Teaching · Rachel McBride

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How we built an undergraduate AI paper in NZ

An account

What can we teach undergraduates in November that won't have changed in March?

My foundations

• Understand NZ business, and what is happening with AI.
• Have four teenagers in the NZ education system.
• Deeply worried and excited. All the time.
• Very, very patient.

A new undergraduate paper takes years. Proposal, committees, revisions, board, timetabling, CUAP. Machinery that exists for good reasons. But moves at machinery speed. AI is changing business, hourly. If I follow the normal path then a course developed in 2026 would be live in 2028.

Instead, I ask, what can we teach undergraduates in November that won't have changed in March?

Our answer became BSNS 350, The AI Room: business decision-making where AI is the context, not the subject. Built through Otago University's fast lane, the special topic, a paper that runs while it earns the right to become permanent. Approved in June. First class in November. Five weeks of summer school, delivered by distance.

I bring what NZ businesses are doing this year, and what is happening at the frontier.

Otago academics make sure it holds as scholarship, teaching first principles, critical thought, curiosity and a lust for learning.

Together we built a paper for students entering those businesses.

Start with a position

The course opens with each student forming a position on AI and putting their name to it. When the world is changing more than Auckland weather, what do I actually think? What would I let a machine decide for me?

Your position may be different from your family, your workplace or your boss. It can change. Five good weeks with me should change it. You write down your position at the start of the course, and then again at the end after a panel of NZ employers have told the class what they want from graduates. We assess the students' view on what has changed and why.

Put NZ practice in the room

Every week at least one NZ business is in the room, live, unscripted. Not case studies from Silicon Valley. Where are they with AI? What did they build? What value did they create? How do they measure success? We talk about what AI does to their own people and we look at it through the lens of Māori data sovereignty.

I ask questions. The students ask questions. The answers differ from one leader to the next, they may contradict. No one has all the answers. We focus on asking better questions.

Students watch NZ business work it out in real time.

Let students build

AI is encouraged in every assessment. Students form a leadership team around a real case and build: analysis, workflows, working dashboards. We want to see their work, how they knew when to iterate (or not), and when they said no to what AI gave them. What other problems can you see in the business? How could you build something else to solve that problem?

There is no tools training in this paper.

But what they learn in November is still true in March: two questions.

What do you think about AI?

What have you built? Today.

Ask your teenagers at dinner tonight. Then answer both yourself.