Getting Started with AI in Tertiary Teaching

Created by Graeme Smith and Liza Kohunui

🪻 Section 1: Welcome and Orientation

Nau mai, haere mai e hoa.

This module marks the beginning of your journey with artificial intelligence (AI) — not to chase hype, but to walk wisely: with care for your learners, your values, and your own confidence. You are not behind. You are arriving — and that matters. This course is part of AI for Good, a framework for growing professional practice that centres AI fluency in ako, whanaungatanga, and mana-enhancing learning, while building the confidence, capability, and shared responsibility we need for ethical AI use. As we explore AI together, our goal is not technical perfection but cultural integrity, practical empowerment, and a transparent, inclusive approach that helps educators guide learners with trust and creativity in a changing world.

“You don’t need to become an AI expert. But you do deserve to feel confident, curious, and in control.”

🪶 Kaupapa Māori Principle — Honouring Te Tiriti in Your AI Practice

Throughout this module, you’ll find kaupapa Māori deep dives — optional sections that explore AI through Te Ao Māori. These are offered to all educators as invitations to:

• Ground your practice in Aotearoa’s cultural context
• Consider the implications of AI for Māori learners and communities
• Develop culturally responsive approaches to emerging technology

You don’t need to be Māori to engage with these sections — they’re written for everyone who seeks to honour Te Tiriti o Waitangi and to strengthen equity, inclusion, and shared empowerment across all learning spaces. Look for the 🪶 symbol throughout to discover these perspectives.

Knowledge check

What’s true about large language models?

1. You get the exact same response every time.
2. It does things perfectly, great at adding numbers, produce working code etc.
3. The response may vary despite using the same prompt. It’s also great at giving you a first draft of something, be it text or code. But you need to improve on the results.

A: 3, an LLM is non-deterministic, the response varies, however, you can control its variance via a temperature setting. You also shouldn’t expect it to do things perfectly, it’s here to do the heavy-lifting for you which often means you get a good first attempt at something that you need to gradually improve.

Great Work! Continue the Journey

After completing this lesson, check out our Generative AI Learning collection to continue leveling up your Generative AI knowledge!