Generative artificial intelligence (generative AI) is changing how we teach and how we learn. What we want our students to learn – the core knowledge, skills and values of our disciplines – guide how we craft our curriculum and shape our pedagogical approaches. As educators we have long adapted what and how students learn to changing technology, changes in our disciplinary knowledge, and changes to the context of the University. We care about our students and about what and how they learn.
The capabilities of generative AI to produce coherent, logical and reflective text – as well as images, code, audio and video – invite new, and sudden, change to teaching and learning here at McMaster and around the world. How we respond to this change – if we respond – is a personal question, and an institutional one.
While many institutions and organizations are offering guidebooks, webinars and resources for adapting teaching methods and materials to address this rapid shift, the truth is we simply don’t yet know the scale of change required. Will you want to adapt a single assessment? Will we need to rethink the core learning outcomes for a program? Will we need to reconsider the purpose of a post-secondary degree?
Media reports traverse the spectrum of panacea to catastrophe; conversations with colleagues and students here at McMaster mirror this breadth. Our individual reactions are shaped by our disciplinary backgrounds, our experience with generative AI and our teaching philosophies.
To say that any one guidebook – like this – can prepare you to teach amid the changes brought and coming by generative AI is foolish. We write this guidebook knowing some of its content will be obsolete in months. We wanted examples – so many examples – that we just do not have yet to offer (please: send us your examples!). We wanted to provide clear, simple and actionable advice for how to adjust your courses and your teaching methods, but ran up against the reality of idiosyncratic courses with unique assessments that each require slightly different guidance.
We offer this guidebook recognizing its limits. It aims to ground you in what generative AI is and what it might mean for student learning and for your teaching here at McMaster. It explores some of the ethical questions you may already be grappling with and invites you to share those we haven’t yet considered. It offers specific advice for redesigning assessments and for how you might explore the use of generative AI in your teaching. It tries wherever possible to be clear about what we don’t yet know, but are trying to answer.
As authors we are educational developers, educators, researchers and students. We write this for you as colleagues and hope you will share with us your reactions, questions and suggestions. This guidebook will be updated – it will have to be updated – and we want to hear from you where we need to do more. You can reach us at email@example.com or through our website.