# Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions

arXiv: https://arxiv.org/abs/2302.03610 · HTML: https://mlsanity.com/paper/2302.03610/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [The Life Cycle of Large Language Models: A Review of Biases in Education](https://mlsanity.com/paper/2407.11203/) (2024)
- [Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters](https://mlsanity.com/paper/2306.17575/) (2023)
- [Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction](https://mlsanity.com/paper/2507.15862/) (2025)

## Abstract (arXiv)

A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for review. However, this approach may be subject to bias in test scores and selection bias in test-taking with recent trends toward test-optional admission. We explore a machine learning-based approach to replace the role of standardized tests in subset generation while taking into account a wide range of factors extracted from student applications to support a more holistic review. We evaluate the approach on data from an undergraduate admission office at a selective US institution (13,248 applications). We find that a prediction model trained on past admission data outperforms an SAT-based heuristic and matches the demographic composition of the last admitted class. We discuss the risks and opportunities for how such a learned model could be leveraged to support human decision-making in college admissions.