identifying the applications of artificial intelligence in the assessment of medical students

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.