Our findings show that students from different disciplines use AI in different ways. Any solutions should be discipline-specific or course-specific.
One response that is currently gaining traction is to move all assessments to a controlled environment. For example, a proctored oral exam or an in-class handwritten exam.
The problem is that this type of assessment only targets a limited group of skills that can be tested in a time-controlled environment. Universities, especially research universities, teach students a much wider range of skills. And some of those skills require long-term engagement with the material.
Going back and forth, writing and coding, or just struggling intellectually is part of how you learn.
If you limit your assessment to narrow settings and very short time frames, you may miss what you are actually trying to teach your students.
You conclude that the solution to AI-based fraud is not a comprehensive university-wide policy. Each department must develop its own policy. How did you come to that conclusion?
One of the problems many teachers face is that GenAI is not easy to detect. You may think that your students’ work is being done by AI, but that may not be the case. Additionally, even if you discover the use of AI, you may end up spending more time proving it, as evidence is easier to collect than in plagiarism cases.
As AI detection software evolves, there are new detectors that can better detect AI-generated text, but there are also AI humanizers (services that make text or code appear to be written by a human), so it’s a cat-and-mouse game. Therefore, it will be a never-ending battle.
Another solution is to ban AI altogether. This is not a productive solution. Students will continue to utilize AI. Some people use it to learn better in a course, to explain content, or to ask difficult questions to the instructor.
Therefore, it is difficult for universities to stay away from the wave of AI adoption and they need to teach students how to use AI. However, what responsible use of AI looks like varies by sector. Writing, coding, problem solving, lab work, and creative work all pose different problems. So a blanket ban probably won’t work.
The study found disparities in the use of generative AI by demographic group members, with low-income, racially underrepresented, and female students less likely to use generative AI. Why is that a concern?
This is even more important than the cheating part. There is less systematic evidence regarding disparities in students’ use of AI tools.
One thing that stands out is the socio-economic and racial disparities in the use of AI, which I think will likely worsen as newer, more expensive models become available.
My concern is that students from wealthier families have access to advanced AI tools with more powerful features and fewer usage restrictions. But students without resources may be limited to clunky and limited free AI tools.
Many employers are interested in graduates with experience using AI tools. Students from higher socio-economic backgrounds may have an advantage not necessarily due to their skills, but rather in being able to pay for those tools. That is a very important element of this study.
Why do you think these findings are important for students?
Using AI for learning can have negative effects. You may create a polished product for your class and even get a good grade, but you won’t develop the skills you were intended to build in the assignment.
There are several experimental studies showing that learning with AI is significantly less efficient and results in less durable skills than without AI. Many students are highly misinformed about how good or bad they are in certain areas and may lack investment in fundamental skills.
And we don’t know what the future holds for AI in the workplace. In such an uncertain world, it is important to understand how AI is impacting education.
I encourage my students to stop and ask themselves questions when using AI. “Can you explain this without tools? Could you do a similar task on your own tomorrow? Did AI help you understand the material better? Or did it primarily help you finish faster?” These simple questions can help students track whether AI is supporting their learning or replacing it.
But it’s challenging. I really care about my students.
Why are your research results important to the university?
There was already a crisis of trust in higher education long before AI. But AI creates another point of criticism for universities. Are universities up to their mission of teaching and assessing student skills in the age of AI?
How a university responds to its challenges shapes public trust in the university. Because when all students achieve excellent grades, it becomes difficult to trust their credentials.
A number of important efforts are already underway by universities to address the impact of AI, including the University of California, Berkeley. However, the evidence in our paper shows that these efforts require more resources and higher priority.
This interview has been edited for length and clarity.
