The Hidden Cost of AI Course Generation: From Content Bloat to Performance Support

When generative AI tools first launched, converting a 40-page PDF into an interactive module felt like a miracle. It looked like an instant win for efficiency.

Then I looked closer. The output had factual errors, strange gaps, and misinterpretations. At first, reading the original document and setting up detailed prompts felt like a reasonable trade-off. It still seemed faster than building a course from scratch. But when you have to produce five or ten courses in a row, that verification loop catches up with you. Between reading the source material, crafting prompts, and checking every slide for discrepancies, the supposed time savings disappear.

Generative AI excels at spitting out words and building slides. It completely fails at judging whether that content delivers actual value.

This realization exposes a clear split in instructional design: generating content bloat versus building performance support.

The Trap of Content Bloat

Content bloat happens when a designer rushes to an authoring tool before asking basic questions. AI tools make this trap dangerous because they make text generation effortless.

When you feed a document into an AI generator, the tool tries to cover everything. It produces pages of summaries, superficial flashcards, and basic quizzes. This content robs learners of their limited cognitive bandwidth. Learners quickly realize they are reading in circles. Nothing in the module helps them do their job better.

It might look fine on slide one. But after three or four modules of repetitive summaries, your training loses all credibility with the workforce.

The Real Work: Performance Support

Performance support requires you to think about the learner long before opening an authoring tool or typing a prompt.  This means stopping yourself and running the request through a repeatable diagnostic sequence:

1. Identify the Real Trigger Is this request solving an urgent operational mistake, or is it just an administrative compliance checkbox? If it is just a checkbox, generating an elaborate module will waste everyone's time.

2. Diagnose the Root Cause Are learners failing because they lack information, or is the internal workflow broken? If the software is buggy or the process is flawed, no amount of training content will fix the performance gap.

3. Match the Right Solution Do learners need to memorize this, or do they just need an accessible job aid on the job? Most tasks only require an accurate checklist, not a multi-screen click-through course.

4. Protect Cognitive Bandwidth What happens when the learner opens this asset? Will this give them immediate clarity to do the task, or will it drain their cognitive bandwidth and leave them reading in circles?

5. Define the Measurable Behavior What specific action must the learner do differently tomorrow? If you cannot point to a clear physical or digital action they must take, the content has no clear purpose.

   
   

Keeping the Skill Alive

Let us be honest about the day-to-day work. When you are juggling three, four, or five projects at once, you will not always have the opportunity to make all of these questions fully actionable on every single request. Tight deadlines and rigid stakeholders are real obstacles.

Even if a project forces you into course-builder mode, simply asking these questions in your head keeps the muscle sharp. Mental practice keeps the skill alive even when a deadline prevents you from putting it into full practice.

Keeping this lens active on every project helps you run practical performance audits and gives your learners a real chance at succeeding on the job.

If your team is evaluated on how quickly they export SCORM packages, this team is in danger of being automated and phased out. If a team is valued for creating actionable performance support, their skills are invaluable.