With AI quickly making its way into higher education, many scholars are reevaluating
how it decides the admissions. A systematic review of the peer-reviewed
literature published during 2019 and 2025 throws light to the state of knowledge in
relation to AI-led admission systems the details of operational architectures and
documented impacts on volume of admission intakes and diversity of institution.
Drawing on research published in Scopus and in Australian Business Deans Council
(ABDC) classified journals, the review collates evidence on four linked themes (1)
An adoption and functional architecture of AI admissions systems; (2) Predictive
analytics and machine learning models for helping run the function of enrolment
management; (3) Algorithmic bias with differential impact on racial, gender and
socio-economic groups; and (4) Ethical and regulatory frameworks for using AI for
admissions. The figures show that AI engines may increase throughput efficiency,
reduce processing cost, increase predictive accuracy. But if historical data collects
bias, then it may lead to reinforcement of same. Models that benchmark against
historical enrolment norms focus on past limiting biases which aggravate the harm
of Black, Hispanic, first-generation and low-income students. Academics studying
Fairness, Accountability Transparency and Ethics (FATE) are constructing a
normative vocabulary for reform. However, there’s a gap between the anticipations
of policies and the technical implementation of these policies. A research agenda
and recommendations for institutional policies to maximize alignment between
equity-centred goals and AI-driven admission in higher education concludes our
review.
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- 8. Semiconductor Manufacturing in India: Assessing Its Impact on Gross Domestic Product and Pathways to Industrial Transformation
- 9. AI-Driven Admission Systems and their Impact on Admission intake and Institutional Diversity: A Review
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Abstract
Keywords
artificial intelligence admission systems predictive analytics algorithmic bias institutional diversity enrolment management higher education machine learning
Chapter information
| Chapter title | AI-Driven Admission Systems and their Impact on Admission intake and Institutional Diversity: A Review |
|---|---|
| Author(s) | Reema Anand, Director Admissions | Dr. Bhanu Pratap Pandey, Assistant Professor Sharda University |
| Book title | Confluence of Perspectives: Essays in Leadership, Management and Society Vol.1 |
| Editor(s) | Abhinanda Bhattacharya | Dr. Ruchi Jain Garg |
| Book Doi | https://doi.org/10.67103/IRG.COP1.2026.9788168516410 |
| DOI | https://doi.org/10.67103/IRG.COP1.2026.9788168516410.9 |
| Publication date | July 17, 2026 |
| Access | Restricted access |
How to cite
Reema Anand, Director Admissions | Dr. Bhanu Pratap Pandey, Assistant Professor Sharda University. 2026. AI-Driven Admission Systems and their Impact on Admission intake and Institutional Diversity: A Review. In: Abhinanda Bhattacharya | Dr. Ruchi Jain Garg (eds). Confluence of Perspectives: Essays in Leadership, Management and Society Vol.1. Book Doi https://doi.org/10.67103/IRG.COP1.2026.9788168516410. https://doi.org/10.67103/IRG.COP1.2026.9788168516410.9