Students often treat critique as something that happens after a draft is mostly finished: a late-stage polish rather than a genuine test of an idea. This exercise moves critique earlier. AI becomes a ready source of objections, and the students’ job is to decide which ones are generic noise and which ones expose a real weakness in the argument.
Ask students to bring a working thesis paragraph, interpretive claim, or partial draft. They paste that argument into an AI tool and ask it to produce the three strongest objections it can imagine.
Students then annotate the objections and sort them into three buckets:
The sorting process is the assignment. It makes students articulate why an objection fails instead of deciding to dismiss it because it’s hard to work with.
Students carry this work into their final revision, leading to sharper prose and a more defensible claim.
Prompt
Here is my argument: [paste your thesis paragraph or interpretive claim]. Generate the three strongest objections you can imagine to this argument. For each objection, be as specific as possible — refer to the actual claims I’m making, the evidence I’m relying on, or the logical moves I’m asking the reader to accept.
A counterargument is only strong if it lands on the actual claim being made. This exercise makes that concrete: defending an argument means specifying scope, evidence, and stakes — not just reasserting it with more confidence. Authoritative tone is not the same thing as analytical precision, and students can see that distinction clearly when working with AI-generated objections that sound reasonable but are detached from the actual text.
AI’s confidence can make even thin counterarguments feel weightier than they are.
Brief instructor modeling of the sorting process helps orient students before they work independently — especially in classes where students haven’t been asked to articulate why an objection fails rather than just dismiss it.