Validity and Reliability of the Medical Artificial Intelligence Readiness Scale, Korean Version: A Methodological and...

Area of Science:

• Medical Informatics
• Nursing Education
• Artificial Intelligence

Background:

• The integration of artificial intelligence (AI) in healthcare necessitates assessing readiness among future healthcare professionals.
• Existing tools may not be culturally relevant or validated for specific educational contexts like nursing in South Korea.

Purpose of the Study:

• To translate and culturally adapt the Medical Artificial Intelligence Readiness Scale for Medical Students into Korean.
• To evaluate the validity and reliability of the Korean version (K-MAIRS) among nursing students.

Main Methods:

• Methodological and cross-sectional study involving 317 nursing students in South Korea.
• Standard translation and cultural adaptation procedures followed by expert content validation.
• Exploratory and confirmatory factor analyses for structural validity; hypothesis testing for construct validity; Cronbach's alpha and split-half for reliability.

Main Results:

• The final K-MAIRS consists of 15 items across four factors: cognition, ability, vision, and ethics.
• These factors explained 66.88% of the variance in AI readiness.
• The K-MAIRS demonstrated good validity and reliability.

Conclusions:

• The K-MAIRS is a valid and reliable instrument for assessing artificial intelligence readiness in Korean nursing students.
• This culturally adapted tool supports global efforts to integrate AI competencies into nursing education.
• The K-MAIRS aids educators in developing targeted AI education strategies for nursing curricula.