Why Case Instructors Matter More Than Ever in the Age of AI

Artificial intelligence (AI) can, in an instant, summarize a case, identify the decision point, compare options, generate stakeholder perspectives, and recommend a course of action. For case instructors, it’s tempting to ask what has been lost because of this rapidly evolving technology. And it raises a more practical and deeply human question: if AI can analyze the case, what is the instructor’s role?

The answer from educators actively experimenting with AI in case-based learning, and experts examining its broader impact on higher education, is not that instructors become less important; rather, it’s that their most important work becomes clearer.

“The strategic question we keep dodging is what happens to a university when expertise itself, the scarce thing we have historically concentrated, credentialled, and sold back to society, becomes cheap, abundant and uncomfortably good,” says Mark Daley, Chief AI Officer at Western University and Professor in the Department of Computer Science. “Educators are optimizing the classroom while the ground under the institution may be shifting.”

Instructors as Curators of Attention

In a traditional case classroom, students prepare by sorting through the facts: What happened? Who is involved? What decision needs to be made? And now AI can help assemble those baseline answers quickly. But a case instructor’s expertise has never been limited to knowing the facts of the case. The pedagogical focus lies in knowing what facts are important, what details are distractions, what nuance needs to be uncovered, and what unresolved tensions get time during the discussion.

That curatorial role of the instructor becomes more significant when students have used AI and come to class equipped with more analysis than they can evaluate. Fundamentally, AI’s value is in how the technology can expand the range of perspectives up for discussion and debate.

“The quality of the prompts and the follow-up prompts is leading to better thoughts and better papers,” says David Wood, Lecturer of Operations Management with the Ivey Business School, during the Ivey Publishing webinar AI in Case Teaching: Deepening Learning with AI as a Thought Partner. “And I’m actually quite happy with that. I’m happy that students are learning how to use the tool.”

In response to the technology, the instructor’s task is to help students decide what to do with the thoughts, ideas, and expanded range of perspectives.

In practical terms, this might mean asking students to bring an AI-generated analysis to class as an artifact to inspect and analyze. Where does it overgeneralize? What are the implicit and explicit biases? What context does it flatten? What stakeholder does it misunderstand? What trade-off does it avoid? What would make its recommendation fail?

Instructors as Examiners of Reasoning

AI can often produce a recommendation that sounds credible. That makes it easier for students to appear prepared and harder for instructors to know whether students have done the deeper work of reasoning.

And this is where the case instructor’s role shifts from evaluating answers to examining the students’ thinking behind them, which does not necessarily require abandoning traditional teaching methods. It could mean asking students to articulate how their position changed. Or it could mean using oral defences, short in-class decision exercises, peer challenges, post-discussion reflections, or reviewing AI chat histories as evidence of their thinking process.

“The teaching moments come from moments of friction, from ambiguity, from where there's nuance that the LLMs [large-language models] are missing,” says Tawnya Means, Founding Partner and Principal, Inspire Higher Ed, during the same webinar. “That's where that deeper learning is going to happen because we're able to bring the expertise of the instructor and the experience or scaffolding of the students, and even the information that's collected and leveraged from the LLM, to be able to bring it all together.”

On that note, Wood notes that large-language models are useful at drawing on what is already known, but weaker in the kinds of uncertain, ambiguous situations where case learning is most valuable. “They aren’t necessarily great at thinking beyond the creative elements and filling in those gaps of ambiguity and uncertainty,” he says. “And that’s, I think, where judgment comes in.”

That kind of thinking can also be shaped by the context and experiences students bring into the classroom. Rajesh Nair, a doctoral candidate at the Indian Institute of Management Ranchi and an Ivey Publishing bestselling case author, points to the diversity within Indian business school cohorts, including differences in region, socio-economic background, professional experience, and exposure to markets and institutions.

He also highlights the concept of jugaad thinking: the instinct to work around constraints, improvise when established approaches fail, and find unconventional, low-cost solutions to complex problems. These different ways of approaching a problem can lead students in directions that AI-generated analysis may not anticipate.

The instructor’s role, Nair says, is to make that diversity of thinking productive by encouraging competing interpretations and challenging the assumptions behind unconventional solutions. “The skill of an experienced case teacher to enable ‘divergence before convergence’ is something AI cannot replicate.”

An instructor can expose those gaps by employing questions that require students to reconstruct, defend, and revise their thinking in real time. Instead of supplying insight, instructors can work to ensure its usefulness and quality.

“We should assess the things a model cannot be rather than the things it can produce: live reasoning under uncertainty, the ability to defend a position, to notice ethical stakes in the wild, to own a conclusion when it has consequences,” says Daley. “Ask students to demonstrate judgment and accountability, not a clean paragraph.”

Instructors as Watchtowers of Human Accountability

Arguably the most enduring and valuable role of the case instructor is leading a classroom where students must think out loud with other people. AI might be able to simulate perspectives, but it cannot create the same feelings when a peer challenges them, when a classmate identifies a blind spot, or when an instructor asks them to justify their decision.

“The seminar, the case debate, the argument that runs long past the end of class, these are technologies for shaping judgment, not delivery mechanisms for content,” Daley says. “They are embodied, relational and time-extended, which makes them far harder to commoditize than document summarization. If anything, abundant machine cognition makes that room more valuable, not less.”

Instructors are now, more than ever, convenors of intellectual accountability: they decide when to let disagreement run its course, when to press for evidence, when to bring in a quieter voice, when to expose an ethical consequence, and when to ask the class to choose.

“Both from the student’s perspective and from the faculty’s perspective, we need to have transparency about the value of these tools,” says Means. “That includes discussing which pieces of this do you use and which pieces of this are not relevant, and how AI-generated material fits with the learning goals of the course.”

Instructors also remain essential because they bring wisdom, maturity, lived expertise, disciplinary judgment, and classroom sensitivity to the discussion. For Means, an instructor’s “ability to curate information and contextualize information is still going to be very vital.”

And because AI can produce an algorithm-driven version of a case discussion before the class starts, the instructor has an exciting opportunity for capitalizing on that preparation to foster a demanding and rich human experience.

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Why Case Instructors Matter More Than Ever in the Age of AI

Instructors as Curators of Attention

Instructors as Examiners of Reasoning

Instructors as Watchtowers of Human Accountability

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