Identifying the Applications of Artificial Intelligence in the Assessment of Medical Students

Abstract

Background: AI has rapidly transformed education, research, and community services in medical universities, surpassing earlier expectations about its integration. A key area of this transformation is student assessment, which plays a vital role in shaping learning outcomes, faculty workload, and public trust in medical education. Objectives: This study aims to explore the applications of AI in the assessment of medical students through a content analysis of relevant scholarly literature. Methods: This qualitative study employed a meta-synthesis method following Walsh and Downe’s seven-step framework. Using targeted keywords, a comprehensive search was conducted across major databases, including ScienceDirect, Springer, ERIC, Emerald, Sage Journals, Wiley Online Library, PubMed, and Google Scholar, covering publications from 2015 to 2024. A total of 200 articles were initially retrieved; after applying quality appraisal criteria, this number was narrowed down to 24 studies. To ensure the credibility of the findings, Whittemore et al.’s ten indicators for methodological rigor were applied. Results: Six key themes emerged regarding AI applications in medical student assessment: (a) feedback, (b) online exam, (c) instrument design, (d) assessment process, (e) student learning management, and (f) faculty workload management, along with 19 sub-themes. These findings reflect the diverse and evolving impact of AI in assessment practices. Conclusion: This study underscores the multifaceted and transformative impact of AI in medical student assessment across six key domains. These applications serve as a strategic roadmap for seamlessly integrating AI into the assessment of medical students while effectively adapting to evolving educational paradigms.

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