Undergraduate Admission Exam

Undergraduate admission exams are undergoing significant scrutiny and transformation, driven by the need for fairer, more efficient, and data-driven processes. Research focuses on leveraging machine learning, particularly deep learning models like feed-forward and transformer networks, to predict admission outcomes, analyze application materials (essays, recommendation letters), and even conduct automated interviews. These advancements aim to mitigate human bias, improve the accuracy of admissions decisions, and enhance accessibility for diverse applicant pools, though challenges remain in fully replicating the nuanced judgment of holistic review. The ultimate goal is to create more equitable and effective systems for selecting undergraduate students.