AI Is Rewiring the Classic M.B.A. Case Study - Without Shortcuts
Business students at Northwestern are using AI to think better, not to cheat. They're applying it to real strategic challenges, using the case method the way it was intended: analyze, debate, decide.
For more than a century, the case method has trained students to make decisions under uncertainty. AI adds a second brain to that process-quick analysis, diverse scenarios, and constant pushback-while keeping judgment in the hands of the student.
What's Actually Changing
- Preparation is faster. Students summarize long cases, extract key variables, and surface blind spots before class.
- Discussion is deeper. AI generates alternative strategies, rebuttals, and sensitivity tests on demand.
- Assignments shift from "write the answer" to "show your thinking, test assumptions, and compare paths."
Why This Matters for Educators
- More time on judgment and ethics, less time on rote analysis.
- Richer participation from students who benefit from scaffolding before speaking up.
- Transparent reasoning trails: prompts, versions, and decisions you can assess.
How to Integrate AI Into Case Teaching (Without Losing Rigor)
- Set the rules first: define permitted tools, required disclosure, and what work must be original.
- Assess the process, not just the output: require prompts, iterations, and final rationale with cited assumptions.
- Use roles: assign CFO/CMO/COO roles; have each student ask AI for function-specific risks and trade-offs.
- Mandate uncertainty checks: students must run at least two contrarian strategies and a worst-case scenario.
- Keep data safe: use institution-approved tools or no-upload modes for proprietary cases.
Sample Prompt Patterns You Can Share With Students
- Structured analysis: "Act as a strategy analyst. Using Porter's Five Forces and a simple unit economics model, identify top 3 revenue levers and top 3 cost drivers. List assumptions, confidence level, and what evidence would change your view."
- Counterarguments: "Generate two credible strategies that would beat my current recommendation. Highlight failure points and early warning signals."
- Clarification: "Ask 5 high-impact questions that would most reduce uncertainty in this case. Explain why each question matters."
- Ethics and risk: "Surface stakeholder risks (customers, employees, suppliers, regulators). Propose mitigations and metrics for each."
In-Class Activities That Work
- Lightning briefs: teams use AI to draft a 150-word recommendation, then defend it against AI-generated objections.
- Sensitivity sprints: give one metric (CAC, churn, price). Students test 3 scenarios and report the pivot point for go/no-go.
- Red team vs. blue team: one side builds a plan; the other uses AI to stress test assumptions and propose a superior move.
Assessment Ideas
- Reasoning rubric: clarity of assumptions, exploration of alternatives, evidence quality, and ethical considerations.
- Process artifacts: submitted prompts, model iterations, and notes on what changed the student's mind.
- Oral defense: 3-minute live pitch plus 3-minute cross-exam powered by AI-generated counterpoints.
Policy and Academic Integrity
- Disclosure: state where AI helped (summary, modeling, editing). No undisclosed ghostwriting.
- Citation: if AI introduces sources or data, students verify and cite originals.
- Privacy: no sensitive case data in public tools; prefer institution-approved options.
Tooling and Setup
- Model variety: allow at least two models for comparison to reduce single-model bias.
- RAG for cases: upload case PDFs and exhibits to a secure system; restrict responses to provided materials.
- Quant support: pair AI outputs with spreadsheets; require formulas and unit assumptions, not just prose.
- LMS integration: collect prompts and versions alongside final submissions.
How to Measure Impact
- Pre/post quizzes on core strategy concepts.
- Rubric scores on decision quality and alternative exploration.
- Time-on-task and student confidence ratings.
- Faculty workload and feedback quality before vs. after adoption.
Context for the Case Method
The case method thrives on tension: incomplete data, clashing incentives, and time pressure. AI doesn't remove that tension-it makes the trade-offs visible faster, so you can coach judgment more directly.
Used well, AI becomes a Socratic partner. It asks better questions, pokes holes in confident answers, and forces students to show their work.
Further Reading
Practical Training for Educators
If you want ready-to-use prompts, assignment templates, and model comparison checklists, explore curated options here:
Bottom line: keep the case method. Add AI as a disciplined practice. Students still make the call-their judgment just gets sharper, faster.
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