Cultural Adaptation and Validation of the Medical Artificial Intelligence Readiness Scale for Medical Students Questionnaire in Indian Undergraduate Medical Education: A Mixed-methods Study

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Abstract Background: Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) is a scale developed in Turkey to measure artificial intelligence (AI) readiness among undergraduate medical students. This scale warrants stringent revalidation in different educational and cultural contexts. The study aimed to reassess the psychometric properties and applicability of the MAIRS-MS instrument in undergraduate medical education in India. Methodology: This study, conducted at a Central Indian medical college, employed a mixed-methods design and included 482 undergraduate medical students across different phases of the MBBS curriculum. The MAIRS-MS is a 22-item questionnaire with dimensions of cognition, ability, vision, and ethics. The analysis was performed on internal consistency using Cronbach’s alpha. The analysis for construct validity was performed using Pearson’s correlation. Thematic analysis was conducted on the qualitative insights resulting from the focus group discussions (FGDs). Results: The instrument demonstrated strong internal consistency across domains, with Cronbach’s alpha coefficients of 0.865 for cognition, 0.879 for ability, 0.763 for vision, and 0.812 for ethics; the overall scale demonstrated excellent reliability with an alpha of 0.923. Item-total correlations of all items were found to be significant ( P < 0.01). The FGDs showed enthusiasm for AI with notable gaps in ethical and legal understanding with unanimous recommendation for its introduction in the curriculum. Conclusion: The MAIRS-MS scale demonstrates robust psychometric properties within the Indian context and is a valid instrument for evaluating AI readiness in medical students. These findings support the structured incorporation of AI education into the undergraduate medical curriculum.

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