Understanding and Preventing AI-Facilitated Cheating in Dental Education

Document Type : Narrative Review

Authors

1 Dentist, Student Research Committee, Faculty of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran

2 Student, Student Research Committee, School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran

3 Department of Pediatric Dentistry, School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: Generative AI has created new academic-integrity risks in professional training. In dentistry, these risks carry direct patient-safety implications.
Method: We conducted a structured narrative review (SANRA-guided) of PubMed, Scopus, and Google Scholar (September 2022–July 2025). Original studies, reviews, and expert opinions on AI-facilitated academic misconduct in dental education were eligible; clinically focused AI papers were excluded. Dual screening and thematic synthesis were applied.
Results: Sixteen studies met the inclusion criteria; ~19% were dental-specific, with the remainder informing transferable practices from health/allied higher education. Three themes emerged: (1) Prevalence & patterns—≈58.7% of students report awareness of AI use by peers; applications include essay generation, clinical note fabrication, image manipulation, and real-time exam assistance; (2) Detection challenges—traditional plagiarism tools detect ≤23% of AI-generated text; faculty report high uncertainty; (3) Emerging solutions—authentic/oral assessments (≈73% reduction in cheating reports), policy frameworks, faculty development, explainable/authorship-verification tools, and integrity culture initiatives.
Conclusion: AI-facilitated cheating requires discipline-specific responses that combine policy (explicit acceptable-use definitions), pedagogy (authentic/oral/practical assessments), and platforms (fit-for-purpose detection/verification). Priorities include clarifying policy, upskilling faculty, and validating detection approaches in clinical assessment contexts.

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Future of Medical Education Journal
Volume 15, Issue 3
Special Issue on Artificial Intelligence
September 2025
Pages 24-28

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