AI in College Admissions - A Five-Part Series on the Future of the Application Process - #2 AI in Admissions: What Colleges Are Actually Doing

While AI is often discussed in the context of student essays and application preparation, colleges themselves are also adopting artificial intelligence behind the scenes. Although these systems rarely make final decisions on their own, they play an increasingly influential role in how applications are processed, evaluated, and prioritized. Understanding these tools is essential for families, counselors, and students trying to make sense of a shifting admissions landscape.

Predictive Modeling: The New Face of Enrollment Management

Colleges have long relied on data analytics to forecast yield—essentially, to predict which admitted students are likely to enroll. But with the introduction of AI, these models are becoming far more sophisticated. Today’s predictive tools can analyze a wide range of inputs, including:

• high school context and curricular rigor

• past institutional data on student outcomes

• academic and performance trends

• demographic information

• application timing and engagement patterns (emails opened, events attended, inquiries made)

  
  

These insights help colleges plan class sizes, allocate financial aid, and balance institutional priorities. However, they also raise important questions. Could a student’s application receive less attention because an algorithm predicts they’re unlikely to enroll? Might certain groups be unfairly disadvantaged if historical patterns or biased data influence predictions?

As predictive modeling grows more advanced, transparency and fairness become central concerns.

AI-Assisted Application Review: Shaping What Reviewers See

Beyond enrollment forecasting, some admissions offices are experimenting with AI tools designed to support (not replace) human readers. These systems can:

• summarize lengthy recommendation letters

• group essays by theme or topic

• flag specific experiences or skills

• sort applications into categories for reviewer prioritization

  
  

The goal is to reduce reviewer fatigue, bring consistency to large applicant pools, and help admissions teams process thousands of applications efficiently.

However, as with predictive modeling, there are risks. Critics worry about algorithmic bias, misinterpretation of cultural nuance, or overreliance on patterns that overlook individuality. There’s also the danger of reducing a student’s story to a set of data tags—losing the richness and complexity that makes each applicant unique.

Counselors should recognize that in some institutions, parts of a student’s application may be filtered, summarized, or categorized by AI before a human ever reads it.

AI is reshaping admissions on the institutional side just as rapidly as it is affecting students. Understanding how these tools work—along with their limitations—is key to navigating the modern application process responsibly and effectively.