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Exploring the Aids of Machine Learning and Artificial Intelligence for the Detection of Fraudulent Documents in University Admissions Databases: A Perspective from University Officers and IT Professionals

  • Jacky Tam,   
  • Apple H. C. Lam,   
  • Dickson K. W. Chiu *

Received: 17 Aug 2025 | Revised: 19 Sep 2025 | Accepted: 30 Oct 2025 | Published: 07 Nov 2025

Abstract

This research examines the vulnerabilities in current university admissions document verification processes and explores the potential application of machine learning (ML) and artificial intelligence (AI) in detecting fraudulent submissions. Drawing on ten semi-structured interviews with admissions staff from various Hong Kong higher education institutions, the study employed thematic analysis guided by the PEACE investigative interviewing framework and the Diffusion of Innovations theory. The findings reveal significant reliance on manual, inconsistent verification practices, frequent procedural loopholes, and a lack of standardized consequences for fraud. While AI adoption is currently minimal, participants demonstrated cautious optimism, viewing AI as a supportive tool for pattern recognition, efficiency enhancement, and fraud flagging—especially in high-volume document types such as language test results. Institutional challenges, such as limited technical infrastructure and staff resistance, were identified as barriers to implementation. This study proposes a phased strategy for AI integration and emphasizes the need for cross-institutional collaboration, standardized protocols, and proactive staff development. It offers original insights into how AI can complement existing work processes, address cultural and infrastructural barriers, and potentially lay a foundation for practical innovation in academic admissions fraud prevention.

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Tam, J.; Lam, A. H. C.; Chiu, D. K. W. Exploring the Aids of Machine Learning and Artificial Intelligence for the Detection of Fraudulent Documents in University Admissions Databases: A Perspective from University Officers and IT Professionals. Library, Information & Services 2025, 1 (1), 2.
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