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As a case study instructor, whether you like it or not, artificial intelligence (AI) is no longer new, and it’s here to stay.
And your students know it. In fact, before they walk into your case classroom, there is a good chance that they have already used AI to summarize the case, analyze the data, apply frameworks, and recommend a course of action. So what happens to the learning that used to take place during those hours of preparation?
“Last fall, while teaching an AI-themed first-year seminar, I was struck less by my new students’ AI proficiency than by their assumptions. They were not debating whether AI belonged in their future. They simply assumed it did,” says James Norrie, a professor of law and cybersecurity and founding dean of the Graham School of Business at York College of Pennsylvania, in the University Business article First AI-native class arrives on campus. What now? “Like Wi-Fi, smartphones, or social media, AI had become part of their environment. Their questions were no longer whether to use AI, but how, when, and why.”
Before class, students are expected to read the case, analyze it, review the assignment questions, and prepare to actively engage in the classroom discussion. This preparation stage is critical to learning. It gives students the opportunity to do a deep dive into the material, then reflect, ponder, and explore.
But much of what used to take students hours to prepare, AI can now generate instantaneously. This includes summaries, data analysis, frameworks, and decision options. So, what now?
A Pedagogical Redesign
Navigating this new reality requires a different approach to case teaching. The goal should not be to prevent AI from entering the classroom, but to redesign the pedagogy of the case classroom around its presence. And the real shift is not necessarily technological, but educational. It's about moving away from content delivery toward better learning design, and from policing AI use toward fostering good judgment.
“I think we need to stop treating a single written product as the only proxy for student understanding. If AI can help students generate sophisticated, polished first drafts, then what happens next? What should a student do with those ideas?” says Cortney Hanna-Benson, associate director of digital learning at Western University's Centre for Teaching and Learning.
“From an instructional design perspective, I see this as a shift towards thinking more holistically about gathering diverse and creative artifacts of our students’ learning. Educators should look to create opportunities for students to demonstrate how they think and make decisions, and how they engage with and adapt their ideas over time,” she added.
Hanna-Benson’s point aligns closely with what the case method has always been designed to do. Even before the introduction of AI, learning with cases has always been about engaging with thinking, judgment, and decisions. The focus has never been on a shiny, polished output. And as AI technology continues to evolve, this approach is more useful and relevant than ever before, for both students and instructors.
The Classroom as a Forum for Developing Good Judgment
Today, AI might help a student prepare, or quickly craft, a passable, baseline response, but it cannot replace the social experience of learning and interacting in a classroom with peers.
“Fluency is not the same as wisdom. A polished answer is not the same as a formed mind. At its best, the [case classroom] has always been a rehearsal space for leadership,” says Euvin Naidoo, distinguished professor of global accounting, risk and agility with the Thunderbird School of Management, during the Ivey Publishing webinar When AI Enters the Classroom: Transforming Case-Based Learning.
Naidoo added that, “Paradoxically, in this age of AI, it seems that the case method has really come to the fore as not just a method to pull learners in, but also as a mindset for both the boardroom and what we call the CXO mindset, of how to move from strategy to execution.”
That means moving away from treating AI as a problem and toward surfacing how students are using the technology. Exercises can uncover both its strengths and weaknesses, while deeper “why” questions push students to articulate their own judgments and opinions about the case scenario. The discussion can then move beyond easy, polished responses and into the confusion, ambiguity, and competing perspectives where deeper learning takes place.
That approach is already playing out in case classrooms. For Debmallya Chatterjee, a professor of operations, supply chain and quantitative methods at S.P. Jain Institute of Management & Research (SPJIMR), AI use has created an unexpected shift: some students who previously weren't opening cases are now using LLMs to prepare them. Knowing that AI is part of their preparation, Chatterjee has adjusted what happens once they enter the classroom.
“As an instructor, I am challenging the analysis, which forces the students to revisit what AI has done,” he says. He also introduces contextual challenges and perspectives that may not appear in the teaching note and that an LLM may have missed.
And when it comes time for students to respond, Chatterjee takes the technology away. “Inside the classroom, I am going pen and paper, not allowing students to access their laptops. This enables thinking on the spot.”
Yasser Rahrovani, an associate professor of Information Systems with the Ivey Business School, makes a similar point during the previously mentioned webinar. “If [students] come to class with clean, well-developed wording, it's not going to be helpful. Keep going back to diagnosing the technical, social, contextual factors that led to failure or success,” he says. “Even if they get answers from AI, they have to be ready and think in depth about why something is a failure or a success.”
Preserving Productive Friction
The approaches Chatterjee and Rahrovani describe point to a broader question: when AI makes preparation easier, what kinds of effort still matter for learning?
Recent research from Ivey Business School offers one way to think about that question. In the white paper From Chalkboard to Chatbot: A Review of AI-Enabled Learning Tools for Business Schools, Julian Birkinshaw, dean of Ivey Business School; Mazi Raz, an assistant professor of strategy; and Markus Walters, directo of strategic Initiatives at Ivey, examined 14 AI-enabled learning tools and the role they can play across the learning journey. Their findings centre on what they call “productive friction”: the effort involved in grappling with difficult concepts, practising skills, and exercising judgment under ambiguity.
AI can support that effort, but it can also make it easier to bypass. As the authors explain, students can use the technology to become more efficient in the short term while potentially undermining longer-term learning. For instructors, the challenge is therefore to identify where students may be handing important parts of the learning process over to AI and deliberately preserve the effort that matters.
Sometimes that means restoring friction that AI has removed, for example, through device-free discussions or closed-book activities. In other instances, AI can be used to introduce new productive friction, such as presenting counterarguments or withholding interpretation so that students must continue doing the reasoning themselves.
In other words, the goal is not to make student preparation harder for its own sake, but to create an environment that produces authentic learning. AI can be useful when it helps students enter the classroom better prepared, but it can become limiting, and even harmful, when it allows students to cognitively offload their thinking to avoid the messiness of learning.
It’s a principle that Naidoo also sees at the core of case-based learning. He describes the effort, ambiguity, and discomfort that students encounter in the case classroom as “the productive struggle.” For him, preserving that struggle is essential: “The struggle is not a defect. It is the pedagogy.”
Case method learning relies on ambiguity, disagreement, and discomfort. And a core value of case discussions is reaching the moment when a student realizes that two intelligent people can look at the same facts and reach different conclusions.
“I increasingly become a designer rather than a deliverer of content,” Rahrovani says. That design is ultimately about preserving the conditions in which deeper learning can happen: where students must think harder, listen to competing perspectives, and make decisions without the certainty of a single right answer.
That puts greater responsibility on students to understand and defend their own thinking, regardless of how they prepared.
“The question [for students] is no longer Did you prepare? The question is, Do you own the thinking behind it? And this is a very important shift for leaders both in the boardroom and in the classroom to do so with intent,” says Naidoo. “AI may raise the flow of preparation, but faculty need to raise the ceiling of judgment.”
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Teaching with AI: Practical Strategies for Case Classrooms
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Case Learning Beyond the AI Answer: Strategies for Better Teaching
Discover how case teaching can move students beyond AI-generated answers to build judgment, adaptability, and decision-making.
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