Curing Case Study Fatigue with UvA AI
This Time, no Windmills: Curing Case Study Fatigue with UvA AI Chat as a Creative Partner – Brandon Armstrong
Does this sound familiar?
You have a fantastic case study—one that perfectly illustrates the core concepts of your course—but you’ve used it so many times that students are starting to greet it with a familiar sigh. For the teaching team of the Political Science course Public Policy and Governance, that sigh came with the “windmills again” scenario.
The case, which explores power dynamics in a dispute over windmill construction, was based on real-world interviews and was pedagogically sound. The problem? Students were getting “distracted or bored” with the topic, which got in the way of the actual learning goals. So, Brandon, one of the teachers on the team, and his colleagues had a thought: what if we could keep the intricate power dynamics of the case, but wrap them in a fresh scenario that would be more engaging to our students? That’s when he turned to the UvA AI Chat to act as a creative teammate.
From Windmills to Surveillance Cameras
Brandon’s goal was simple: to “update the scenario” without losing the original case’s complexity and connection to the course materials and ILOs. He began by feeding the AI the context of the course and the details of the original windmill case. The AI was “better than I thought it would be” at extracting the key stakeholders and their power relationships.
From there, Brandon prompted the Chat to generate new policy scenarios that would appeal to his students while maintaining the same character dynamics. After generating about ten ideas, one stood out: a historic tourist town facing a mandate to install modern surveillance cameras. This new case pitted privacy advocates against security proponents, traditionalists worried about aesthetics against a tech company with deep pockets.
The new scenario was a hit. The power dynamics perfectly paralleled the windmill case, but the topic felt fresh and highly relevant to students’ lives. “Nobody mentioned, oh, it’s windmills again,” Brandon confirmed, noting that the focus returned squarely to the intended learning outcomes.
Caution: Your Brain Is Still Required
While the pilot was a success, Brandon is quick to point out that the AI isn’t a simple “plug and play” solution. His experience highlighted a few critical warnings for colleagues:
- Proofread for substance, not just spelling. The AI’s output can sound plausible at a glance, but it requires a “high degree of proofreading” to ensure it maintains continuity and connects deeply with the course material. Simply copying and pasting “would not have been convincing”.
- Watch out for hallucinations. At times, the AI invented a type of stakeholder or a power dynamic that wasn’t in the original case, which could have thrown off the entire exercise. Double-checking the output is essential.
- Inject your own voice. The teaching team had to replace some of the AI’s language with “more natural sounding phrases that we would use in the class” to make the material feel authentic.
The Main Takeaway for Other Teachers
Brandon’s core message is a nuanced one. He doesn’t see the AI as a time-saver, but as a quality-enhancer. “To use it well is not exactly a time saver,” he explains, “but if you spend the same amount of time on it, the quality of what you get can be much higher”.
He encourages teachers to think of the AI as a new “teammate”. It’s an incredibly fast reader and a tireless generator of ideas that can save you the “imaginative energy” needed to dream up new content from scratch. But this teammate needs direction, critical oversight, and a human touch to turn its raw output into a polished, effective teaching tool. The teacher’s brain is still very much the most important part of the equation.