Artificial Intelligence in undergraduate medical education: a scoping review of educational applications
DOI:
https://doi.org/10.17765/2176-9206.2026v19e13977Keywords:
Medical Education, Student, Teaching, Learning, Artificial IntelligenceAbstract
The objective was to map the application of artificial intelligence (AI) in undergraduate medical education, focusing on the tools used, learning outcomes, performance, and gaps in the literature. Searches were conducted in PubMed, Scopus, Web of Science, EBSCOhost (CINAHL/MEDLINE), ScienceDirect, and Google Scholar, including empirical studies in English, Spanish, and Portuguese, without temporal restrictions. Study selection was performed in duplicate using Rayyan, and findings were described based on the TPACK framework. Of 605 records, 58 studies (2018–2024) were included, mostly from high-income countries. Quantitative designs predominated, with small to medium sample sizes. AI was primarily applied to theoretical instruction, educational assessment, and simulation-based training, often linked to improvements in academic performance and objective skills. It is concluded that AI demonstrates educational potential but remains limited by methodological heterogeneity, theoretical gaps, and contextual inequalities, highlighting the need for structured, ethical, and longitudinal curricular integration.
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