When generative AI tools like ChatGPT exploded onto the scene, many educators felt a mix of excitement and concern. For me, teaching MGSC 291, the second required business statistics course at the Darla Moore School of Business, it was clear: this technology wasn’t just a trend—it was a turning point.

We serve over 1,500 students a year, most of whom come in with only a basic stats course under their belt—one that uses Excel and covers simple linear regression. In MGSC 291, we take things further. We introduce R, a powerful scripting language that opens the door to advanced algorithms and real-world data analysis. But for many students, this leap is intimidating.

From Fear to Fluency

Imagine being handed a blinking cursor in R with no menus, no buttons—just code. For students who’ve never programmed before, it’s like being dropped into a foreign country without a map. Some are excited. Many are nervous. And when Gen AI tools became widely available, the temptation was real: why not just ask ChatGPT to do the homework?

I get it. Students are busy. They want to be efficient. But efficiency without understanding doesn’t prepare them for the workforce. That’s why this fall, we’re embracing Gen AI—not to replace learning, but to enhance it.

Teaching Students to Use Gen AI the Right Way

We’re not banning Gen AI. We’re teaching students how to use it responsibly and effectively. That means:

  • Understanding key AI terms like LLM, GPT, and RAG
  • Learning how to write effective prompts and avoid common pitfalls like multi-turn drift.
  • Using Gen AI to diagnose R errors, explore statistical concepts, and brainstorm approaches—not to copy-paste solutions

We’re also talking openly about hallucinations (AI-generated misinformation), ethics, and the importance of verifying outputs. “Vibe coding” might sound cool, but it only works if you understand the fundamentals.

Building Career-Ready Skills

The truth is, Gen AI is already in the workplace. Job postings increasingly ask for candidates who can leverage AI tools while maintaining accuracy, efficiency, and ethical standards. By integrating Gen AI into our course, we’re helping students build skills they can take straight to their internships and jobs.

We’re also collecting student prompts and feedback to analyze how they’re using Gen AI. What kinds of questions do they ask? Where do they struggle? What helps them learn? This data will guide future iterations of the course—and I’ll be sharing those insights in a follow-up post.

What’s Next?

This is just the beginning. In my next blog post, I’ll share what we’re learning from student prompt patterns, how Gen AI is impacting engagement and understanding, and what surprises (good and bad) we’ve encountered along the way.

Another exciting shift we’re making soon is moving away from teaching the algorithms is R and instead, focusing on pseudo-code thinking—teaching students to understand the logic and structure of algorithms without being tied to a specific syntax. This way, when they are ready to vibe-code they will be equipped to evaluate the outputs in any language.

So stay tuned—and if you’re an educator, student, or employer curious about how Gen AI is reshaping business education, I’d love to hear from you.

Let’s prepare the next generation of business leaders to use AI not just wisely—but brilliantly.