The Classroom Has Lost Its Monopoly On Learning: Are B-Schools Ready To Rethink Where Learning Happens?

For more than a century, business schools have organized learning around a single event: the classroom discussion.

Students prepared individually before class by reading and analyzing a case. Faculty then brought everyone together to debate competing interpretations and recommendations. Although students spent hours preparing, the classroom remained the center of gravity. It was where assumptions were challenged, judgment was refined, and learning ultimately occurred.

Generative AI is changing that architecture.

The debate today focuses on whether AI will replace the case method. That is the wrong question because it assumes the case is the unit of analysis. It is not.

The real unit of analysis is learning.

Generative AI is not simply introducing a new teaching tool. It is redistributing learning across the educational journey, making it possible for different forms of learning to occur at different times, in different settings, and in ways that are more personalized, interactive, and repeatable.

This matters because learning is not one activity. Understanding a concept, practicing a managerial skill, exercising judgment, and developing a deeper perspective on leadership require different kinds of experiences. The traditional case method brought all of them together in one classroom discussion. Generative AI makes it possible to separate them and give each form of learning more of what it needs.

One of the great strengths of the case method was precisely this integration. A single case discussion could involve acquiring knowledge, practicing managerial thinking, developing judgment, and experiencing moments that reshaped how students thought about leadership and organizations.

But concentrating all four functions in a single classroom discussion also required compromises. Faculty had limited time to move from explaining concepts to debating decisions to practicing managerial skills. Every student experienced essentially the same learning environment, regardless of their preparation, prior knowledge, or individual learning needs. Opportunities for repetition, individualized feedback, and deliberate practice were necessarily limited.

There was little alternative. The case method was designed around the scarcity of faculty attention.

One professor. One classroom. Ninety minutes. Eighty students.

Everything had to happen within those constraints.

The case method bundled learning because it had to. Generative AI allows us to unbundle it so that each form of learning can occur where it is most effective.

Different forms of learning benefit from different conditions. Knowledge can be acquired more effectively through personalized explanation and feedback. Managerial skills improve through repeated practice. Judgment develops through making decisions, experiencing consequences, and reflecting on them. Deeper strategic understanding can emerge when experiences unfold over time rather than being compressed into a single discussion.

AI makes it possible to give each of these forms of learning more of what it needs: more personalization, more practice, more repetition, more immediate feedback, and more continuity.

Instead of forcing every student and every learning objective through the same ninety-minute experience, business schools can begin to design the learning journey around how different capabilities are actually developed.

The opportunity is therefore not simply to move learning beyond the classroom. It is to put learning in the place, time, and experience where it can be most effective.

This shift can already be seen across four emerging educational settings.

BEFORE CLASS: PERSONALIZED LEARNING

Before class, AI increasingly serves as a personalized learning companion. At Harvard Business School, students use ChatLTV, a course-specific AI assistant trained on faculty-approved materials, to ask questions, clarify concepts, and receive feedback as they prepare for class. Similar AI coaching tools are being explored at schools including Wharton and Stanford.

The important change is not simply that students have another source of information. Preparation itself becomes a learning experience. Students can ask questions when they become confused, test their understanding, explore alternative interpretations, and receive immediate feedback.

Knowledge acquisition can therefore happen before students enter the classroom, allowing classroom time to focus more heavily on the activities that benefit from being together: challenging assumptions, comparing perspectives, debating difficult choices, and developing judgment.

DURING CLASS: INTERACTIVE PRACTICE

During class, AI is becoming an active participant in learning. At INSEAD, Darden, ESSEC, and a growing number of other schools, students use Learn AI to engage in AI-powered role plays, negotiating with virtual executives, coaching employees, or interviewing stakeholders. Harvard Business School has also experimented with AI avatars that allow students to practice sales conversations and entrepreneurial pitches before reflecting on their performance with classmates.

Rather than simply discussing leadership, students practice it.

The difference is important. Students can try a behavior, see how the other party responds, receive feedback, change their approach, and try again. Skills that are difficult to develop through discussion alone become objects of deliberate practice.

The classroom remains important, but students can now use it for richer forms of interaction rather than relying on discussion alone.

BEYOND THE CLASSROOM: DECISION PRACTICE

Beyond the classroom, AI is creating new forms of experiential learning.

INSEAD’s Immersive AI Cases allow students to interview case protagonists, gather information, make decisions, and receive personalized feedback in adaptive learning environments. At Northwestern Kellogg, students investigate organizational problems by interviewing multiple AI characters, each holding different pieces of information, before developing and defending their recommendations.

Instead of analyzing someone else’s decisions, students learn by making their own.

The shift is from observing judgment to exercising it. Students decide what information to seek, which questions to ask, which alternatives to pursue, and when they have enough evidence to act. They can reconsider their choices, receive feedback, and try again.

This creates something the traditional case method could provide only in limited form: repeated opportunities to practice managerial judgment.

ACROSS THE COURSE: DYNAMIC CASES

Looking further ahead, AI may enable a fundamentally different kind of case experience: a case that evolves with the decisions students make.

Instead of discussing a different case each week, students could manage an organization over the course of a semester, inheriting the consequences of their previous decisions. A pricing strategy adopted early in the course might trigger a competitive response weeks later. A hiring decision could shape organizational culture and employee engagement. An acquisition might create unexpected integration challenges. A supply chain disruption or regulatory change could force students to rethink their strategy.

The organization would remember.

It would evolve continuously in response to the decisions students make, much like a real business.

Students would experience how strategic choices accumulate over time. Early decisions could create later opportunities or constraints. Success could generate new problems. Today’s solution could become tomorrow’s challenge.

This remains an emerging frontier rather than common practice. But it points toward a fundamentally different learning experience. Students would not simply discuss what happened to an organization. They would create its history and then have to live with the consequences.

A NEW ARCHITECTURE OF LEARNING

These four settings represent more than four applications of AI. Together, they suggest a new architecture for business education.

Before class, students can explore and experience. During class, they can understand and challenge. After class, they can practice, reflect, and apply. Across the course, they can decide, adapt, and experience the consequences.

The case method is not disappearing. The classroom is not disappearing either.

What is changing is the assumption that they must carry the entire burden of learning.

The case remains powerful precisely because it brings people together to challenge assumptions, compare perspectives, and develop judgment. The classroom remains uniquely valuable for social learning, debate, reflection, and the productive friction of having one’s thinking challenged by others.

But these activities no longer have to accomplish everything.

Generative AI gives business schools the opportunity to distribute learning across experiences that are better suited to different educational objectives. It can make learning more personalized, more experiential, more iterative, and more continuous while preserving the human interaction at the heart of business education.

But this redistribution of learning also changes something else: the role of the professor.

A NEW ROLE FOR FACULTY

For generations, teaching has been organized around the timetable. A professor teaches on Tuesday at 10 a.m.; students come to class; and the professor’s expertise is concentrated into that scheduled encounter. The timetable determines not only when teaching happens, but often what teaching can accomplish.

AI begins to loosen that constraint.

If students can acquire knowledge, ask questions, practice skills, and receive feedback throughout the course, the professor no longer needs to use classroom time to carry the entire burden of learning. Instead, faculty can concentrate their attention where it adds the most value: challenging a deeply held assumption, helping students make sense of conflicting evidence, intervening when a difficult decision exposes a misconception, or bringing a real-world development into the learning experience at precisely the moment it matters.

The classroom remains essential. But its role can change.

Rather than serving as the place where everything must happen because there is nowhere else for it to happen, the classroom can become one of several carefully chosen moments of human interaction.

Some meetings may deserve more time because a difficult debate or complex decision benefits from being explored collectively. Others may be shorter because students have already mastered the underlying concepts. Some topics may call for an additional discussion when students’ experience in a simulation reveals an unexpected problem. And occasionally, a real-world event may make a concept suddenly urgent and worth bringing the class together to examine.

The timetable can become a design variable rather than a constraint.

This does not mean fewer faculty interactions. It means more purposeful ones.

AI can handle many of the activities that previously consumed faculty time: explaining foundational concepts, answering repetitive questions, providing initial feedback, and creating opportunities for practice. That frees professors to focus on what is harder to automate: framing the right questions, connecting seemingly unrelated ideas, challenging assumptions, interpreting ambiguity, creating productive disagreement, and helping students understand the implications of what they have experienced.

Paradoxically, AI could therefore make faculty more, not less, central to the learning experience.

The professor of the future may spend less time delivering the same ninety-minute lesson to everyone and more time designing, orchestrating, and intervening across a much richer learning journey.

The goal is not to eliminate the classroom or the timetable. It is to stop treating them as the boundaries of teaching.

THE QUESTION BUSINESS SCHOOLS MUST NOW ASK

For more than a century, business schools built their educational architecture around the scarcity of faculty attention. AI changes that equation.

The opportunity is not to replace professors with machines, nor to replace classrooms with screens. It is to rethink how scarce human expertise is deployed.

Students can learn concepts when they need them. They can practice skills repeatedly. They can make decisions and experience consequences. They can receive personalized feedback between sessions. And faculty can devote more of their attention to the moments when human judgment, perspective, challenge, and interaction matter most.

This could ultimately make business education more human, not less.

The classroom may lose its monopoly on learning. The timetable may lose its monopoly on teaching.

But what replaces them is not less faculty involvement.

It is a richer learning journey in which technology handles more of the repetition and personalization, while professors spend more of their time doing what only great teachers can do: challenging how students think, shaping how they see the world, and helping them become better leaders.

The question is therefore no longer whether AI will replace the case.

It is whether business schools are ready to rethink where learning happens—and when, where, and how faculty can make the greatest difference.

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Ithai Stern is a Professor of Strategy at INSEAD. Benjamin Stevenin is the former Director of Business School Solutions and Partnerships at Times Higher Education.

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