Few understand the nuances of TMU's relationship with AI - past, present and future - as deeply as Sean Kheraj.
As vice-provost, academic and co-chair of the university's generative AI task force, he is responsible for compiling a comprehensive report on how TMU should approach the rise of AI in the classroom and among staff and faculty. The task force, launched in collaboration by the vice-president, academic and vice-president, administration, reviewed data from surveys, meetings and virtual town halls in order to form its new recommendations report in summer 2026.
Kheraj shares the ethical and practical issues generative AI tools are raising for students and faculty alike.
Q&A:
Let's start with students. What kinds of conversations has the widespread use of AI platforms, such as ChatGPT, Gemini and other tools, prompted?
It comes down to academic integrity. Generative AI is disrupting the relationship between instructor and student, and challenging the ability to assess learning outcomes. If a student outsources an assignment to a third-party tool, the instructor cannot evaluate that student's actual knowledge and skills - meaning they cannot assign a grade to students' work that does not represent their own thinking.
What we really want is transparency. Students and faculty need to be open about how they are using AI tools. A key question for all of us is: when should we disclose that work was completed with machine assistance?
The task force is offering recommendations to faculty. What have you seen in terms of how instructors are using AI?
Examples include using AI to draft a course syllabus or build out lesson plans and in-class activities from existing course materials. That is generally seen as a positive use - it helps organize information more efficiently.
The trickier area is the use of third-party educational technology companies that offer marking and grading assistance. For many instructors and students, that is an absolute red line. Assessment is at the heart of learning, and using machine assistance there raises serious concerns.
What are the most under-recognized challenges of building a university-wide AI policy?
Every department sets its own agenda for how to introduce relevant AI skills to its discipline. Computer science programs will approach this very differently than, say, a film program examining AI's ethical dimensions and creative applications.
The key for any university is building a framework that is adaptable enough to span a broad spectrum of disciplines. To do that, you have to centre academic freedom within that framework. That is how you end up genuinely leveraging new technologies, rather than just reacting to them.
Fast facts
A course you wish you had taken as a student?
I did all of my undergraduate and graduate studies in history. I wish I had taken more! I would have loved to take another course in the philosophy of history and more courses on North American Indigenous history.
Early bird or night owl?
I'm more of an early bird than a night owl, but I tend to keep to a fairly consistent schedule. Sleep is important and I just got a pretty fancy new sleep mask (brag). I'm not a sleep maxxer, but I do love a good night's rest.
The last book you read that genuinely surprised you?
I don't know if this book exactly surprised me, but it challenged some ideas and presented history in a novel manner. The book is Fossil Capital: The Rise of Steam Power and the Roots of Global Warming by Andreas Malm. It's a fascinating book that re-examines the history of industrialization with a new perspective, centering the roles of energy and labour.
Favourite spot on campus to clear your head?
It's got to be the faculty and staff lounge in POD. I like having lunch here from time to time. And it has a nap room! Maybe I am a sleep maxxer.
