The largest study of AI use by undergrads is in, revealing disparities in access — and in cheating

From our findings, we saw that students in different disciplines used AI differently. Any solution should be discipline-specific, or maybe even course-specific.

One response that is gaining traction right now is to move all assessments into controlled environments. For example, proctored oral exams or hand-written in-class exams.  

The problem is, those types of assessments only cover a narrow group of skills that can be tested in a time-controlled environment. Universities, and especially research universities, teach students a much broader set of skills. And some of those skills require prolonged engagement with material. 

Going back and forth, writing or coding or just struggling intellectually — that’s part of how you learn. 

If we limit our assessments only to those narrow settings and very short time frames, then we may lose out on what we are actually trying to teach students.

You conclude that the solution to cheating with AI is not blanket policies across universities. Departments need to develop their own policies. Why did you come to that conclusion? 

One of the issues that a lot of faculty are running into is that it’s not easy to detect GenAI. Sometimes you think student work is by AI, but it may not be. And even if you detect AI use, you may spend a lot of time trying to prove that compared to plagiarism cases, where there’s evidence that is easier to collect.

AI detection software is evolving, so there are newer detectors that are able to find AI-generated text better, but it’s a cat-and-mouse game, because at the same time, there are AI humanizers, services that allow you to make your text or code look human-written. So it will be a never-ending battle. 

Another solution is to ban AI throughout. This is not a productive solution. Students will continue using AI. Some use it in their courses to learn better, to explain material and to ask questions that they would not be comfortable asking their faculty. 

So it’s challenging for universities to stay away from that wave of AI adoption, and they need to teach students to use AI. But what responsible AI use looks like is different in different fields — writing, coding, problem-solving, lab work and creative work all raise different questions. For that reason, a blanket ban probably wouldn’t work.

The study found disparities in the use of generative AI by different members of demographic groups, with low-income, racially underrepresented and female students less likely to use it. Why is that concerning?

This is even more important than the cheating part. We have not had much systematic evidence on disparities in students’ use of AI tools.

One thing that stood out is socioeconomic and racial disparities in AI use, and that, I think, will probably become worse as newer, more expensive models become available. 

My concern is that students from wealthier families can access advanced AI tools with stronger capabilities and fewer usage limits. But students who don’t have resources may only be able to use free AI tools that are clunkier and have limits.

A lot of employers are interested in graduates who have experience working with AI tools. Students from higher socioeconomic backgrounds may get an advantage that’s not necessarily about their skills, but rather about the ability to pay for those tools. That’s a very important component of this study. 

Why do you think that these findings should matter to students?

Using AI in your learning can do a trick on you. You may produce something polished for class, and may even get a good grade, but not develop the skill the assignment was meant to build. 

There are some experimental studies that show that people learn much worse with AI and don’t develop durable skills compared to learning without AI. A lot of students may just be very misinformed in terms of how good or bad they are in certain areas, and they may underinvest in some foundational skills. 

And we don’t know the future of AI in the workplace. In that kind of uncertain world, it’s important that you understand how AI is impacting your education.

I encourage students to pause and ask themselves when using AI: “Could I explain this without the tool? Could I do a similar task on my own tomorrow? Did AI help me understand the material better, or did it mainly help me finish faster?” Those simple questions can help students track whether AI is supporting their learning or replacing it.

But it’s challenging. I really feel for students. 

Why do your study’s findings matter for universities? 

There was already a crisis of trust in higher education long before AI arrived. But AI creates another point of critique of universities — are they up to the task of teaching and assessing student skills during the Age of AI? 

How universities respond to that challenge will shape people’s trust in them. Because if every student gets an excellent grade, it becomes harder to trust that credential.

There are already a lot of important efforts underway by universities to navigate the impacts of AI, including at UC Berkeley. But the evidence from our paper shows that these initiatives need more resources and higher prioritization.

This interview has been edited for length and clarity.