At a recent academic retreat I attended, the air was thick with what I can only call educational gaslighting. A panel of graduate and undergraduate students looked a room full of professors in the eye and claimed they only used AI to verify their work because they valued learning too much to take shortcuts. Minutes later, when the answers were blind, those same students estimated that over 80% of their peers were using the technology for nearly everything.

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. 

But how do we help students become experts if they don’t show up? 

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. 

By evolving my courses to embrace this data, I’ve seen attendance surge, even in the freeze of a Michigan winter. 

Flipping the lecture cycle

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. 

To break this cycle, I’ve flipped my classroom. Each week, I assign a 2-hour recorded video lecture, along with a related article. The assignments are made in Perusall, an AI-enabled tool that treats the video and article a bit like a social network. Students are graded based on their active engagement with the material, such as how much of the lecture they view, what questions and comments they leave in the system, and so on. I can monitor which students leave comments, answer peer questions, and engage with the material before they ever set foot in my classroom. And if they try to cut and paste comments in multiple locations, the system flags them. It does not yet flag comments that seem AI-generated, but I expect that will be coming soon.

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.

The result? My students show up to class because the value is no longer in the information I provide—it's in the friction and growth of live exchange.

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.

By moving the passive learning to the home and continuously pushing students to test their knowledge, I’ve reclaimed the classroom to create what AI cannot replicate: spontaneous, high-stakes human interaction.

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.

As someone at the forefront of AI research and creation, I don’t coach my students to avoid it, but rather I use it as a sophisticated, one-on-one tutor that facilitates active learning and helps them grow their expertise. This means moving beyond passive consumption and toward a rigorous, iterative process of trial, error, and refinement.

Some best practices I share with my students include:

Mastery-First Workflow: Solve problems manually first. Then use AI to check your work and identify where your logic diverges from the model.

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.

Brain Dump Standard: Never ask AI to write from scratch. Instead, provide a brain dump of ideas and structure. After the AI helps organize your expertise, personally refine it through meticulous review or even rewrite, if necessary.

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. 

Amid the promise of AI to supercharge the work of experts, we must treat this technology with the same proactive mastery we apply to any other essential tool of modern life. 

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.

There is no epidemic of noncitizens voting in our elections. Federal law already bars it, and despite repeated allegations by a number of prominent conservative figures, hunts for violators have turned up next to nothing.

Yet President Donald Trump continues to aggressively push for passage of his SAVE Act, a bill that would require every American to show a passport or birth certificate in person just to register to vote, effectively ending online and mail-in registration. Framed as a fraud-prevention measure, I expect the bill would ultimately do far more to keep eligible citizens off voter rolls than to stop alleged noncitizen voting. And then there’s what Trump expects: he has said that the SAVE Act would “guarantee the midterms” for Republicans, who, he’s promised, “will not lose an election for 100 years.”

But the SAVE Act should scare voters across the political spectrum. Free and fair elections are the cornerstone of our democracy. They are the primary mechanism by which we select our leaders and hold them accountable. When any politician—whose access to power depends on election outcomes—repeatedly casts doubt on election integrity without evidence, we must see it as an attempt to undermine our democracy and push back hard.

This is why I see the passage of the SAVE Act as the equivalent of a four-alarm fire for our democracy. Not only could the bill make it impossible for millions of Americans to exercise their right to vote, but it would also hand the federal government unprecedented new powers over election administration and voter data, powers that have always belonged to the states. Such is the potential for mass disenfranchisement and abuses of power that even members of Trump’s own party have decried the bill as “a mistake.”

And yet the SAVE Act is only one front of the attack on our democracy. There are other examples of disturbing ways that the Trump Administration is fearmongering about immigrants, effectively sowing mistrust in our electoral systems.

Since May 2025, the Department of Justice has demanded that nearly all states and the District of Columbia turn over full, unredacted voter rolls, including driver’s license and partial Social Security numbers. When most of those states refused, DOJ filed lawsuits against 30 of them and D.C. For the states that did provide the data, DOJ then shared it with the Department of Homeland Security to supposedly “scrub aliens from voter rolls.”

Since then, the pressure has only escalated. In July, a day after Trump gave a primetime speech in which he again railed against immigrants, made unsubstantiated claims of noncitizen voting and demanded that states change their election policies, Homeland Security Sec. Markwayne Mullin then threatened state election officials with prison time if they don’t acquiesce to Trump’s demands.

The intimidation isn’t just limited to officials; it’s also aimed at voters. Trump’s longtime advisor Steve Bannon has openly called to “have ICE surround the polls” on Election Day and recently brushed off criticism of that proposal, saying that, “it should be intimidating.” When Trump himself was directly asked if he planned to deploy Immigration and Customs Enforcement (ICE) and Customs and Border Protection (CBP) to polling stations, he equivocated, stating that he would do “anything necessary to make sure we have honest elections.”

Both federal and state laws prohibit the deployment of federal agents at polling places. However, we have already seen what fear does to a community under ICE presence. If people in Minneapolis, Chicago, and Los Angeles were too afraid to venture out for basic needs like groceries or a doctor’s visit rather than risk an encounter, what should we expect with the looming threats of a militarized presence around elections? How many people will stay home rather than risk being racially profiled and brutalized?

It is ironic that this is all unfolding in the year of America’s 250th birthday. And it begs the question: Are the actions of this administration consistent with the basic tenets of the Declaration of Independence? It is clear to me that they are not. 

By demonizing immigrants, the Trump Administration has endangered due process, the balance of powers, and the Constitution. Now, it’s using similar fear tactics to come for the very thing that makes self-government possible at all: elections we can trust.

The stakes here outlast any single midterm. Whether our democracy survives this depends on our collective ability to recognize the pattern before it’s finished—and to stop it while we still can.

And it depends on our ability to identify if we actually have something to be afraid of.