Cross-cultural adaptation and psychometric evaluation of the Japanese version of the scale for the assessment of non-experts’ AI literacy among medical trainees in a multicenter cross-sectional study

This cross-sectional study aimed to develop the Japanese version of the "Scale for the assessment of non-experts' AI literacy" (J-SNAIL) and provide its initial psychometric evidence. In 2025, after translating the SNAIL into Japanese according to an international cross-cultural adaptation guideline, we distributed the translated scale as an anonymous online self-administered questionnaire to medical students in five universities and residents in nine hospitals across Japan. We tested the questionnaire's structural and convergent validity and internal consistency reliability. 326 participants were included in the analysis. Exploratory factor analysis led to a modified 25-item J-SNAIL comprising 3 factors, including "technical understanding," "critical and ethical appraisal," and "risk awareness." Notably, the original SNAIL's "practical application" factor was not identified; instead, "risk awareness" factor emerged, which may reflect linguistic, cultural, or educational contextual differences. The J-SNAIL total score had a significantly positive correlation with the global rating scale for measuring AI literacy, which indicated good convergent validity. Cronbach's alpha value for the total of 25 items was 0.92, indicating good internal consistency reliability of the J-SNAIL. Future investigation of the scale may strengthen factor construction. J-SNAIL may contribute to future educational initiatives and research on AI literacy in Japan.

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Publication Details

Journal
Scientific Reports
Published
2026-07-17
DOI
https://doi.org/10.1038/s41598-026-62794-8
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Cross-cultural adaptation and psychometric evaluation of the Japanese version of the scale for the assessment of non-experts’ AI literacy among medical trainees in a multicenter cross-sectional study

Hirohisa Fujikawa, Kayo Kondo, Y N Nishizaki, Yuichiro Yano et al.
Scientific Reports
Artificial Intelligence in Healthcare and Education
article

Cross-cultural adaptation and psychometric evaluation of the Japanese version of the scale for the assessment of non-experts’ AI literacy among medical trainees in a multicenter cross-sectional study

Hirohisa Fujikawa, Kayo Kondo, Y N Nishizaki, Yuichiro Yano, Hirotake Mori, Toshio Naito
article en

Abstract

This cross-sectional study aimed to develop the Japanese version of the "Scale for the assessment of non-experts' AI literacy" (J-SNAIL) and provide its initial psychometric evidence. In 2025, after translating the SNAIL into Japanese according to an international cross-cultural adaptation guideline, we distributed the translated scale as an anonymous online self-administered questionnaire to medical students in five universities and residents in nine hospitals across Japan. We tested the questionnaire's structural and convergent validity and internal consistency reliability. 326 participants were included in the analysis. Exploratory factor analysis led to a modified 25-item J-SNAIL comprising 3 factors, including "technical understanding," "critical and ethical appraisal," and "risk awareness." Notably, the original SNAIL's "practical application" factor was not identified; instead, "risk awareness" factor emerged, which may reflect linguistic, cultural, or educational contextual differences. The J-SNAIL total score had a significantly positive correlation with the global rating scale for measuring AI literacy, which indicated good convergent validity. Cronbach's alpha value for the total of 25 items was 0.92, indicating good internal consistency reliability of the J-SNAIL. Future investigation of the scale may strengthen factor construction. J-SNAIL may contribute to future educational initiatives and research on AI literacy in Japan.

Scientific Reports
Tokyo Medical and Dental University (JP), Durham University (GB), Keio University (JP), Juntendo University (JP), Saitama International Medical Center (JP), Keio University Shonan Fujisawa (JP)
Ministry of Education, Culture, Sports, Science and Technology
Quality Education
Openalex Percentile: Top 11%
Artificial Intelligence in Healthcare and Education
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