Walking through the Martha and Spencer Love School of Business this past spring, you’d have found a fascinating reversal of the typical classroom dynamic. Instead of professors lecturing students, it was often the students leading the way, teaching their peers and even faculty how to harness artificial intelligence. This was the work of Elon University’s AI Student Fellows, a pioneering program that spent the spring 2026 semester embedding undergraduate experts directly into the fabric of business education.
The initiative paired students with faculty members on projects designed to build AI fluency across the school. The scope was deliberately broad, ranging from introductory workshops for first-year students to advanced resources for specialized courses. The core idea was simple yet powerful: leverage the unique perspective of students who are digital natives to demystify AI for everyone.
For Sakura Kawakami ’26, a Business Analytics major from New Zealand, the role was about translation. “My job was to act as a bridge,” she explained. Working with Assistant Teaching Professor Scott Oakes and two other fellows, her team visited eight Gateway classes, reaching about 240 students with compact, 20-minute sessions. They didn’t just talk about AI; they had students open Gemini Pro, Elon’s chosen platform, and start typing.
The primary lesson wasn’t about which button to click. It was about mindset. “One of our biggest teaching points was the importance of iteration,” Kawakami noted. They challenged the notion that you ask an AI a question and accept the first answer. Through hands-on exercises, students learned that a good output requires a refined input, that adding context and clarity to a prompt transforms generic responses into useful tools. Professor Oakes saw immediate value in this peer-led model. “First-year students often feel more comfortable asking questions and exploring new technologies when they see successful upper-level students demonstrating how they actually use those tools,” he said.
This foundational work was crucial, but the program’s ambition stretched further. Another team, working with Associate Professor Kem Zhang, tackled a more complex challenge: moving students from being AI users to AI builders. Thomas Case ’26 and Oliver Lorraine ’27 focused on supporting specific courses, like Associate Professor David Jiang’s “Bring the Venture to Life” entrepreneurship class.
Their task was to design an “AI-powered entrepreneurship” workshop. This wasn’t about writing a better email; it was about using AI to:
- Generate code for an e-commerce site
- Integrate payment systems
- Deploy a functional prototype
- Create lasting coding templates
- Build reusable resources in Google Colab notebooks
- Support future cohorts in entrepreneurship
Case led the workshop design, drafting materials that broke down these technical tasks. Lorraine built something lasting: reusable coding templates in Google Colab notebooks, creating a scalable resource for future cohorts. Professor Zhang framed this shift as the new competitive edge in business. “Knowing how to use an AI tool is expected,” he stated. “The real advantage comes when they understand how these tools can be used in problem solving and to create concrete, AI-based solutions.” Case echoed this, observing the critical “transition from AI users to AI architects,” where the tool becomes a partner in innovation, not just a convenience.
The program’s success is measured not just in surveys but in its sustainability. The entrepreneurship workshop, the AI Boot Camp for incoming students, and other resources developed this spring are slated for continued use in the 2026-27 academic year. The fellows themselves, ten students from various majors and class years, became a living repository of practical AI knowledge within the business school.
What makes this initiative stand out is its authentic, bottom-up approach to educational change. It acknowledges that expertise in a fast-moving field like AI isn’t confined to tenure-track faculty. By formalizing the role of student fellows, Elon created a feedback loop where pedagogical goals meet real-world, student-level practice. The students learned to articulate and teach complex concepts, the faculty gained invaluable partners in curriculum development, and every first-year in those workshops left with a far more sophisticated—and less intimidating—view of what AI can do.
In the end, the program didn’t just teach AI skills; it modeled a collaborative, adaptive way of learning that might be the most important business lesson of all.
