Psychometric Properties of the Chinese Version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

This study aimed to evaluate the psychometric properties of the Chinese version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) and assess its applicability among undergraduate students in medicine and health-related disciplines in China. Using Brislin’s translation model, we translated and culturally adapted the original (English) scale to produce the Chinese version. Between May and July 2024, we used convenience sampling to recruit undergraduate students from a medical university in Changsha City, Hunan Province, China (population size = 520). We collected a total of 480 valid responses (participation rate = 92.3%) through the Wenjuanxing platform. We analyzed data using R and AMOS 29.0. Content validity was supported by two rounds of Delphi expert consultation, with item-level content validity indices (I-CVI) ranging from 0.80 to 1.00 and a scale-level index (S-CVI) of 0.95. The Chinese version of the MAIRS-MS showed good internal consistency, with a total Cronbach’s alpha coefficient of 0.90 and subscale coefficients all exceeding 0.78. The split-half reliability was 0.94, and the test-retest reliability was 0.95. Exploratory factor analysis supported the instrument’s original four-factor structure—Cognition, Ability, Vision, and Ethics—with a KMO value of 0.93, cumulative variance explained of 67.2%, and all item loadings greater than 0.40. Confirmatory factor analysis indicated a good model fit (χ2/df = 1.11, RMSEA = 0.02, CFI = 0.93, TLI= 0.99). In sum, the Chinese version of the MAIRS-MS demonstrated satisfactory content validity, internal consistency, and structural validity, supporting its use as a reliable tool for assessing AI readiness among Chinese undergraduate health professional students.