Identifying the Applications of Artificial Intelligence in the Assessment of Medical Students
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".