This study evaluated four popular free machine translation (MT) tools for translating critical care educational texts from English into Chinese, Spanish, and Ukrainian. A multimodal approach measured MT quality using human ratings, automated metrics, and ease of use. Results revealed varying strengths across tools: Google Gemini excelled in bilingual ratings for Chinese and Spanish but faltered in Ukrainian, while Google Translate scored lowest for fluency. Notably, MT showed competitive performance against human translations in Spanish and Ukrainian, indicating advancements in MT quality. However, consistent translation challenges were identified, such as cultural nuances and specific phrases in Mandarin. The tools’ usability and human-rated quality showed discrepancies, especially in Ukrainian with Microsoft CoPilot rated highly for translation quality but lower for usability. Limitations include evolving tool performance and a small sample size. Future research should explore additional languages, newer MT tools, and real-world applications in healthcare education.
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Enhancing Global Access to Critical Care Education: A Comprehensive Assessment of AI Machine Translation Tools | BMC Medical Education
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