University admissions leaders are finding ways to leverage artificial intelligence (AI) and machine learning (ML) within academia. Particularly, this technology helps institutions increase early acceptance rates while cutting back on expensive and time-consuming calls to staff.
The recent AI uptake in the academic space is likely due to factors such as tighter budgets and reduced reliance on standardized testing, Emily Campion, PhD, assistant professor in management at University of Iowa, told Student Select AI in a recent interview.
Tighter budgets mean fewer staff, and fewer staff means less time with students.
But AI and ML considerably combat these barriers by helping leaders leverage resources more effectively and make better overall decisions. Campion noted that individuals and institutions are likely to be smart consumers of this technology.
“AI is really helpful in two key ways,” Campion stated. “First, it's helpful when tasks are repetitive, frequently performed, and time-consuming. And the second way AI can be helpful is to synthesize large amounts of information from diverse sources.”
For example, admissions staff receive the same questions from students about where to submit their applications or about what materials they need. But, this information is already online somewhere. Therefore, a chat box to help applicants navigate that information without taking the time of a staffer who’s already wearing many hats would be useful to both parties.
Additionally, biases in the admissions process are all too common, translating to “racism, sexism, ageism,” and other stereotypes. Throughout the process, admissions staff often have their personal biases come in and run out of cognitive resources.
All of these factors can affect how they synthesize student information.
AI and ML can help smooth the admissions process and synthesize large amounts of information through a well-trained algorithm. This technology can extract information from thousands of student materials and train a model to make admission-related predictions or recommendations.
Notably, text data has been historically difficult to score because thousands of applicants submit personal statements, responses to essay questions, resumes, letters of recommendation, and transcripts. For a human, processing all this information is a cumbersome task.
Therefore, offloading some of this burden using natural language processing (NPL), a type of AI, offers an opportunity to combine these with data already quantified, including GPA, to create the predictions and recommendations mentioned earlier, along with a swath of other insights.
Personality research helps leaders understand how people are going to perform in a job or the classroom. And extracting and analyzing this information can help uncover red flags or potential great successes early on in the application process.
Campion mentioned that good predictors of success include adaptability, grit to be strong predictors, conscientiousness, and high critical thinking skills. And on the other end, a few personality traits that tend to be “red flags” include narcissism, psychopathy, and Machiavellianism.
Most importantly, personality and psychological characteristics aren't currently being assessed in an admissions system. So personality variables related to student outcomes may be missed.
“We ask students for a lot of things, but there's a point at which we can't ask for much more. So, we can use the information they already give us to extract information and inform our decision-making,” Campion said.
“AI requires monitoring like any other system and takes a little time to build. But, it does save time and money in the long run and we do have some early and compelling research that AI can actually be a really useful tool for admissions officers,” she concluded.