Let the Robot Get It Wrong
A make-or-buy assignment designed to turn ChatGPT from a cheating threat into a critical-thinking workout.
- Time to try 45 min prep
- Works for Tutorials · large classes in pairs
- Level 2nd year+
How many of us have spent the past two years playing whack-a-mole with AI-written assignments? Denise Wiseman and Deanna Foster suggest a different game. Instead of hiding the chatbot, give it the problem. Then give your students the job of checking its answer, like an auditor ticking and bashing a dodgy working paper.
What they did
Their framework, published in the Journal of Accounting Education, puts students "in the role of practicing cost accountants." In a classic make-or-buy decision scenario, students work with AI-generated solutions. They independently verify the calculations and assess the reasoning behind the recommendations.
Two techniques do the heavy lifting. The first is reperformance: redo the numbers yourself. The second is critical error review: work out where the AI's analysis went wrong, and why. The authors describe this "dual engagement" (AI-driven analysis alongside manual recalculation) as a way to build a fuller understanding of accounting principles and to "cultivate students' ability to critically evaluate AI outputs."
What they found
The assignment was run in undergraduate cost accounting courses. Thirty-nine students evaluated AI-generated analyses using various text-generating tools, mostly ChatGPT. In the survey, students reported strong engagement and rated their learning highly, with evaluation items scoring above 4.4 on a 5-point Likert scale.
The reality check: this is a small study (39 students), and the evidence comes from student survey ratings, not a controlled comparison of exam marks. Treat it as a well-designed recipe with happy early taste-testers rather than proof that it beats your current approach.
The clever part is that students have to explain why a number is right or wrong, which is the professional judgement we keep telling employers we're developing. The AI simply supplies a convincing first draft to argue with. It costs almost nothing to try, and it brings AI into your module without pretending it doesn't exist.
Try this in class next week
- Pick one decision topic you already teach (make-or-buy, special order, limiting factor) and ask a chatbot to solve it before class.
- Hunt for a juicy answer. Try a few prompts until you get a plausible-looking answer with a flaw, such as a sunk or unavoidable fixed cost treated as relevant. (Or use our printable one, below.)
- Reperform first. In pairs, students redo every calculation in Excel or on paper before they look at the AI's working.
- Red-pen review. Students annotate the AI answer: "calc error", "unsupported assumption", "missing qualitative factor".
- Sign-off memo. Finish with three lines: "Would I sign off on this recommendation? Why or why not?"
- Keep it formative. Grade the quality of the critique, not just whether students found the "right" number.
These suggestions are ours, not the authors'.
Printable: the flawed AI answer exercise + critique rubric →
After you try it, ask yourself…
- Which AI error did most pairs miss, and what does that tell you about where their understanding is thin?
- Did students argue about the qualitative factors, or only the numbers?
- Would you grade the critique next time, or keep it formative?
Source card
Wiseman, D., & Foster, D. (2025). Bridging AI with cost accounting education: A pedagogical framework using reperformance and critical error review. Journal of Accounting Education, 72, 100988. Read the paper (doi.org) · What we read: published abstract.
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