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Updated: Jan 14, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Artificial Intelligence Use in Academic Applicant Screening: A Systematic Review.

Timothy Jiang1, Seray Er2, Matthew J Heron2

  • 1From the University of Maryland School of Medicine, Baltimore, MD.

Plastic and Reconstructive Surgery. Global Open
|October 27, 2025
PubMed
Summary
This summary is machine-generated.

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Artificial intelligence (AI) shows promise in academic admissions screening, with traditional machine learning (TML) outperforming natural language processing (NLP). However, ethical concerns like bias require careful attention for responsible AI adoption in admissions.

Area of Science:

  • Educational Technology
  • Computer Science
  • Data Science

Background:

  • Increasing application volumes strain traditional holistic admissions processes.
  • Artificial intelligence (AI) presents a potential solution for enhancing screening efficiency in academic admissions.
  • This systematic review investigates the application, performance, and ethics of AI algorithms in admissions.

Purpose of the Study:

  • To systematically review the use of AI algorithms in academic admissions.
  • To categorize AI types, evaluate their performance, and assess ethical considerations.
  • To provide insights into the effectiveness and challenges of AI in admissions screening.

Main Methods:

  • Systematic search of five databases up to October 2024 for studies on AI in admissions (undergraduate to residency).

Related Experiment Videos

Last Updated: Jan 14, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
  • Data extraction included study characteristics, AI types, performance metrics, and ethical concerns.
  • Bias risk assessed using the Newcastle-Ottawa Scale; objective outcomes statistically compared.

    • Main Results:

    • Eighteen studies involving 61,327 applicants were analyzed.
    • Primary AI applications included interview selection, admissions decisions, and reviewer scoring.
    • Traditional machine learning (TML) models achieved an average AUC of 0.89, outperforming NLP models (0.77); 50% of studies reported ethical concerns, particularly bias.

    Conclusions:

    • TML algorithms are effective with structured data, while NLP shows potential for unstructured data, despite transparency and data requirements.
    • Ethical considerations, including bias and transparency, are critical for AI adoption in admissions.
    • Further research is needed to address ethical and methodological challenges in AI for academic admissions.