AI Use Cases for Universities
Organizations in different industries are using artificial intelligence (AI), machine learning, and data science to uncover deep insights about their processes and procedures and make predictions that will help them allocate resources and increase productivity. Universities, like other businesses, need to do the same to best serve their students and protect their bottom line.
AI can help universities predict, for example:
- The probability that a prospective student will apply for a particular program
- The probability that the prospective student will pass the preliminary admission screening
- The probability that an accepted student to actually enroll
- How likely it is that a student will be at risk
- Which jobs students should apply for
- The likelihood that an alumnus / alumna will donate to the university
In a marketing use case, the university can build a model to predict whether a prospective student will apply. The prediction would be based on interaction data including the following:
- How they arrived at the university website
- Which pages they visited
- Which buttons they clicked
- How long they spent on the site
- Whether they tried to apply but didn’t submit
- What location they visited the university website from
For prospective students who have a high probability of applying, the university can do some follow-up to encourage them to submit their applications. This can increase the marketing conversion rate and make the university more cost-effective.
AI is also useful after the university receives applications. It can help identify which applications will pass a preliminary admissions screen. This allows the university to accept more applications while keeping the same number of people working on the admissions process. AI can help at the preliminary stage, allowing staff to focus on the following admissions stages.
After the university decides which prospective students to accept, not all accepted students will actually enroll. This challenge can directly affect the university revenue. Low enrollment rate means that the university needs to take more efforts to get more students in limited time. While a high enrollment rate can cause a capacity problem which is additional cost to the university for providing extra resources to assist more students. AI can help to predict which accepted students are going to enroll so that the university can have a better preparation for the next intake in terms of dorm space, classes needed, and other resources.
After the students enroll in the university program, the performance of these students will affect the university’s reputation, for better or for worse. The admissions process helps to identify promising students. However, everything can change over the course of a college career, including the students themselves. It is essential for the university to track their students’ progress so they can pinpoint those who are at risk of failing one or more courses and determine what kind of assistance they need to graduate. The increased graduation rate can help the 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 students at risk.
After graduation, the university can continue to help students with their employment by building models that recommend to them specific jobs based on job requirements, the student’s profile, behavior, and academic performance history. By helping students get jobs fast, students enter the workforce successfully, industries benefit, and universities fulfill their missions of helping students build on their academic careers.
The downstream effect is that many grateful alumni want to give back to the university. But sometimes they don’t know how. For their part, universities have so many alumni, and it’s difficult for the institution to keep in contact with all of them. AI can help universities predict which alumni to target for donation based on demographic data, records of recent interactions, and their relationship with the university.
These are just a few examples of how using AI and machine learning can help bring value to universities. There are other use cases as well, such as how to match students with lecturers or academic advisors, how to select students to receive scholarships, which elective courses to recommend to students, how to predict student retention and which students will fail. When it comes to using AI to optimize the university experience, the possibilities are virtually limitless.