Optimize the University Experience with AI
Organizations in different industries use artificial intelligence (AI), machine learning, and data science to uncover deep insights about their processes and procedures that help them make predictions to allocate resources and increase productivity. These same benefits could also help universities better serve their students and protect their bottom line.
AI can help universities predict possible outcomes to the following scenarios:
- Prospective students who will apply to the university.
- Prospective students who will pass preliminary admission screenings.
- Accepted students who will enroll at the university.
- Individuals who will identify as at-risk students.
- Jobs that will best match student skills.
- Alumni who will donate to the university.
A university can use AI to better manage student applications with a machine learning model that can predict whether a prospective student will apply. The prediction for these students can be based on the following interaction data:
- How they arrive at the university website
- Which pages they visit
- Which buttons and navigation they choose
- How long they spend on the university website
- Whether they attempt to apply but don’t submit
- Where they are located
For prospective students who have a high probability of applying, a university can put processes in place to follow-up and encourage them to submit their applications. These actions can increase the marketing conversion rate and help a university become more cost-effective.
A university can also use AI to process preliminary admission screenings. This benefit allows the university to accept more applications without increasing staff. During this preliminary stage, AI can help a university better manage the admissions process.
Once a university decides which prospective students to accept, not all accepted students will actually enroll. This challenge can directly affect a university’s revenue. While a high enrollment rate can cause additional costs for students resources, a low enrollment rate produces an additional workload for a university to attract more students in a limited time. AI can help predict which accepted students will enroll so that a university can be better prepared in terms of dorm space, class size, and other resources.
After the students enroll in the university program, it’s essential for a university to track its students’ progress to pinpoint those who are at-risk and determine what kind of assistance they need to graduate. An increased graduation rate can help a university improve its reputation and increase its accreditation, which can attract more students. Florida International University is an example of a university that has already implemented AI to predict and help at-risk students.
After graduation, a university can continue to help students with employment by building models that recommend specific opportunities based on the student’s profile, behavior, and academic performance history. By implementing AI, a university can help students enter the workforce successfully, industries can benefit with new talent, and students can build on their academic careers.
Transitioning from academics to career, many grateful alumni want to give back to their university; however, many find they don’t know how to do so. A university may also find it difficult to keep in contact with its large number of alumni. AI can help a university predict which alumni to target for donations based on demographic data, records of recent interactions, and their relationship with the university.
AI and machine learning can help bring value to universities from the enrollment process to alumni relations. There are other benefits as well, such as matching students with lecturers or academic advisors, selecting students to receive scholarships, recommending elective courses to students, predicting student retention, and identifying at-risk students. When it comes to using AI to optimize the university experience, the possibilities are virtually limitless.