There will be no take-home essays in Stuart Chambers\u2019 university courses\u00a0this fall.<\/p>\n
Tired of trying to detect AI usage, the University of Ottawa sociology professor is replacing the traditional means of assessment with an in-person 75-minute handwritten paper. \u201cI was becoming too much of a police officer and less of a professor. I want to go back to being a professor.\u201d<\/p>\n
As educational institutions grapple with how to harness AI ethically without sacrificing critical thinking, classrooms have become petri dishes for a period of intense experimentation in teaching and assessment.<\/p>\n
\u201cOur credibility as a university is dependent on how accurately we can assess grades,\u201d says Chambers. \u201cAnd if we\u2019re not accurate in assessing grades, the diploma is worthless.\u201d<\/p>\n
Here\u2019s how five Ontario professors are transforming their practice.<\/p>\n
Roll of the dice<\/p>\n
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Kyle Smith, a professor in the history of religions program at University of Toronto, has returned to the oral exam in light of increasing use of AI by students.<\/p>\n
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For the first 15 minutes of each class, University of Toronto Prof. Kyle Smith conducts what he calls \u201cproof of human thinking.\u201d<\/p>\n
He hands out a question, typed on paper, based on that week\u2019s reading materials. He recognizes students may use AI to study, but once in his lecture hall, there\u2019s no hiding behind a screen. Students \u201chave to show up in class and, without any sort of device other than a pencil, be able to respond in a coherent way.\u201d<\/p>\n
Instead of banning AI or grudgingly tolerating it, the history of religions professor has redesigned how he measures student understanding. \u201cA lot of colleagues maybe still have their heads in the sand and think we can do the same things,\u201d he says, but AI is \u201cimpacting post-secondary education, particularly in the humanities, in ways that are unwelcome.\u201d<\/p>\n
Instead of a written final, Smith now conducts a \u201cviva voce,\u201d an oral exam in which students meet with him and roll a 12-sided die three times, randomly selecting three of the course\u2019s 12 weeks of topics for discussion. Students must also pose one hard question to Smith to demonstrate they have synthesized the material.<\/p>\n
\u201cThe vast majority of students have never had this sort of assessment,\u201d says Smith. \u201cThe pressure to perform live is a little scary \u2026 but the feedback I have consistently gotten is, \u2018I was really worried about this; it turned out I was super-prepared, eager for the conversation and it went really well.\u2019 \u201d<\/p>\n
That final is worth 20 per cent; the in-class questions a total of 40. The remaining grade consists of two multilayered, sophisticated take-home assignments in which AI is not just permitted but potentially necessary, enabling students to create such things as a religious shrine using digital tools.<\/p>\n
But as AI improves, the balance between human and machine keeps shifting: \u201cI\u2019m reacting to the changes I\u2019m seeing,\u201d Smith says, noting he may need to pivot again this fall.\u00a0<\/p>\n
Positive peer pressure<\/p>\n
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Sharon Ferguson, assistant professor in the management science and engineering department at the University of Waterloo, found that having students do group projects resulted in less AI-generated content.<\/p>\n
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When Sharon Ferguson put her first-year students into randomized groups for projects, she noticed something striking: their finished work contained almost no AI-generated content.<\/p>\n
Not because they couldn\u2019t use it. But students who might have happily thrown the assignment into a chatbot found themselves working alongside peers who wanted to do their own thinking. The group kept itself honest.<\/p>\n
The result confirmed something Ferguson, an assistant professor in management science and engineering at the University of Waterloo, has already built into her practice: the most effective response to AI isn\u2019t restriction, it\u2019s design.<\/p>\n
\u201cA large majority of our students go into tech, and the philosophy in most tech companies is use as much AI as you can, gain as much efficiency as you possibly can with this tool \u2026 So, it doesn\u2019t make sense to say you absolutely cannot use it.\u201d<\/p>\n
As a researcher of human-AI interaction and in her second year of teaching, Ferguson has always inherently thought about generative AI. Her approach is to teach responsible and effective use while demanding transparency, asking students to sign a declaration and show their chat history. She has also built a grading rubric tied to the exact terminology and concept applications she uses in lectures, making generic AI work easier to spot. And she introduced frequent quizzes with thought-provoking questions that can\u2019t just be memorized.<\/p>\n
In foundational year courses, she still does as many in-person, hands-on activities as possible: \u201cIf we never let them become experts because they\u2019re never learning, because they are just using AI all of the time, then that\u2019s a very dangerous future.\u201d<\/p>\n
Plugging the holes<\/p>\n
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Rahul Kumar, associate professor in the department of educational studies at Brock University, has started interviewing students as a way to better assess student knowledge in the age of generative AI.\u00a0<\/p>\n
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Rahul Kumar is teaching the teachers. And there\u2019s great responsibility in that.<\/p>\n
\u201cWithout themselves knowing (AI), how can they guide their students?\u201d says the associate professor of educational studies at Brock University.<\/p>\n