ArticleBMC nursing2025
Translation and psychometric validation of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Cross-cultural adaptation and psychometric evaluation of the artificial intelligence learning intention scale (AILIS) among medical sciences students: a methodological study.Medical education online · 2026Article
- Development and validation of the student readiness and anxiety scale for AI-assisted biology teaching (SRAS-AIBT).Scientific reports · 2026Article
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4 authors.
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Abstract
backgroundWith the rapid integration of artificial intelligence (AI) into medical education, assessing medical students' readiness has become critical. This readiness encompasses not only familiarity with AI tools but also the ability to apply, evaluate, and ethically reflect on them. Despite international advances, China currently lacks a validated instrument to systematically evaluate medical students' readiness for medical AI. Therefore, this study aimed to translate, culturally adapt, and evaluate the psychometric properties of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students.
methodsThe MAIRS-MS was translated into Chinese following Brislin's guidelines, with subsequent cultural adaptation informed by expert consultation. A pilot study was then conducted with 30 medical students to refine the Chinese version (C-MAIRS-MS). A cross-sectional survey was conducted among 516 undergraduate medical students from March to May 2025. The psychometric properties of the C-MAIRS-MS were evaluated through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), Cronbach's α coefficient, Spearman-Brown split-half reliability, and the intraclass correlation coefficient (ICC).
resultsThe C-MAIRS-MS included 22 items with scale content validity index (S-CVI) of 0.982. EFA extracted four factors explaining 65.274% of the total variance, with factor loadings ranging from 0.508 to 0.881. CFA results indicating that the revised 4-factor model was well fitted (χ²/df = 2.303, RMSEA = 0.071, CFI = 0.924, IFI = 0.925, and TLI = 0.912), with good structural validity. The Cronbach's α coefficient, Spearman-Brown Split-half reliability, and ICC values for the C-MAIRS-MS were 0.935, 0.832, and 0.945, indicating the scale has good reliability.
conclusionThe C-MAIRS-MS demonstrated sound psychometric properties and provides a reliable tool to assess medical students' readiness for medical AI. Beyond individual assessment, the scale can inform curriculum development, facilitate ongoing monitoring of students' progress, and support the evaluation of AI-focused educational programmes, thereby offering educators valuable evidence to guide the design and refinement of AI-related training in medical education. CLINICAL TRIAL NUMBER: Not applicable.
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