SynthesisBMC medical education2026
The impact of generative artificial intelligence tools on assessment equity for non-native English-speaking medical students: a systematic review.
Synthesis in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundGenerative artificial intelligence (GenAI) has been integrated into medical education assessment, creating concerns about fairness toward linguistically diverse students. This systematic review summarized evidence on the effect of GenAI on assessment validity and systematic bias for non-native English speakers (NNES). This study focused on medical students (NNES) who undergo training within the framework of the modern theory of validity.
methodsPRISMA 2020, ERIC, PubMed, Scopus, and Web of Science databases were searched (2022–2025). The included studies assessed GenAI applications (e.g., LLMs, AI detectors, and automated scoring) in tests administered to medical, nursing, pharmacy, or dental students in English-medium institutions. Two reviewers screened, extracted the data, and independently evaluated the quality of the MMAT and AXIS instruments. Thematic synthesis was based on Kane’s validity framework.
resultsA total of 1213 were recorded, screened, and 27 studies were included in the sample (n = 14 quantitative, n = 8 mixed methods, n = 5 qualitative). Six experimental research studies concluded that AI detectors falsely labeled NNES writing as AI-created in 50.2%–61.3% of all cases (compared to less than 5% among native writers). Four automated scoring studies showed a downward bias of 0.5–1.2 SD of NNES to be systematic for students. According to the student survey, GenAI adoption was lower (n = 8, total = 2,847), with 79% expressing considerable apprehension (63% said it was an issue of concern) and 71% perceived policy ambiguity. Qualitative research showed tension between GenAI as a linguistic aid and the danger of false misconduct accusations, highlighting the need for clear guidelines and support for NNES students to effectively navigate these challenges.
conclusionsModern GenAI assessment instruments may cause systematic construct-irrelevant variance, putting NNES medical students at a disadvantage. In high-stakes decisions, equity requires multilingual validation, bias audits, transparent governance, and human supervision.
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