The ongoing discussion surrounding the widespread adoption of artificial intelligence models frequently centers on their potential to displace human jobs across various sectors. Identifying specific employment categories particularly susceptible to AI integration can reveal patterns about which roles face immediate transformation.
One area ripe for the application of AI is the admissions offices of colleges and universities nationwide. While these positions do not represent an enormous segment of the workforce, compelling reasons exist to leverage AI in these departments, extending beyond mere cost savings for institutions already grappling with budgetary pressures.
The Scope of Admissions Employment
A study released in April 2023 by the College and University Professional Association for Human Resources (CUPA-HR) examined 12,042 admissions employees across 940 institutions. The findings indicated that, on average, each institution employed more than a dozen admissions staff members. Considering there are over 4,000 degree-granting institutions in the United States, a conservative estimate places the admissions workforce at approximately 40,000 individuals. While AI is poised to significantly impact this group, a complete job loss is unlikely, as some human oversight and interaction will likely remain essential.
Why Admissions is Ideal for AI Integration
The college and graduate school application process is inherently data-intensive, driven by a multitude of quantitative and qualitative inputs: standardized test scores, grade point averages, personal essays, résumés, and letters of recommendation from tens of thousands of aspiring students. A common aspiration among applicants is a genuinely fair evaluation process.
The task of sorting and scaling numerical data, such as GPAs and test scores, is precisely where AI excels, capable of compiling and assessing information in a fraction of the time human staff would require. If AI's capabilities are even a fraction of what is advertised, it could effectively scrutinize résumés and recommendations for veracity, quality, and authenticity. Similarly, essays could be analyzed for originality and to detect any signs of external assistance.
AI models can also be configured to assign appropriate weight to legitimate indicators of merit that extend beyond academic achievement. These might include in-state versus out-of-state residency, gender, family income levels, challenging life circumstances, and the imperative for broad geographic and socioeconomic diversity. Furthermore, AI systems can be trained to contextualize grades and performance based on the specific nature and rigor of the secondary school or college previously attended.
Crucially, AI models can be explicitly instructed to disregard factors such as an applicant’s race, ethnicity, or religion—characteristics whose use in admissions decisions is proscribed by federal law and Supreme Court rulings. Other legitimate considerations, such as athletic prowess, legacy status, musical or theatrical talent, debate skills, foreign-language fluency, and many other attributes, can be factored in. Indeed, AI holds the potential to be a far more effective tool for constructing a well-rounded incoming class and forecasting the long-term success of that student body on campus and in their future careers, surpassing the current capabilities of human admissions officers.
Enhancing Trust and Accountability
The implementation of AI could also provide assurances to various stakeholders, including donors, college evaluators, and legal bodies, that the admissions process remains untainted by the use of prohibited criteria. An institution seeking a robust defense against legal challenges regarding its admissions practices, particularly those related to unlawful considerations like race, could significantly benefit by transparently outlining the weighting scheme of its AI model, even if the model's output is not the sole determinant of admission.
Educational institutions must consider a range of factors beyond academic merit, including an applicant's ability to cover tuition costs, their likelihood of securing employment post-graduation, and their potential to become a future financial supporter of the university. Institutional reputation is also paramount, as the network effects within a strong student body are a tangible benefit. AI can, of course, be trained to weigh indicators for all these aspects, including an applicant's work history and whether they are a first-generation college student—both indicators generally associated with future life success.
In recent decades, the application process has become increasingly fraught with suspicions of politicization and the use of controversial factors, such as race, by admissions officers—a practice now significantly curtailed by the Supreme Court. The nationwide admissions system could greatly benefit from a substantial infusion of objectivity, leading to a consequent increase in public trust in its outcomes. An AI-driven admissions process, transparent to external evaluators, would represent a welcome advancement in the increasingly contentious issue of selecting future leaders and professionals.
The Future of Admissions Roles
What then of the estimated 40,000 individuals currently employed in admissions roles? Their current responsibilities largely involve sorting, sifting, and making recommendations to senior staff. This work often entails subjective judgments, allowing personal biases to influence decisions across a vast pool of applicants.
It is arguable that all involved, including the admissions staff themselves, would be better served by engaging in work that can be objectively assessed and does not encourage the exercise of subjective decision-making. AI should be embraced in any professional domain where large volumes of data require impartial analysis. At a minimum, colleges and universities ought to explore implementing a parallel admissions process managed by AI alongside their existing structures. A side-by-side comparison of accepted applicants from both systems could yield invaluable insights.
