Abstract
BACKGROUND: Artificial intelligence (AI) has recently entered the medical field, but the level of readiness of medical students for it is not obvious. A tool with appropriate psychometric properties for use in different languages and for international comparison is required to measure this readiness. Medical Artificial Intelligence Readiness Scale for medical students (MAIRS-MS) is most complete scale for this purpose till now. OBJECTIVES: The purpose was to evaluate the Psychometric properties of the Persian-translated version of the MAIRS-MS and verify the replication of the original factor structure in Persian. MATERIALS AND METHODS: This study was conducted at Guilan University of Medical Sciences in 2023. Validation of the Persian translated scale (P-MAIRS-MS) was performed by determining the face, content, and construct validity and reliability, impact Score, CVI, CVR, Cronbach's alpha, McDonald's omega, and ICC, and performing confirmatory factor analysis (CFA). AMOS26 and SPSS26 software were used. RESULTS: The translated scale had good quantitative and qualitative face and content validity (all items had the Impact Score higher than 1.5, CVI >= 0.8 and CVR>= 0.8). CFA confirmed the appropriate fit of the four-factor model (χ2/df = 1.963, RMSEA-0.063, CFI = 0.939, GFI = 0.901). Convergent validity was suitable in the first- and second-order CFA (AVE > 0.5, CR > 0.7 CR > AVE for each factor except Ability). Cronbach's alpha (α=0.938) and McDonald's omega (ω= 0.938), and ICC (0.992) indicated acceptable reliability and reproducibility of the scale. CONCLUSION: The P-MAIRS-MS demonstrated good psychometric properties and can be used for measuring and international comparing the medical students' readiness for AI.