Teaching The Disruption #

In some ways, it’s already too late.

AI has already revolutionised the post-secondary classroom. Students are learning to write, code, and create with AI. Administrators are using it to generate meeting notes and automate spreadsheets. Professors are already using it to grade papers, build courses, and augment their research.

We’re not at the beginning of this change. It’s already happened. From computer science to medicine to drama—AI has redefined what is possible across so many different disciplines.

When I started at the University of Alberta, not too many years ago, it was a novelty to bring a laptop to a classroom. Most computer work was done in a dedicated computer lab. Then, something happened—by the end of my undergraduate degree the room was filled with laptops. It’s hard to imagine a classroom without them now. Many students I’ve talked to carry both their computer and their phone around all the time.

A similar adoption pattern is happening with AI. Early adopters are finding ways to use AI in their learning, research, and teaching. In a few years, everyone on campus will use AI every single day.

Universities faced a choice when those first laptops appeared in classrooms. They could ban computers from campus because of how they challenged traditional learning. Or, they could accept the new learning paradigm, provide critical infrastructure—such as ubiquitous wireless internet and electrical outlets on every desk—and empower students to integrate these digital devices into their own learning.

When it comes to AI, universities are already struggling to manage this change. Inconsistent classroom policies, inequitable access, and poor infrastructure are holding students back from using the technology of our generation to improve their learning outcomes.

It doesn’t have to be this way. We can imagine a different future which leverages ongoing improvements in AI.

Imagine a hypothetical AI system. This system is trained on your particular interests, expertise, and research in your field. It also has direct access to all of the relevant and accessible literature and findings from your top colleagues. You can interact with this system by exploring the collision of an idea with a new way of thinking. You can interact with it to better understand your results—and make informed hypotheses for future research. You might be thinking about interacting with this system as you would interact with a Google Search or Semantic Scholar, helping you to build reading lists for literature reviews.

Imagine a system with a rich understanding of not only your entire field of study but complimentary fields as well. This system has access to the combined knowledge output of every researcher who you can interact with, learn from, and work alongside as you deepen your own expertise—and contribute your own expertise.

Imagine a model that is tailored to your specific course work. A model that can be prompted like a personal tutor to help you learn the way you learn best. An interactive 24/7 study buddy that replaces scribbled, half-remembered notes with easy-to-access tailored information.

Imagine a model that simply navigates the university’s administrative system for you. That holds all of the required information about how to register for classes, schedule and build out courses, access services, and book meeting rooms on campus. A system that navigates institutional red tape in plain language with intuitive prompts.

These models might seem like far off dreams. But they’re just around the corner. And it’s time for universities to act—while they can still lead the way.

Universities have a rapidly closing window of opportunity to incorporate and develop new tools and systems—like the models I’ve speculated above. Already, organisations like Coursera and Khan Academy are building AI-first offerings designed to disrupt the traditional education experience. And, organisations such as OpenAI (A Student’s Guide to Writing with ChatGPT) and Google (AI in the Classroom with Irina Jurenka) are beginning to define best practices for AI use in the classroom.

Artificial intelligence, like the portable and handheld computers that came before, needs the support of universities. We need strong policies that guide ethical and informed use of the technology; such as requiring transparency on the use of generative AI for coursework. We need campus-wide infrastructure to support artificial intelligence in the classroom. The right tools, training and innovation to support the campus community. And we need research labs dedicated to interdisciplinary research on AI. Exploring how AI is changing—and will continue to change—every field of study.

The disruption of post-secondary education is already well underway. Now, it’s time for universities to play catch-up, build on early practices — for example: Using Generative AI and Teaching in the Context of AI from the University of Alberta — and harness the power of AI to reimagine the future of learning.