Yes — describe the decision your students repeatedly face in your course, give Claude the key criteria and outcomes, and ask it to produce a decision tree structure. The result is a visual problem-solving tool that helps students make confident choices without needing to ask you every time.
Why Decision Trees Are Powerful Learning Tools
One of the most valuable things a course can give a student is the ability to make decisions independently, even after the course ends. A decision tree captures your expert judgment in a form students can follow on their own — it’s essentially your decision-making process, written down as a series of yes/no questions that lead to a clear outcome.
Think of it like the troubleshooting guide in the back of an appliance manual. “Is the light on? → Yes → Is the door closed? → No → Close the door.” Each branch narrows the problem until the student reaches a clear action. Your course decision tree does the same — it walks students through your logic step by step until they arrive at the right choice for their situation.
How to Build One with AI
Start by identifying a decision your students commonly struggle with. It might be “Which AI tool should I use for this task?” or “Should I run this as a live session or a recorded module?” or “Is my course ready to launch?” — any repeating decision where context matters and there’s no single universal answer.
Prompt Claude: “Create a decision tree for coaches and consultants trying to decide [specific decision]. The key factors they should consider are [list 3-5 criteria]. Each branch should end with a clear, actionable recommendation. Write the tree as a series of yes/no questions in a logical order.”
Claude will produce a text-based decision tree you can then have formatted visually — either in a tool like Canva, Miro, or a simple Word table. Ask Claude to also produce a plain-text “how to use this decision tree” paragraph for students who find the visual format confusing.
For more complex decisions, build in an escape hatch: a branch that says “Still unsure? Bring this to the next live Q&A with your specific situation.” This keeps the tree manageable while directing genuinely complex cases back to your live support.
What This Means for Educators
Decision trees reduce the number of “what should I do?” questions in your community and on your calls — because students have a structured way to work through the answer themselves. They also build student confidence: when a student follows the tree and gets a good result, they trust their own judgment more the next time.
The Simple Rule
Identify the one decision your students ask you about most often, then build a decision tree for it. That single tool will save you dozens of repeated answers and give your students a sense of self-sufficiency that makes them feel your course delivered real capability, not just information.