcomes.

3. Don’t Use AI to Replace SMEs (Subject Matter Experts)

Think of AI as a fast learner, but not a seasoned professional. It can generate content in seconds, but it doesn’t have the years of experience, hands-on knowledge, or nuanced understanding that subject matter experts (SMEs) bring.

When you rely solely on AI, you risk creating courses that look complete but lack critical depth. For instance, imagine using AI to develop a safety training module without consulting engineers or safety officers—it might skip crucial safety protocols or fail to address real-world scenarios, putting your team at risk.

What to Do Instead: Use AI as a helper, not the sole creator. Let it handle the heavy lifting for repetitive tasks like drafting or formatting, but always bring SMEs into the process to validate and enrich the content. SMEs ensure that the training is accurate, practical, and grounded in real-world expertise, giving learners the confidence to apply their knowledge effectively. Together, AI and SMEs make the perfect team.

Recommended read: The Future of Instructional Design in the AI Era

4. Don’t Overuse Stock-Like AI Visuals

Generative AI tools can recommend or create photos, avatars, and graphics in seconds. While some visuals might look fantastic, others need more care, strategy, and thought to truly connect with your learners.

Relying too heavily on AI-generated visuals without reviewing them can lead to a bland, stock-like feel—or worse, visuals that fail to represent the diversity and culture of your workforce. For example, using generic avatars that don’t reflect the unique makeup of your team can make the training feel disconnected and impersonal.

What to Do Instead: Treat visuals as a strategic part of your training. Use AI-generated images as a starting point, but review and refine them to align with your organization’s values and learner demographics. For instance, ensure avatars reflect the diversity of your team, including gender, ethnicity, and abilities. Collaborate with designers or use editing tools to tweak visuals so they resonate with your audience and enhance the learning experience. Thoughtful visuals go a long way in making your training inclusive and relatable.

5. Don’t Depend on AI for Tone and Empathy

AI might be a master of speed, but it doesn’t have a heart. When it comes to sensitive topics like workplace harassment, mental health, or diversity, generative AI can produce text that feels cold, robotic, or out of touch.

A course on diversity, for example, might be factually accurate but fail to acknowledge the emotional weight of the subject or the lived experiences of employees, leaving learners feeling disconnected or misunderstood.

6. Don’t Skip Quality Control—Speed Isn’t Everything

AI might create content in record time, but rushing to publish without thorough quality checks can lead to embarrassing errors or inconsistencies. Imagine launching a course only to find that the AI-generated quiz contains poorly worded questions or irrelevant answers. It’s not just frustrating for learners—it undermines the credibility of your entire training program.

What to Do Instead: Slow down to speed up. Build a robust quality assurance process that includes multiple layers of review. Have team members review the content for accuracy, clarity, and alignment with your objectives. Conduct peer reviews to catch overlooked errors, and run pilot tests with a sample group of learners to spot gaps or confusing elements. Taking the time to refine AI-generated content ensures your training is polished, professional, and ready to deliver results.

7. Don’t Overwhelm Learners with Bulk Content

Just because generative AI can create a mountain of content doesn’t mean your learners can—or should—climb it. Flooding them with too many modules, pages, or details can leave them overwhelmed and disengaged.

Imagine creating a 15-module training program stuffed with AI-generated lessons. While it might seem comprehensive, employees could quickly lose focus, resulting in lower completion rates and poor knowledge retention.

What to Do Instead: Ask yourself, "What do learners really need to succeed?" Focus on the essentials and break content into bite-sized, actionable modules. Use microlearning to deliver small, impactful lessons that fit into busy schedules. Always prioritize clarity and relevance over volume, ensuring every piece of content has a purpose. Thoughtfully structured courses will keep learners engaged and help them retain more, turning information overload into meaningful progress.