Can AI Help Students—and Colleges—Determine the Best Fit?

Whether you call it “right fit,” “best fit” or good fit, the concept of an institution being a suitable match for a student’s needs—be they social or academic—is probably one you’ve read about or discussed in the classroom or at the dinner table. It’s a concept universities and applicants alike are paying close attention to, as it relates directly to student success.

Admissions officers pour through a lot of materials, such as resumes and essays, to make the final call on whether or not to accept an applicant. But before an application ends up on an admissions officer’s desk, a student has to decide if he or she even wants to apply to that school.

Factors that can influence students’ ultimate decision on where to apply can include finances, advice from parents or teachers, majors and activities at the university and confidence or lack thereof when they compare their scores to those of a university’s admitted students. Artificial intelligence has emerged as a technology that can help these students make decisions.

Students’ considerations of where they’d like to attend college are naturally based on what they think is important, but these self-examinations can be influenced by other people, says Curtis Patrick, a senior architect at Ellucian, a company that builds software for higher education institutions. He thinks passive data, such as a person’s Youtube viewing habits, can offer insights that help a student determine which schools are a good fit.

“Maybe, maybe, maybe machine learning could come back and say look, we’ve identified these things and we think this school is a better fit for these reasons,” Patrick says.

There are companies that already use AI to help students with the admissions process. Take ConnecPath; it’s an AI-based Q&A platform that seeks to answer students’ questions about colleges. Then there’s Delphia, a company that recently presented at Y Combinator Demo Day, which aims to use surveys to help people make life choices, including where to attend college.

However, Patrick warns that the machine learning and AI approach is a gray area that can only do so much. No system, he explains, can say “100 percent” that a student should attend a certain school. That’s because even though machine learning and AI are fact-based, Patrick says, they can’t understand everything. “Even with all the data in the world, you can’t really capture the essence of a person.”

Patrick also cautions that the recommendations on what school to attend, or even how those recommendations are derived, can “chase diversity” out of institutions.

But does AI have a role on the admissions office side of the process? Kasey Urquidez, dean of undergraduate admissions at the University of Arizona, tells EdSurge there are current tools that use AI to pick up key words in essays—not to “count anybody out automatically,” but “help to enhance” exactly who a school is looking for. She adds that her institution is not using such tools in admissions at this time. That said, she thinks machine learning and AI are “definitely coming” to admissions to help determine if a student is the right fit for a school.

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However exactly what a school is looking for—how it defines the term “right fit”—varies depending on the school’s philosophy and the policies it must work with, says Paul Hays, a consultant who helps students with the college application process. Hays, whose professional history includes working in enrollment management at DePaul University, points to the University of Texas at Austin as an example; UT has to abide by Texas law and automatically admit applicants that fall into a certain class rank.

“Colleges are for the most part looking for the best class, not necessarily the best person,” says Hays, referring to higher education institutions’ desires to have a well-rounded student body.

Arizona’s Urquidez says her institution wants to recruit students with diverse backgrounds and abilities from all over the world, but looks at students individually when it reviews applications.

In particular, she and her colleagues hope to admit students they believe will be successful and will graduate. “So of course, academics are important,” she says. Along with that, the admissions team also desires “really active learners” who will participate in clubs and organizations, rather than just going to and from class.

Urquidez believes that “good fit” is a proxy for retention rate. She claims that students generally leave an institutions for one of three reasons—money, academics and fit. She adds that students who leave the University of Arizona generally “fall into those three categories evenly.”

And while Urquidez does think that AI can help admissions officers with their work, she doesn’t think it will fully replace the current process. In her view, the “human element” will always be needed.