Content Generation: Exam Variants, Cases, and Course Materials
Draft exam variants, mini-cases, and rubrics in minutes — faculty curate, edit, and approve.
Content Generation: Exam Variants, Cases, and Course Materials
BeginnerTeaching Problem
Developing fresh case studies, problem-set variants, and discussion prompts is time-consuming. Textbook examples can feel dated within a year. Creating multiple assessment versions for academic integrity multiplies the workload.
How AI Addresses the Problem
AI drafts multiple variants of problem sets, mini case studies anchored in current events, discussion questions, learning objectives, and even accreditation documentation. Faculty serve as editors and curators—reviewing for accuracy, adding nuance, and calibrating difficulty—reducing content development time from hours to minutes.
Deployment Details
- Courses
- All finance courses (UG and MBA)
- Tools
- ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity (for research-grounded content generation)
Sample Prompts
TVM Problem Set Variants
Create 5 time-value-of-money problem variants for Principles of Finance. Requirements: (1) Each uses a different realistic business scenario (equipment purchase, commercial real estate loan, lease-vs-buy decision, retirement planning, bond pricing). (2) Vary the number of periods (3-15 years), interest rates (4-12%), and payment structures (lump sum, ordinary annuity, annuity due, uneven cash flows). (3) Include one problem that requires solving for the interest rate and one that requires solving for the number of periods. (4) Provide complete answer keys with intermediate steps showing the financial calculator keystrokes (N, I/Y, PV, PMT, FV). (5) Difficulty should range from straightforward (Problem 1) to challenging (Problem 5).Tech Acquisition Mini Case
Draft a 600-word mini case study for MBA Corporate Finance based on a real 2024-2025 technology sector acquisition. Change company names and disguise identifying details. Include: (1) acquirer and target financial profiles (revenue, EBITDA, debt levels), (2) stated strategic rationale, (3) deal structure (cash, stock, or mixed), (4) at least one complicating factor (regulatory scrutiny, cultural integration risk, or customer overlap). End with 4 discussion questions that require students to evaluate the deal using NPV, comparable transactions, and strategic fit analysis.AACSB AoL Alignment Matrix
Generate an AACSB Assurance of Learning alignment matrix for an undergraduate Financial Statement Analysis course. Map 6 student learning objectives to Bloom's Taxonomy levels, specific course assignments, and assessment rubric categories. Include at least 2 objectives at the Analyze or Evaluate level.Assessment Approach
Not directly student-assessed (this is primarily an instructor productivity tool). When used as a student exercise in advanced courses—asking students to generate and then critique AI-produced case studies—grade the quality of the critique, not the AI output.
Skills Developed
Finance Concepts
N/A (instructor tool); when used as student exercise: critical evaluation of financial scenarios, identifying unrealistic assumptions
Analytical Skills
Efficiency in assessment design, quality control of AI-generated content
Professional Skills
N/A (instructor tool)
AI Literacy Skills
Effective prompting for structured content generation, output quality verification, understanding AI’s tendency to fabricate financial data
Evidence
AACSB Insights and the AAC&U Institute on AI, Pedagogy, and the Curriculum (2024–2026) report widespread faculty adoption of AI for content generation. Bowen & Watson (2025, 2nd ed.) include expanded sections on using AI for assignment design.
Risks and Safeguards
Fabricated Financial Data
AI can invent plausible-sounding but entirely fictional financial figures. Mitigation: ALWAYS verify numbers against primary sources before distributing to students.
Insufficient Complexity
AI-generated cases tend to be too ‘clean.’ Mitigation: treat AI output as a first draft; add messiness, conflicting data points, and red herrings.
IP Considerations
Check institutional policies on ownership and distribution of AI-generated course materials.
Curricular Homogenization (OECD Warning)
OECD reports that GenAI can improve content quality while reducing the collective diversity of generated material. AI-generated finance cases tend to cluster around large-cap U.S. technology and retail sectors with straightforward deal structures. Build in a diversity check before using any AI-generated case or problem set: rotate across (1) industry sector, (2) firm size (micro-cap to large-cap), (3) geography (U.S., international, emerging markets), (4) stakeholder lens (creditor, equity holder, regulator, employee), and (5) market regime (expansion, recession, rising rates). If your case library lacks diversity on any dimension, prompt explicitly for it.
Try it this week
First-Week Implementation Pilot
Open ChatGPT right now. Paste this prompt: ‘Generate 3 variants of a bond pricing problem for Principles of Finance. Each should use a different coupon rate (4%, 6%, 8%), different maturity (5, 10, 20 years), and different YTM (3%, 5%, 7%). Include answer keys.’ Compare the output to your existing problem set. Edit the best variant for your next quiz. Total time: 10–15 minutes.