As an engineering professor at the University of Michigan, I believe we need to move past the fear and hype. The future job market will not be dominated by autonomous AI, but by experts who have mastered their field so thoroughly that they can use it to multiply their output exponentially.
This question precedes the LLM onslaught. Since the pandemic, traditional lecture attendance has cratered, but active learning has been shown to significantly improve both turnout and long-term retention.
Classically, engineering courses default to hours of lectures where students are expected to take notes, with problem sets and exams bolted on. Many students treat a lecture as passive entertainment. And, often, the material is so technical that it is disconnected from real-world use, leading to even less engagement and retention.
With everyone primed to dig in, only one-third of my students’ time with me is devoted to classic lecturing. I offer a one-hour live lecture and invite industry guests to share stories of computer vision in the wild. Then my students spend the rest of our in-person time participating in small breakout sessions, a large group discussion, and an in-person quiz. Not only do they grade their own quizzes, but they only get credit for an answer if one of them argues the logic behind it.
This fall, I’m taking this a step further. We won’t just read technical papers; we will debate them. Anyone can be called to the front of the room to spontaneously argue one side of a research argument, which means every student must come prepared.
Using AI as a supercharged tutor
As we try to understand how AI can help and hinder learning, the most dangerous misconception is that it is a labor-saving device for the mind. In reality, AI is an expertise-amplifier that can turn weeks of manual programming into a few hours of focused work. But for a novice, relying on AI before mastering the fundamentals creates a technical debt that leads to a lack of depth.
Some best practices I share with my students include:
AI as a Problem-Generator: One of the most effective ways to learn is through constant testing. Use AI to generate new practice problems and engage in active learning.
Redefining the honor code in the age of AI
I am not an AI police officer. I cannot—and should not—spend my academic career hunting for digital shortcuts in my students’ work. I can only set the boundaries and allow them to choose how they show up.
By shifting the focus to high-stakes, spontaneous human interaction and leveraging AI for active learning rather than trusting it to do the work, educators can ensure that the knowledge lives within the student, not just the model.
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