AI Ethics in Higher Education: How Schools Are Proceeding

Why AI Ethics Is Unique in Higher Education

Higher education is uniquely positioned to deal with AI’s ethical considerations, partly because AI adoption is already prevalent in academia.

At Miami University in Ohio, “there are courses about AI, and there are courses that use AI,” says Vice President for IT Services and CIO David Seidl. As AI use widens, colleges and universities need to give students “an ethical foundation, a conceptual foundation to prepare them for the future,” he says.

Many schools have the institutional expertise on campus needed to lay that foundation. “We have people who are very thoughtful, who bring subject matter expertise from a lot of lenses, so that you can have well-informed conversations about the ethics of AI,” says Tom Andriola, University of California, Irvine’s vice chancellor for IT and data.

Given higher education’s access and use of the technology, and the experts staffed at these institutions, the scene is already set for conversations about AI’s ethical quandaries. At many colleges and universities, those discussions have already commenced.

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Key Ethical Concerns Surrounding AI in Academia

At UC San Diego, CIO Vince Kellen says the top ethical issue with AI is democratization; specifically, the ability to access AI through an intellectual lens.

“Those who exert critical reasoning in using AI get a bigger benefit,” he says. “Those who do not get a lesser benefit.”

Universities have an ethical imperative to teach critical-thinking skills, and this dovetails with concerns about AI accuracy.

For example, you can ask AI, how do you keep cheese on pizza? “And it says: ‘Glue is a great way to keep cheese on pizza,’” Seidl says. “The ethical concern there is in giving that answer to individuals who may or may not be good at assessing the quality of that response.”

Privacy ranks high for Michael Butcher, assistant vice president for student affairs and dean of students at the College of Coastal Georgia, where he also co-chairs the AI task force.

“Folks don’t yet fully understand what happens when they input their data into an institutionally supported or a noninstitutionally supported AI application,” he says. Given the nature of academic data — from personal information to sensitive research — privacy becomes a significant ethical consideration.

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Bias is another concern. Ask AI to create a picture of a nurse, and it will likely draw a woman, because it’s been trained on data that reflects “long-term biases that exist in society,” Seidl says. “What are we inadvertently doing by having AI continue to perpetuate those things?”

There are also questions about academic integrity and the risk that users may lean too heavily into AI. Higher education needs to consider “where legitimate academic assistance ends and where unethical dependence begins,” Butcher says.

Given the various ethical gray areas, higher education is being challenged to establish guard rails in these early days of AI adoption.

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