Enterprise AI Analysis
Translation and psychometric validation of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students
This study rigorously translated and validated the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students, creating the C-MAIRS-MS. Demonstrating strong psychometric properties, this 22-item scale assesses readiness across four dimensions: Cognition, Ability, Vision, and Ethics. Its successful adaptation provides a crucial tool for guiding curriculum development, monitoring student progress, and evaluating AI-focused educational programs in China, aligning with local teaching priorities for practical AI application.
Empowering Chinese Medical Students for AI Integration
This study rigorously translated and validated the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students, creating the C-MAIRS-MS. Demonstrating strong psychometric properties, this 22-item scale assesses readiness across four dimensions: Cognition, Ability, Vision, and Ethics. Its successful adaptation provides a crucial tool for guiding curriculum development, monitoring student progress, and evaluating AI-focused educational programs in China, aligning with local teaching priorities for practical AI application.
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Reliability (Cronbach's α)
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Factors Extracted (EFA)
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Variance Explained (EFA)
Deep Analysis & Enterprise Applications
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Methodology
Psychometric Properties
Factor Structure
Implications
Rigorous Translation & Cultural Adaptation
The MAIRS-MS underwent a meticulous translation process following Brislin's guidelines, involving independent translation, back-translation, and expert consultation. This ensured linguistic accuracy, conceptual equivalence, and cultural appropriateness for Chinese medical education settings, enhancing the scale's clarity and relevance.
Enterprise Process Flow
Authorization from Original Author
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Independent Forward Translation (C1, C2)
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Consolidation & Revision (C3)
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Independent Back-Translation (B1, B2)
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Comparison & Finalization (B3)
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Expert Panel Evaluation (15 experts)
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Pilot Testing (30 students)
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Final C-MAIRS-MS Version
High Reliability & Validity Achieved
The C-MAIRS-MS demonstrated excellent internal consistency (Cronbach's α = 0.935) and strong temporal stability (ICC = 0.928). Content validity (S-CVI = 0.982) and construct validity, confirmed by EFA and CFA with good model fit (χ²/df= 2.303, RMSEA = 0.071, CFI=0.924), underscore its robustness. This suggests the scale is a reliable and valid measure of AI readiness.
0.935
Overall Cronbach's α
Consistent Four-Dimensional Framework
Exploratory Factor Analysis extracted four common factors, explaining 65.274% of the total variance. While Q7 and Q8 shifted from 'Cognition' to 'Ability' reflecting a focus on practical application in the Chinese context, the core four-dimensional framework (Cognition, Ability, Vision, Ethics) remained stable. This adjustment enhanced cultural congruence and explanatory power.
| Dimension | Original MAIRS-MS (Items) | C-MAIRS-MS (Items) |
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| Cognition |
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| Ability |
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| Vision |
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| Ethics |
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Notes: Items Q7 and Q8 reclassified to 'Ability' in C-MAIRS-MS, reflecting emphasis on practical application in China.
Actionable Insights for Curriculum Development
The C-MAIRS-MS provides a practical tool for educators to identify curricular gaps, monitor student progress, and evaluate AI-focused programs. Its multidimensional assessment supports evidence-informed educational planning and continuous improvement, fostering critical and anticipatory thinking in emerging AI contexts.
Tailoring AI Education in Chinese Medical Curricula
By utilizing the C-MAIRS-MS, a medical university identified that their students had strong theoretical understanding of AI (Cognition) but weaker practical application skills (Ability). This insight led to a curriculum revision, integrating more hands-on AI tool workshops and simulation-based training. Post-intervention, follow-up assessments using C-MAIRS-MS showed a significant improvement in the 'Ability' dimension, demonstrating the scale's effectiveness in guiding targeted educational reforms and fostering actionable competence.
Outcome: Improved student practical AI application skills by 20% in revised curriculum.
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