Beyond the AI-Written Case Study: A Faculty Guide to Assessing Reasoning

A polished case analysis is not enough to show what a student understands. For faculty, the useful question is how to design an assessment that makes reasoning visible, including when students have access to AI.

Why a polished case analysis is incomplete evidence

AI can generate plausible analysis, commit to a recommendation, and apply course vocabulary. A convincing submission therefore needs to be assessed alongside the student's ability to explain the evidence and defend the choices.

Writing style or a generic recommendation does not establish whether a student used AI.

Case study assignments, when they're designed well, don't ask students to summarize information. They ask them to commit to a strategic recommendation, defend it against plausible alternatives, explain why option A beats option B given specific constraints, and justify trade-offs with course concepts.

Ask students to connect their recommendation to specific evidence, explain a rejected alternative, and respond to a changed assumption. AI can assist with these tasks too, so use follow-up discussion and intermediate work to examine understanding.

Assess the reasoning using stated criteria. Do not infer misconduct from fluency, hesitation, or a change in writing style alone.

What decision-based simulations reveal

The smartest response from educators isn't better detection software. It's redesigning the assessment itself.

Decision-based cases can introduce new information after a learner makes a choice. Authors determine the branching paths and assessment criteria. Students can then explain how the new evidence affects their recommendation.

Three design choices make the assessment more informative:

1. Reveal evidence in stages. Ask learners to explain what changed their view at each stage.

2. Connect the response to the case. Require a justification tied to the information available on the learner's path. Branches can differ, but every path need not be unique.

3. Ask for reasoning. Evaluate the student's explanation against explicit criteria rather than treating a correct choice as sufficient evidence of understanding.

These choices give instructors more material to discuss. They do not make AI assistance impossible, and students still need clear guidance about permitted use.

We go deeper into student reasoning in Design AI-Resistant Assessments That Reveal Student Reasoning.

What to assess in student work

Focus feedback on the quality of the argument:

Depth where it matters. Does the analysis examine the most consequential issue in the case?

A defensible recommendation. Does the student choose a course of action and explain the trade-offs?

Specific course connections. Does the student show how a concept applies to this case?

Evidence and uncertainty. Does the student distinguish what is known from what they assume?

Set expectations before the assignment

Tell students which AI uses are permitted, what they must disclose, and which parts of the assessment require independent work.

Ask for enough process evidence to support a conversation about understanding, without treating extra documentation as proof of authorship.

Provide examples of a well-supported recommendation and a weakly supported one. Explain how the rubric distinguishes them.

Give students a practical case-analysis framework

The following six prompts can help students structure their work:

1. Find the decision. Before analyzing anything, find the single strategic choice the case wants you to make. Everything else is context for that decision.

2. Make a provisional choice. Ask students to select an initial recommendation, then revise it if the evidence warrants a change.

3. Apply one framework, specifically. Pick the tool (SWOT, Porter's Five Forces, whichever the course covers) and apply it to this business, in this industry, at this moment. Generic application gets generic marks.

4. State your recommendation in the first paragraph. Do not bury the lead. "We recommend entering the Southeast Asian market via a joint venture with X", the reader knows where you stand, and the rest of the paper defends that position.

5. Acknowledge the alternative you rejected. This is the single strongest signal of genuine analysis. Show you considered the other path and chose against it for a specific reason.

6. Connect every paragraph to your thesis. Before you finish a section, check: does this paragraph advance the argument for the decision you made? Cut anything that does not.

LiveCase can support this design with multimedia, questions, branching paths, and AI character conversations. Use a case to collect explanations, then build the debrief around the evidence students used.

FAQ

Can professors identify AI-written work from its style?

Style alone does not reliably establish authorship. Evaluate the work against the rubric and follow institutional procedures if there is a concern about academic integrity.

What are decision-based simulations?

They are interactive cases in which learners make choices and respond to scenario developments. Paths and consequences depend on the case design. Students may still use AI, so the assessment should include evidence of understanding.

Is using ChatGPT for research on a case study cheating?

State the permitted uses in the assignment and follow your institution's policy. Requirements for disclosure and independent work vary.

What evidence should I use to assess understanding?

Combine the submission with appropriate process evidence, specific follow-up questions, and a clear rubric. Avoid treating a detector score or writing style as conclusive evidence.

How can I help students begin a case analysis?

Ask them to identify the main decision, make a provisional recommendation, and gather evidence for and against it. Then have them explain what would change their view.

What makes a case study analysis good?

A good analysis commits to a clear recommendation, defends it against at least one plausible alternative, applies course concepts to the specifics of the case rather than in general terms, and acknowledges the risks of the chosen path. Strong recommendations are specific, not "grow the business" but "enter the German market via a distribution partnership with X."