Creating a Course Has Never Been This Fast
In the era of AI, we all know that what used to take weeks now takes hours, as we are just a few clicks away from a script, interaction, or assessment. Inside iSpring Suite Max, that entire early production process can happen in a single afternoon. That’s a real shift. You no longer need advanced technical skills or a full development sprint just to get a professional-looking course off the ground. If you know your subject, you can start building the same day.
Most of the friction that used to kill momentum early on, the blank slide deck, the hours spent writing learning objectives, the back-and-forth over quiz formats, is now gone because AI can easily handle the drafts for you.
But as more of these courses appear, a pattern is getting harder to ignore. While they look logical, professional, and well-organized, when learners actually go through the experience, something feels missing.
The Problem Isn’t the Tool. It’s How Designers Are Using It.
A lot of instructional designers are leaning on AI too heavily right now, not just to speed up production, but to replace the thinking entirely. They’re using it to generate everything from the structure and content to the assessments and examples. As a result, courses look and feel like they came out of a textbook: technically correct, neatly structured, and totally disconnected from how learners in that organization actually work. Learners cannot recognize themselves in the material and, most importantly, cannot connect with it. That’s not an AI problem. It’s an instructional design problem.
Don’t get me wrong, tools like iSpring AI are genuinely helpful, and you should use them to generate a draft outline, get quiz question suggestions, or refine an explanation that isn’t landing. That’s where AI earns its place in your workflow. But as an instructional designer, you should not prompt your way to a finished course. Your job is to bring the strategy, the structure, and the judgment that AI can’t provide.
Generating Content and Designing Learning Are Two Different Jobs
AI organizes information fast, explains concepts clearly, and mirrors the structure of existing educational content with impressive speed. Pair that with a course authoring tool and those capabilities are available to anyone. The problem is that a working learning experience doesn’t come from organized information. It comes from sequence, pacing, deliberate practice, and knowing where confusion is likely to hit. Someone has to think through what learners need at each stage, how ideas should build on each other, and what the experience should actually accomplish by the end.
Those decisions don’t show up visually in the finished course, but they determine whether any learning happens at all.
A well-designed course isn’t lessons placed next to each other. While AI can produce something that looks like that structure, it can’t determine the instructional intent behind it. That part is still yours.
AI Defaults to Coverage, and Coverage Isn’t Enough
AI-generated courses tend to be full of content: subtopics, explanations, and examples. The problem is that content and progression are completely different things. A learner can sit through two hours of well-organized content and still be unable to apply any of it. They may recognize the terminology and understand the concepts in the abstract, but put them in a real situation and they’ll be stuck.
Strong instructional designers create courses that build ideas gradually, where practice reinforces understanding at the right moments and each activity connects to a specific outcome. As a result, learners develop actual capability, not just familiarity.
AI doesn’t design for that kind of progression on its own. Without someone directing the process intentionally, you get a course that’s packed with information and structurally hollow underneath.
What AI Can’t Give You: Real Examples from Real People
One of the main gaps in AI-generated courses is the lack of personality, personalization, and real-world examples that come from subject matter experts.
When you work with an SME, they give you the messy, specific, lived version of the topic: the customer call that went wrong, the compliance issue that actually happened in their department, the shortcut that everyone uses even though the manual says otherwise. That content isn’t just facts or theory. It’s material that reinforces learning, brings it to life, and promotes application and retention.
Learners can easily tell when examples are generic. Even when they’re clear and plausible, they don’t come from anywhere real and aren’t built around their actual work. The truth is that even the best prompts can’t fix that, because you need the human voice.
The Cracks Show Between Sections
The weakest part of most AI-generated courses isn’t the individual lessons. It’s what happens between them. Even when the content, examples, and assessments seem fine, as learners move from section to section, the instructional logic either weakens or disappears. Activities feel disconnected from the outcomes they’re supposed to support, and interactions appear because they are common in online courses, not because they serve a specific purpose. Assessments test recall in a course that claims to teach judgment or decision-making, simply because AI tools are either unfamiliar with or don’t prioritize learning taxonomies or higher-order thinking.
That’s usually what makes these courses feel unsatisfying. The content isn’t wrong. It just doesn’t go anywhere.
Your Judgment Is the Part AI Can’t Replace
That said, AI can be very helpful in cutting production time. It can help you brainstorm, draft, reorganize content, generate questions, and work through repetitive tasks that used to eat up hours. For smaller teams and creators working under real deadlines, that’s genuinely useful. However, as a learning designer, you still have to decide what matters most in the learning experience. Where does a learner need more practice? Where should complexity increase? Which examples are too abstract to be useful? What content doesn’t serve the learning goal and should be cut entirely?
Those calls require more than subject matter knowledge. They require understanding how people learn and making deliberate choices about what the experience needs to do at each stage. Use AI to speed up your production, but don’t use it to skip your thinking.
Use AI to Support Your Process, Not Replace It
The most effective approach is using AI to support a process you’re already directing. When your learning goals are defined, your audience is clear, and you know what the course needs to accomplish, AI becomes much more useful. It can refine explanations, generate alternative examples, adapt content for different audiences, and speed up revisions.
The AI features in iSpring work well in that role. Drafting, question generation, and content refinement can all move your production faster without removing the thinking that determines whether the course actually works.
The quality of what AI produces depends on the quality of direction you give it. Without a clear instructional strategy, speed becomes a problem. You produce a course faster, but faster doesn’t mean better. Often it just means more polished content that learners can’t connect with.
Final Thought
AI has changed course production in ways that aren’t going away. Content is faster to build, easier to access, and more scalable than before. The problem is that too many instructional designers, and even SMEs with limited knowledge about instructional design and how people learn, are using that speed as a reason to stop thinking. The courses that come out of that approach are easy to spot: clean, organized, and forgettable. Learners complete them and move on without taking much with them.
Your value as an instructional designer isn’t in how fast you can produce a course. It’s in the strategy behind it, the real examples you pull from the people who know the work, and the judgment you apply at every stage.
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