AI Basics for Teaching and Learning Leaders
You May Never Be an Expert (and that's fine)
In our experience, you don’t need to be an AI expert to lead a response to, and plan for, AI in teaching and learning. But you do need to know some basics, and know what you don’t know (and who to ask). This section is for teaching and learning leaders who haven’t – until now – learned what they might like about generative AI. If you already have a solid understanding, skip ahead.
It’s an odd experience for a teaching and learning leader to be expected – seemingly overnight – to be an AI expert simply because AI – again, seemingly overnight – transformed the what, why and how of teaching and learning. While you may have known a lot or a little about educational technology or digital pedagogy, few and far between are the teaching and learning leaders who are also AI experts.
So we witnessed a few approaches from teaching and learning leaders in learning about AI:
- Deferring questions about AI use to disciplinary experts (faculty in computer science or the IT department)
- Offering some training on AI tools to our staff and taking it ourselves
- Dedicating one or two staff members to lead learning about AI
- Leading the institutional response to AI when it comes to teaching and learning
- (Hoping it might pass)
These approaches are sensible. Most of us operate in highly resource constrained environments where we simply do not have the time or money to stop everything we’re doing to have our full staff – or ourselves – learn enough about AI to confidently lead institutional responses and planning.
And yet.
AI is already transforming teaching and learning at your institution, whether you have formal responses in place or not. Students are using these tools in their coursework, faculty are struggling with questions about assessment and academic integrity, and unevenly adopted institutional guidance and/or resources creates confusion and inconsistency. If you haven’t taken up leadership yet, AI requires your immediate attention – not because of external pressure to adopt AI, but because your community needs leadership to navigate the changes already underway.
What we’ll do in this section is point you to the places where you can review good materials quickly in a way that will support you in what you need to know about generative AI to lead. You’ll need 4-5 hours and it’s okay to make the time for this: it matters.
The following online resources will introduce you to some of the basic ideas behind generative AI and its implications for teaching and learning. Complete one or two of them, and move on to the next chapter when you are able to confidently explain to another leadership colleague:
- Differences among machine learning, generative artificial intelligence, large language models, diffusion models, foundational models, agential AI and reasoning models.
- The common capabilities of generative AI tools across a range of disciplines
- The risks generative AI tools present to society at large, and post-secondary institutions specifically, including data security/privacy, environmental impact, labour disruption, dis/misinformation, ‘hallucinations’, copyright, discrimination and bias.
- Use cases or examples of how generative AI can be used by educators in the administrative work of teaching and learning and use cases or examples of how generative AI can be used by students to support their learning.
Here are some options to read, watch or listen to:
General Introductions
- CIFAR (Canadian Institute for Advanced Research) has two great online courses
- Destination AI introduces AI for anyone – the broad audience is ‘the public’
- Indigenous Perspectives on AI
- This free certificate-granting course teaches AI trainees about Indigenous knowledge-keeping and worldviews. Self-paced for independent learning, it takes about 2.5 hours to complete online.
- Ethan Mollick is an accessible writer for a general audience, with a specific focus on the post-secondary environment (as he is a professor at the Wharton School of Business at the The University of Pennsylvania). He has several great pieces for helping you achieve these foundational goals:
- Co-Intelligence: Living and Working with AI: An introductory book that meets most of the foundational aims for this chapter – defining terms, capabilities and risks
- One Useful Thing newsletter: It’s written for a general audience with lots of practical and accessible examples and keeps you current with lots of changing information.
- Artificial Intelligence (AI) Education for Teachers: Coursera course offered by Dr. Anne Forbes and Dr. Markus Powling from Macquarie University.
- Care About AI: Introducing Artificial Intelligence from CARE-AI at the University of Guelph, is an introductory, self-paced course for learning about AI fundamentals, ethics, and impact on the broader society. Use the code TWAI2025 to the relevant field on the registration form.
Teaching and Learning Specific Introductions
- AI for Educators by the University of Sydney offers an overview both of the technology and many practical examples of AI use for educators – along with a companion site for students, built by students
- Harvard’s AI Pedagogy Project: A general introduction and a place to experiment (and a thorough collection of curated resources)
- Experiments with AI for teaching and learning – challenges and guides for using AI for teaching and learning from UBC
- More Ethan Mollick:
- Assigning AI: Seven Approaches for Students, With Prompts: A paper that reviews seven ways faculty can use AI with their students in learning enhancing ways.
