As educators, we have an imperative to consistently evolve how we teach and assess students to ensure they are developing the future-focused skills required to thrive amid rapid technological change. Generative AI is arguably the most transformative technological shift of our era, and rather than being insulated from it, our learners need to be immersed in these tools and their implications.

That’s why for the upcoming 2024-2025 academic year, I’ll be completely revamping a major case study assignment for my masters module – leading and managing startups. Instead of providing students with a pre-written business case scenario to analyse, they will leverage generative AI like Claude or ChatGPT to design their own original 2500-word case studies from scratch.

The new assignment challenges students to first define the background of a fictitious startup company they envision, including the initial product/service, founding story, and early market traction. They then outline key parameters around two domains where the company would likely face significant scaling challenges as it rapidly grows, such as operations, human resources, leadership, marketing, IT, and more.

Using a generative AI model, students will iteratively refine prompts to have the AI construct a dynamic, contextually rich 2500-word narrative that explores how those escalating scaling challenges could realistically unfold for their hypothetical startup. What bottlenecks might it hit? What risks and constraints must be navigated? What potential solutions and best practices could be implemented?

By driving this entire case study creation process, students gain vital experience in AI prompt engineering – strategically scoping prompts and leveraging an AI’s language abilities to transform that prompt into a nuanced, multi-faceted business narrative. It’s a form of human-AI co-creation that requires continuously refining inputs based on the AI’s outputs.

But the benefits go beyond just procedural experience with generative AI. This revised assignment design synthesises many core pedagogical priorities:

• Developing critical thinking and problem-solving by having students grapple with open-ended challenges without prescribed resolutions.
• Promoting self-directed learning as students synthesize domain knowledge to effectively scope and critique their AI-generated case studies.
• Facilitating more authentic skill application by blending theoretical concepts with dynamic, contextual business scenarios.
• Nurturing responsible innovation by having students evaluate the AI model’s blind spots and consider ethical implications.
• Cultivating written and oral communication abilities through crafting well-structured cases and presenting findings

To help prepare students, I’ll provide support in the form of an example case I created using Gen-AI to illustrate well-scoped versus poor case study prompts. There will also be workshop sessions where they get feedback on draft prompts and outputs.

Additionally, the assignment will have built-in points for peer review. Students will share their AI-generated cases, provide and receive feedback on areas that need greater nuance or realism, and have opportunities to further refine their narratives through this iterative critique process.

The final deliverable will involve students presenting a comprehensive rationalization of their case study – the context and plausibility of their fictitious company, the key scaling challenges they chose to focus on, their prompt engineering approach, the AI’s strengths and limitations they had to navigate, ethical considerations around its outputs, and their proposed frameworks for effectively analysing the case.

I’m under no illusion that this revised assessment instantaneously solves all our pedagogy challenges. It’s simply an iterative step in evolving how we mindfully incorporate generative AI’s capabilities into curriculum design for more engaging, higher-order learning experiences. I look forward to getting student feedback and continually refining the approach.

But I am confident that immersing learners in these human-AI co-creation processes is vital for developing core competencies they’ll need. If we want our curricula to remain relevant and prepare graduates for future workforce demands, we must thoughtfully integrate generative AI into assessments and pedagogy. This new case study assignment strives to put that principle into practice in an authentic, purposeful way. I’m excited to pilot it next year and continue adapting our approaches to harness generative AI’s creative potential responsibly.