As the former Dean of Admissions and Financial Aid at Dartmouth College, I remember the endless days and nights of reading applications, fueled by coffee and passion. Those of us with long careers in selective admissions considered the nuance of your voice, the depth of your experience, and the fit for our university community as we painstakingly crafted an incoming class.
Today, that work is fueled by algorithms: before a human reader ever opens your essay, a complex machine learning model has probably already analyzed your application. It has scored your likelihood of enrollment, standardized your academic record, and potentially flagged your essays and letters of recommendation for specific keywords or patterns.
PREDICTIVE ANALYTICS: THE BUSINESS OF YIELD
For highly selective institutions, the acceptance rate is only half the battle; the yield rate is equally critical. Yield is simply the percentage of accepted students who ultimately choose to enroll. Maximizing this rate is crucial for maintaining selectivity rankings and efficiently managing the class size. As author Jeff Selingo recently noted in an essay in the New York Times, “Today, a school’s yield rate, like its selectivity, is a sign of status and popularity in an admissions industry obsessed with numbers and rankings.”
Predictive modeling algorithms aren’t necessarily new, but they’ve been supercharged by AI. These models are fed historical data—tens of thousands of data points from a college’s previous admissions cycles. They analyze everything from your grades and scores to your high school’s academic profile and your geography (down to the zip code) to your financial aid needs and (for those schools who track it) your demonstrated interest (Did you open the admissions emails? Did you attend virtual or in-person info sessions?).
The output from these models is a yield score, which estimates the probability that you’ll enroll if admitted. Admissions offices strategically use this yield score to shape decisions around who to admit, craft personalized financial aid packages, and even manage the waitlist.
The use of yield models is not without controversy, especially at schools that take demonstrated interest into consideration. If a model predicts a high-achieving student will likely attend an Ivy over a slightly less selective institution, that student might not be admitted in favor of a similar applicant with a higher predicted yield score.
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ANALYZING YOUR TRANSCRIPT: STANDARDIZING ACADEMIC DATA
It has always been challenging for admissions officers to analyze high school transcripts, primarily due to lack of standardization across thousands of high schools and, for those colleges that practice holistic review, the need to evaluate performance in context of available opportunities. The careful analysis of a high school transcript and accompanying profile (if provided) was typically the responsibility of the first reader on an application. Some university admissions offices, most notably Princeton University, would calculate a standard 4.0 unweighted GPA for all applicants before the reading process began.
Not surprisingly and in the pursuit of efficiency, many large and selective institutions require students to self-report their grades and course names into a portal. Whether through the Common App, ApplyUC or the separate Self-reported Transcript and Academic Record System (STARS, usually accessed through the college’s applicant portal after the Common App has been submitted), applicants now do the hard work of transcribing their academic data into a structured format that can be read by AI.
You can bet that colleges are using AI tools to quickly process college transcripts by extracting information like student coursework, assign common course codes (e.g., matching “AP Calc BC” to a standard college code), and normalize GPAs across different scales. This dramatically speeds up the initial review phase, preparing the data for the reader. For schools that maintain a holistic review process, the AI prepares the data, but the admissions officer still verifies the data’s context, such as the overall rigor of the applicant’s high school and curriculum.
AI: READING ESSAYS AND LETTERS OF RECOMMENDATION
The question of whether a bot is truly judging your personal story is where the conversation turns sensitive, but the technology is there and some schools are being very transparent about utilizing it. Virginia Tech, for example, has integrated “a new approach to evaluating applicant essays that pairs human reviewers with an artificial intelligence (AI)-supported model developed by Virginia Tech researchers.” In this case, AI is fully replacing a second human reviewer. Virginia Tech News reports, “AI is being utilized to confirm the human reader essay scores, not make any admissions decisions. Final admissions decisions will be made exclusively by qualified and trained admissions professionals.”
As reported in The Daily Tar Heel earlier this year, “application essays reviewed by the UNC Office of Undergraduate Admissions are auto-scored based on writing quality.” UNC, like many selective admissions offices, uses Slate, an information management system. Checking Slate’s promotional materials, we can see its Reader AI functionality: “You can treat Reader AI as your knowledgeable colleague, able to quickly analyze and identify pertinent details in letters of recommendation, college essays, or any other document you provide.”
What are some of the other “pertinent” details that these tools might glean from reviewing your application? NLP algorithms can categorize essays by theme (“overcoming adversity,” “questioned belief”) or flag specific keywords or patterns. AI can quickly scan hundreds of letters of recommendation to identify highly generic, boilerplate letters—suggesting a weak recommendation—or, conversely, detect specific keywords that data suggests correlate with successful applicants. An AI could also cross-reference the language and tone of the letter of recommendation against the student’s self-reported activities list or essay, checking for consistency across the application components. It is easy to see how overworked readers and understaffed admissions offices would turn on this functionality to help their readers more quickly and efficiently evaluate applications.
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AI: THE FUTURE AND YOUR APPLICATION
Will AI eventually be trusted to make final qualitative judgments? The current consensus seems to us to lean toward AI remaining a powerful assistant to the admissions officer, focusing on data optimization rather than nuanced evaluation.
For you, this means the best strategy remains authenticity. Since the qualitative AI is designed to detect patterns and anomalies, a genuine, distinctive application that resists easy categorization is still the most powerful way to stand out. Don’t write for the machine—write for the human reader who will ultimately make your decision.
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In addition, Maria is proud of her long-term volunteer work with Opera North (Lebanon, NH) in fundraising, marketing and audience engagement. She is an avid masters rower with the Upper Valley Rowing Foundation (Hanover, NH) and also serves as a member of its board of directors.
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