Run two quick checks: ask yourself whether you could design a session activity that directly targets each objective, and ask the AI to generate an end-of-lesson check-in question for each one. If either check fails, the objective needs revision before you build around it.
AI Objectives Are a Starting Point, Not a Final Answer
When you ask Claude or ChatGPT to write learning objectives, it will produce something that sounds polished and professional — which can make it tempting to just copy-paste and move on. The problem is that AI writes from the information you give it, and you often know things about your students, your teaching style, and your intended outcome that you didn’t put in the prompt.
Think of AI-written objectives like a template your tailor made from standard measurements. It might fit most people reasonably well, but it won’t fit you perfectly until someone makes the adjustments. The AI gives you a strong first draft. Your job is the fitting.
The Two-Step Verification Process
The first check is the activity test. For each objective, ask yourself: “What would I have students actually do in the session to practice or demonstrate this?” If you can immediately picture a concrete activity — a prompt exercise, a group discussion, a quick demo — the objective is specific enough to build around. If you’re struggling to think of anything, the objective is probably still too abstract.
The second check is the AI-generated check-in question. Paste the objective back into Claude and say: “Write one question I could ask students at the end of a session to check whether they achieved this objective.” If the resulting question is clear and answerable in one or two sentences, the objective is well-formed. If the question is vague, repetitive, or basically restates the objective without adding any clarity, the objective needs tightening.
You can also do a simple read-aloud test. Say the objective out loud as if you’re opening a live session: “By the end of today, you’ll be able to…” If finishing that sentence feels natural and convincing, you’ve got a keeper. If it feels awkward, the objective probably contains jargon or is trying to do too much at once.
What This Means for Educators
For coaches and consultants running live cohorts, this verification step is most valuable before your first session with a new group. Objectives that haven’t been tested against the activity test tend to produce sessions that feel slightly off — too vague, too ambitious, or disconnected from what students actually need. Catching that before you’re on Zoom saves you the awkward mid-session pivot.
It’s also worth keeping a simple running log of which objectives your students consistently achieve and which ones they struggle with. Over time, that log becomes your most valuable curriculum data — and AI can help you revise the underperforming objectives based on what you observed in the room.
The Simple Rule
Never use an AI-written objective without running it through the activity test and the check-in question test. Both take under two minutes and catch most of the problems before they become visible to students. An objective that passes both checks is one you can build a session around with confidence.