The use of Artificial Intelligence in higher education admissions: a scoping review

Artificial Intelligence (AI) is increasingly integrated into higher education admissions systems. This scoping review maps and critically synthesises peer-reviewed research published between 2020 and 2026 on AI in higher education admissions. Following PRISMA-informed search procedures across ERIC, Scopus, and Web of Science, 673 records were screened, and 25 studies met inclusion criteria. Descriptive analysis indicates a rapidly expanding body of research that is predominantly quantitative and geographically concentrated in the United States. Reflexive thematic analysis identifies three interconnected themes: (1) AI used by institutions in admissions systems; (2) AI used by applicants in performance-based assessments; and (3) AI, fairness, and the governance of admissions. The review argues that AI in admissions extends beyond efficiency gains, reshaping how merit is defined, how performance is interpreted, and how fairness is perceived and justified within high-stakes higher education decision-making systems.

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Publication Details

Journal
Assessment in Education Principles Policy and Practice
Published
2026-08-20
DOI
https://doi.org/10.1080/0969594x.2026.2717193
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

The use of Artificial Intelligence in higher education admissions: a scoping review

Berna Sena Civan
Assessment in Education Principles Policy and Practice
Online Learning and Analytics
article

The use of Artificial Intelligence in higher education admissions: a scoping review

Berna Sena Civan
article en

Abstract

Artificial Intelligence (AI) is increasingly integrated into higher education admissions systems. This scoping review maps and critically synthesises peer-reviewed research published between 2020 and 2026 on AI in higher education admissions. Following PRISMA-informed search procedures across ERIC, Scopus, and Web of Science, 673 records were screened, and 25 studies met inclusion criteria. Descriptive analysis indicates a rapidly expanding body of research that is predominantly quantitative and geographically concentrated in the United States. Reflexive thematic analysis identifies three interconnected themes: (1) AI used by institutions in admissions systems; (2) AI used by applicants in performance-based assessments; and (3) AI, fairness, and the governance of admissions. The review argues that AI in admissions extends beyond efficiency gains, reshaping how merit is defined, how performance is interpreted, and how fairness is perceived and justified within high-stakes higher education decision-making systems.

Assessment in Education Principles Policy and Practice
University College London (GB)
Quality Education
Openalex Percentile: Top 5%
Online Learning and Analytics
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