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As health informatics expert  introduces new artificial intelligence (AI) tools into her classrooms at 911����, she's also made a point of asking students what they think, both through formal research and casual conversation. 

In one early experiment, Avramovic designed AI avatars to present course material in short, on-demand videos students could watch at their own pace. Some students balked a little, finding the digital instructors monotone and robotic. They said they preferred a human. 

So Avramovic asked a follow-up question: Should she remove the videos? 

"They said, 'don't you dare,'" she recalled. "'They're excellent supplemental material. Leave them. But we want you to teach.'" 

That push and pull has become a central dynamic at the College of Public Health, as faculty respond and adapt to the undeniable rise of AI in higher education. The question is no longer whether AI belongs in the classroom, but how to use it in ways that actively benefit students. 

“The goal isn’t AI-free learning,” Avramovic said. “It’s AI-augmented thinking.” 

Learning to think with AI 

One challenge is persistent: How do you keep students learning when AI can provide answers in seconds? 

Research by Avramovic, assistant professor in the Department of Health Administration and Policy , and colleagues found that students can become overly dependent on AI-generated answers, reducing their engagement, interaction, and critical thinking. 

Rather than prohibiting AI use, faculty developed and tested AI tutors that function more like coaches than answer generators, walking students through assignments one step at a time. In order to build them, instructors map out common mistakes and teach the systems how to respond in ways that promote deeper learning. “It interviews students,” Hafeez said. “It compels them to think critically.” 

Early findings suggest this approach can help counter passive AI use, with students showing stronger performance and engagement than their peers. In one analysis, undergraduates using the tutors improved performance by nearly 9 percentage points. 

Tempering the trust 

Along with the enthusiasm for AI’s potential, faculty are just as focused on teaching students where the technology falls short. 

, an assistant professor in the Department of Global and Community Health, warns students against relying on it too early in the learning process, likening it to “using roller blades when you’re crawling.” 

In one global health course, Chee requires students to develop a 2,500-word narrative and a presentation proposing a public health intervention entirely on their own. Afterward, they use AI to generate a version of the same project, and compare the two. 

That exercise is often revealing. Many students walk away disappointed by the quality of the generated work, but with a sharper understanding of what their own thinking contributes, Chee said. 

, an associate professor in the School of Nursing, introduces AI early in the research process, using it to help students scan the literature and identify possible gaps worth investigating. But she draws a line between assistance and analysis. Students are expected to verify sources, refine their own research questions, and critically test whether AI summaries actually hold up. 

"I encourage AI to assist in some structural aspects of writing, like outlining and grammar, but not to author the work," she said. " AI can be a writing tool, but it cannot be an author." 

For Avramovic, the biggest concern is that students will trust AI too quickly. She recalled one student who relied heavily on AI while studying, learned flawed logic, and then repeated it on an exam. 

“Don’t use AI until you know enough to correct it,” she tells her students. 

Rebuilding the classroom 

As AI changes how students learn, faculty are also rethinking how they create and deliver their own course content. 

With one undergraduate health informatics class, Avramovic and colleagues realized that much of the content felt disconnected from the clinical realities that nursing students—who are required to take the course—would eventually face. So, the instructors used AI tools to rewrite assignments around nursing-specific scenarios, like identifying problems in electronic health records. They also introduced AI-generated videos narrated by digital avatars, guiding students through technical material. 

The updates made a difference. Nursing students in the AI-redesigned course showed statistically significant learning gains, more than half said AI-generated videos were more helpful than assigned readings, and nearly four out of five reported that the tools improved their overall learning experience. 

, a professor in the Department of Health Administration and Policy, took a similar approach in his graduate statistics course, using AI to help deliver lectures and tutor students. 

For students, the change meant more access to their instructor. “Because the AI handled a lot of mechanical tasks, I used the extra time to have more interactions with students, give them career advice, call up at-risk students, just be more available,” he said. 

Evolving the toolkit 

Faculty at the College of Public Health compare the current moment to earlier technological shifts in education, from the arrival of calculators in math classrooms to the rise of the internet and online learning.  

Students still need to learn the fundamentals, faculty said, but once they understand the underlying concepts, AI can help them explore more complex problems and apply knowledge in new ways. 

“You still need the human in the loop,” Avramovic said. 

The technology continues to evolve quickly. But faculty say the larger goal remains to help students build the skills they’ll need in a world where AI is already becoming part of the job.