Hugh Hewitt, a Fox News contributor and law professor, contends that artificial intelligence presents a viable solution for the higher education sector rather than solely a threat to employment. He argues that the specific mechanics of college admissions, which rely heavily on processing standardized data, make the sector an ideal candidate for AI integration. This shift would impact an estimated workforce of at least 40,000 admissions employees across the United States.
Hewitt cites a study released in April 2023 by the College and University Professional Association for Human Resources, which surveyed 12,042 admissions employees at 940 institutions. The data indicated that these institutions averaged more than a dozen admissions staff members each. Given that there are more than 4,000 degree-granting institutions in the U.S., Hewitt calculates this represents a significant labor pool. He notes that while not all positions would be eliminated, the volume of work suggests substantial displacement.
The core of Hewitt's argument rests on the nature of the admissions process. He describes it as being driven by paper and numbers, including test scores, GPAs, essays, resumes, and recommendations from tens of thousands of applicants. He asserts that AI models can compile and assess these quantitative metrics in hours or minutes. Furthermore, he suggests AI can evaluate qualitative materials such as essays for originality and evidence of outside assistance, and sort resumes for truthfulness and sincerity.
Beyond basic sorting, Hewitt proposes that AI can be programmed to apply specific weights to legitimate indicators of merit. These factors include in-state or out-of-state status, gender, family income, life circumstances, and geographic or class diversity. He emphasizes that AI models can be trained to evaluate grades based on the secondary school attended and can be instructed to ignore race, ethnicity, or religion. The use of these protected characteristics in admissions is restricted by federal law and Supreme Court precedent.
Hewitt suggests that AI could also model long-term success for incoming classes by considering factors such as athletic ability, legacy status, musical talent, foreign-language fluency, and first-generation college student status. He argues that AI would be a better agent for targeting the long-term success of an applicant pool than young admissions officers. Additionally, he claims AI can help schools defend against legal challenges by providing transparent evidence that prohibited factors were not used in the decision-making process.
The current application process has faced suspicion regarding politicization and the use of controversial factors like race, a practice the Supreme Court has significantly restricted. Hewitt states that an AI-driven process transparent to outside evaluators could increase trust in results. He views this as a welcome evolution in a controversial area of higher education.
Regarding the displaced workers, Hewitt notes that their current role involves sorting data and making recommendations to higher-ups, allowing subjective biases to influence outcomes. He argues that these employees would be better served by work that can be objectively evaluated. He recommends that colleges deploy a parallel admissions process run by AI alongside their existing structure to allow for a side-by-side comparison of accepted applicants.