Reinventing the Classroom Means Reinventing Ourselves

• As schools more deeply integrate AI into their curricula, the professor’s role is to contribute judgment, mentorship, and real-world expertise to students’ learning process.
• In a two-course sequence at Texas Christian University, the professor works alongside students on an industry-driven experiential project as both coach and team member.
• As a member of the team, the professor enables students to tackle more ambitious projects, helping them gain an edge in a competitive job market.

 
Thanks to artificial intelligence, higher education is experiencing a soul-searching moment. But if handled successfully, this moment is an opportunity for educators as well as students. As business faculty who are preparing 21- and 22-year-olds for jobs today and beyond, we must place a new premium on reinventing ourselves in the classroom.

That realization has sparked an evolution in our program at Texas Christian University’s Neeley School of Business in Fort Worth. Back in 2022, TCU Neeley added a single week on a curious new tool called ChatGPT to our deep learning course. Today, large language models (LLMs) comprise nearly 80 percent of that class. This spring, we introduced one week on agentic payments into our digital assets and payments course. This fall, agentic systems will occupy more than a third of the curriculum.

The pace of change is no longer incremental. It is structural. Universities were created under a model in which professors delivered expertise. Over the years, their methods of delivery have taken many forms—from lectures to experiential learning to flipped classrooms to online and hybrid formats. These formats all operated under the assumption that knowledge is scarce, professors possess it, and classrooms distribute it.

AI has shattered that model. The uncomfortable truth is that it can already perform many traditional academic functions astonishingly well. It can generate research summaries, create analytic models, test hypotheses against public data sets, explain concepts endlessly, and customize instruction and tutoring to the needs of individual learners. It has made the availability, synthesis, and delivery of knowledge instantaneous, personalized, and constantly available.

At the same time, the marketplace is changing too fast to give professors time to accumulate expertise before introducing students to new ideas. But this does not diminish the value of professors. It simply changes where that value resides.

The Business Professor’s New Role

Given that AI is here to stay, what must business faculty now deliver in the classroom? Students still need someone to provide context, guide them as they cultivate judgment, and help them distinguish between what is possible and what is practical—between a technically elegant answer and one that works in the real world. They need mentors who can model intellectual curiosity and demonstrate what lifelong learning looks like in practice. They need to understand that mastering AI and embracing continuous learning can help them stand out in the job market and advance their careers. 

How do we create value for students under these new conditions?

Working alongside my students, I can use the judgment I’ve developed to point out when students might want to alter tactics, concede and move on, or recognize that they are underestimating themselves.

At TCU Neeley, we have answered this question in part with two new courses called the Fintech Scholar sequence, in which students complete yearlong projects for industry partners. The first semester focuses on learning emerging technologies and evaluating their potential and feasibility. The second semester focuses on building solutions.

On paper, this sounds like traditional experiential learning. In reality, these courses incorporate a new dimension: The professor now participates in students’ hands-on learning experience.

This is not a capstone course where the professor broadly knows the technology, establishes realistic outcomes, and guides students toward predictable results. This is not a flipped classroom where students teach themselves while the professor supervises from a comfortable distance. Instead, the professor works as part of the team.

In this case, I am the educator working alongside my students. Earlier this year, we piloted the Fintech Scholar program in partnership with Vantage Bank. Eight students and I built a proof of concept exploring AI agents that conduct private stablecoin transactions across public blockchain rails while satisfying regulatory constraints.

Within the project, we worked with a range of technologies, from Gradio, an open-source Python tool for creating user interfaces, to GitHub Codespaces, which supports cloud-based development. We also used Solidity to create Ethereum smart contracts and created keystore wallets, which enable autonomus AI agents to manage and transact small cryptocurrency balances. The subject was new, with few external examples to draw from; we were working in a space where tools were only months old and outcomes were uncertain.

At the start, my first role was to act as a coach who set broad goals for the course. But then my role shifted, and I became a player who was assigned a hands-on task as a team member. I used judgment I’ve developed from professional experience and from having to learn new subject matter constantly to provide a different form of expertise—pointing out when students might want to alter tactics, concede and move on, or recognize that they are underestimating themselves. Learning occurred not simply because I was encouraging them, but because I was doing the work alongside them.

Learning and Solving Problems Together

As part of the same team, my students and I confronted uncertainty together. That factor mattered enormously. There were moments when my experience helped us avoid blind alleys and rethink our assumptions. But there also were moments when students discovered solutions before I did.

That exchange was exactly the point. The classroom became less about transferring knowledge and more about taking part in an experience where we built the ability to learn, adapt, and solve problems together. In the end, the solutions that students presented to industry partners weren’t just the products of a classroom exercise—they were genuinely new and useful.

I believe this coach-player model will become more essential in experiential learning in the years to come. Because they are active participants in the process, professors can select industry projects that might otherwise be too challenging for students in a traditional experiential class.

As coaches and players, professors must learn with (and sometimes faster than) their students, use judgment about where the marketplace is heading, and actively engage with industry.

At a time when graduates are finding it increasingly difficult to distinguish themselves in the job market, students completing the Fintech Scholar sequence have an edge—they can highlight their work on subject matter that many of their peers might not encounter for years. These team projects also force professors to expand their own knowledge. As coaches and players, they must learn with (and sometimes faster than) their students, use judgment about where the marketplace is heading, and actively engage with industry.

This is not a burden. For me, taking part in the Fintech Scholar pilot was fun—I looked forward to each classroom assignment. Students were more engaged and the relationships I had with them were deeper, because I was experiencing and growing from the learning process along with them.

New Demands, New Possibilities

In the past, technological advances often resulted in demands for humans to develop deeper expertise. But in the case of AI, some predict that this trend will end with LLMs—that humans will set the problems while machines handle the complexity required to solve them. I do not believe that is true.

Instead, I believe that LLMs introduce the potential for custom applications, new ways of conducting business, and new vulnerabilities and remedies. These possibilities raise, not lower, the bar for human understanding. Even if having an LLM available is like working with someone who has a PhD in a particular subject, humans still must communicate in a manner that allows the technology to be productive, and they still must evaluate whether the outcome actually addresses the original problem.

Reinventing the classroom to accommodate AI ultimately means reinventing ourselves—how we teach and how we continue to learn. This truth applies not only to students preparing for careers that do not yet exist, but also to professors whose responsibility is no longer simply to teach what we know. This moment requires us to demonstrate how we all can keep learning, even (or especially) when nobody knows what is coming next.