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Background <p>Generative 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.</p> Methods <p>PRISMA 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.</p> Results <p>A total of 1213 were recorded, screened, and 27 studies were included in the sample (<i>n</i> = 14 quantitative, <i>n</i> = 8 mixed methods, <i>n</i> = 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 (<i>n</i> = 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.</p> Conclusions <p>Modern 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.</p>

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The impact of generative artificial intelligence tools on assessment equity for non-native English-speaking medical students: a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Jean Claude Yvan Ndacyayisenga,
  • Charles Kidega,
  • Lucy Aciro Can

摘要

Background

Generative 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.

Methods

PRISMA 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.

Results

A 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.

Conclusions

Modern 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.