Demystifying AI: Current State and Future Role in Medical Education Assessment
Abstract
Medical education assessment faces multifaceted challenges, including data complexity, resource constraints, bias, feedback translation, and educational continuity. Traditional approaches often fail to adequately address these issues, creating stressful and inequitable learning environments. This article introduces the concept of precision education, a data-driven paradigm aimed at personalizing the educational experience for each learner. It explores how artificial intelligence (AI), including its subsets machine learning (ML) and deep learning (DL), can augment this model to tackle the inherent limitations of traditional assessment methods. AI can enable proactive data collection, offering consistent and objective assessments while reducing resource burdens. It has the potential to revolutionize not only competency assessment but also participatory interventions, such as personalized coaching and predictive analytics for at-risk trainees. The article also discusses key challenges and ethical considerations in integrating AI into medical education, such as algorithmic transparency, data privacy, and the potential for bias propagation. AI’s capacity to process large datasets and identify patterns allows for a more nuanced, individualized approach to medical education. It offers promising avenues not only to improve the efficiency of educational assessments but also to make them more equitable. However, the ethical and technical challenges must be diligently addressed. The article concludes that embracing AI in medical education assessment is a strategic move toward creating a more personalized, effective, and fair educational landscape. This necessitates collaborative, multidisciplinary research and ethical vigilance to ensure that the technology serves educational goals while upholding social justice and ethical integrity.
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Concerns persisted regarding algorithmic bias and its implications for academic integrity in AI-generated assessments.
“concerns persisted regarding algorithmic bias and its implications for academic integrity [143].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [143]
Ensuring transparency in AI-generated assessments remained a challenge.
“ensuring transparency in AI-generated assessments remained a challenge”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [143]
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- Authors
- Turner, L., Hashimoto, D. A., Vasisht, S., & Schaye, V.
- Affiliations
- University of Cincinnati (US)
- University of Pennsylvania (US)
- New York University (US)
- Corresponding author
- Turner, Laurah
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-12-28
- Volume
- 99
- Issue
- Supplement_1
- Pages
- S42-S47
- ISSN
- 1040-2446 (ISSN-L)
- 1938-808X
- OpenAlex
- W4390546628
- Cited by
- 70
- References
- 44
- Citations by year
- 2026: 28 · 2025: 34 · 2024: 7 · 2023: 1
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence in medical education, medical education assessment, precision education, personalized learning, competency assessment, machine learning, deep learning, algorithmic bias, algorithmic transparency, data privacy, predictive analytics, personalized coaching, educational equity, AI ethics
- MeSH terms
- Mentoring, Artificial Intelligence, Education, Medical, Educational Measurement, Educational Status, Humans
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