You tell AI to anchor every exercise to something the student is already doing in their real business — not to a hypothetical scenario. That one instruction shifts the output from textbook to immediately useful.
The Theory Trap in Course Design
Theoretical exercises explain a concept but don’t require students to apply it to anything real. They work in academic settings where the goal is to demonstrate understanding. But educators, coaches, and consultants don’t enrol in courses to understand things — they enrol to change something in their business. An exercise that asks them to respond to a made-up scenario misses the point. An exercise that asks them to apply the same skill to their actual client list, their real course, or their live community hits the mark.
The gap between theory and practice is where students stop doing the work. They read the theory, nod, and move on. They engage with the practice, get stuck, and ask for help — which is exactly what you want in a cohort-based program.
How to Prompt AI for Practical Exercises
The phrase that changes everything is: “using their own [business/course/students/content].” Try: “Write one exercise for educators learning how to use AI for content creation. The exercise should require them to use their own existing course topic, not a made-up example. They should produce one piece of real content they can actually use after the exercise.” When AI knows the output needs to be real and usable, it designs toward that. It stops generating scenarios and starts generating processes.
Claude handles this well because you can push it further: “Make sure the exercise produces something the student can publish or share this week.” That deadline — this week, not someday — is the final test of whether an exercise is practical. ChatGPT responds similarly when you add that kind of concrete constraint.
What This Means for Educators
Practical exercises are what make students say “I got more done in this one session than I did in three months on my own.” That sentence is what fills your next cohort. When every exercise in your course produces something a student can use — a real email, a real lesson plan, a real community post — your course becomes a production environment, not just a learning environment. The transformation is the product.
You can run a quick audit of your existing exercises using AI too. Paste them in and ask: “Which of these exercises require students to use real content from their own business, and which use hypothetical examples? Flag the theoretical ones and suggest how to make each one practical.” That audit alone can significantly improve your course in one afternoon.
The Simple Rule
Add “using their own real [thing]” to any exercise prompt you give AI. That phrase is the difference between an exercise that gets skipped and one that produces something your student will reference long after the cohort ends.