When I arrived at Fuqua, I understood that AI was becoming more important, but I had little firsthand experience using it in a meaningful way. Over the course of my first year, that began to change as I became one of the first students to test new AI-enabled classroom and a system that provides custom feedback on how my classmates and I collaborated in our team meetings. Through that experience, and my own experiments with AI in interview preparation and other coursework, I began to see both the potential and the limitations of the technology. Most importantly, I came to believe that AI is most valuable when it supports, rather than replaces, human judgment and interaction.

In the Q&A below, I share how these experiences shaped my perspective on AI and its role in learning, collaboration, and my future career.

1. Coming into Fuqua, what expectations did you have for how AI should be explored in the classroom or in the curriculum?

As someone with little real-world experience in AI, I really had no expectations. I knew that AI was a useful tool in some settings, and that many were claiming it was going to make many professions obsolete, but I had no paid subscription and no workplace or serious personal use. I expected that it would be discussed heavily and used occasionally, and for the most part, that’s exactly how AI was explored in my core classes in Fall 2025.

2. Can you share a specific way you’ve used AI in a class, project, or club that changed how you approached the work?

I started to lean heavily on AI midway through my consulting interview preparation. For those unfamiliar with consulting interviews, they involve casing, which is essentially a mini test run of a type of project that consultants may work on. For example, a generic software company may desire to become more profitable, and the interviewee must examine exhibits, discuss ideas with the interviewer, and ultimately support a conclusion. It is a highly conversational process that requires some quantitative skill as well as general knowledge of business and quick thinking.

Most students who prepare for consulting recruiting practice anywhere between 30 and 60 cases before they are ready for the final-round interviews. A key challenge in case preparation is distilling consistent, actionable feedback from practice interviews with a wide variety of interviewers and case types. Often, there is a high level of variance in casing, and it can feel like each case interview is a shot in the dark, with some practice interviews going really well and others being a disaster. As a result, I leaned heavily on AI’s ability to pull insight from large amounts of unstructured data like interview transcripts.

After my first 5 cases, I started recording transcripts of my practice interviews and uploading them to a custom GPT I created to evaluate my casing performance. Through this, I was able to pull key insights from my performance over time. While this approach had its limitations, I was able to quickly speed up my casing ability from novice to intermediate. By the latter half of my casing journey, I no longer needed this AI approach, but it was incredibly beneficial early on.

3. What stood out about your experience in the AI-enabled classroom?

I appreciated Professor Scott Dyreng’s emphasis on human interaction through this classroom. In the future, I believe AI will de-emphasize the less human elements of work and force us to focus on the more human elements. Scott’s approach reflected that perspective. The approach didn’t treat AI like some menacing threat or all-knowing assistant; it was just simply a useful tool in the learning process.

4. What did you learn from the custom feedback provided to your team? Did it change your team’s approach to collaboration or overall dynamic?

Most of us on the team already knew each other, and we had good rapport from the start. We didn’t take the AI tool too seriously when we were discussing, so our discussions didn’t feel affected or altered as a result.

The biggest effect it had was the fact that we had to meet synchronously twice a week, which is a big challenge in the MBA program, where each class involves teamwork with a different team. We knew that we would be held accountable based on our submitted recordings, so it forced us to prioritize synchronous meetings far more than any other class I have been in. In terms of specific feedback from the AI, the most valuable data was meeting participation. It allowed me to see exactly how much I was talking during our meetings, and I was able to adjust my input accordingly.

5. What’s a capability of AI that you think will be most valuable in your career, and how are you actively building the skills to use it well?

Right now, I think AI is still in its infancy with regard to professional utility. Some functions like coding have seen incredible advances as a result of heavy post-training and harness engineering for foundational models, but I don’t see that as much in other industries or functions yet. I’m currently using it to perform research on industries that I want to learn more about, but I’m finding that it’s useful mainly for early knowledge building. If I want to learn about something at a very deep level, I still need to invest significant time and energy to read and think. I guess it goes back to the idea that AI is still just a tool, albeit an extremely powerful one. In the end, humans still need to understand what is going on for the tool to be useful.