Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

AmirAli Moodi Ghalibaf; Maryam Moghadasin; Ali Emadzadeh; Haniye Mastour

doi:10.1186/s12909-023-04553-1

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)

BMC Med Educ. 2023 Aug 15;23(1):577. doi: 10.1186/s12909-023-04553-1.

Authors

AmirAli Moodi Ghalibaf¹, Maryam Moghadasin², Ali Emadzadeh³, Haniye Mastour⁴

Affiliations

¹ Student Research Committee, Faculty of Medicine, Birjand University of Medical Sciences, Birjand, Iran.
² Department of Clinical Psychology, Faculty of Psychology and Education, Kharazmi University, Tehran, Iran.
³ Department of Medical Education, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
⁴ Department of Medical Education, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Haniye.Mastour@gmail.com.

Abstract

Introduction: There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results.

Methods: In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance.

Results: Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively.

Conclusions: The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students' readiness for medical artificial intelligence.

Keywords: Artificial intelligence; Medical education; Medical students; Psychometric properties; Validity and reliability.

MeSH terms

Adult
Artificial Intelligence
Female
Humans
Iran
Male
Psychometrics
Reproducibility of Results
Students, Medical*
Surveys and Questionnaires
Young Adult

Grants and funding

992291/Mashhad University of Medical Sciences