Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS).
Open accessAbstract
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.
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The full benefits of AI in patient care will be realized only if future physicians are equipped with theoretical and practical competencies.
“While AI can potentially aid in diagnosing, prescribing, and managing patient care, its full benefits will be realized if future physicians are equipped with theoretical and practical competencies [13–15].”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [15]
The Introduction (page unavailable) describes AI’s potential to improve patient care and calls it imperative to teach medical students technical and non-technical AI principles, including clinical applications. It does not establish that equipping future physicians with these competencies will, by itself, realize AI’s full benefits. (LLM, conf=0.80)
Experimental — accuracy not yet measuredAI can potentially aid in diagnosing, prescribing, and managing patient care.
“While AI can potentially aid in diagnosing, prescribing, and managing patient care, its full benefits will be realized if future physicians are equipped with theoretical and practical competencies [13–15].”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [15]
The Introduction says AI can help make and simplify diagnoses and improve patients’ clinical management. It does not specifically address prescribing, so that part of the attributed claim is unsupported by our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMAIRS-MS evaluates four domains of AI competencies and literacy: cognition, ability, vision, and ethics.
“This tool evaluates four domains of AI competencies and literacy: cognition, ability, vision, and ethics [35, 36].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [36]
Research such as Grassini (2023), Moodi et al. (2023), and Wang and Chuang (2023) generally reports high reliability coefficients for scales related to self-efficacy and attitudes toward artificial intelligence.
“Research such as that by Grassini (2023), Moodi et al. (2023), and Wang and Chuang (2023) generally report high reliability coefficients for scales related to self-efficacy and attitudes toward artificial intelligence.”
From Adaptation and psychometric properties of a brief version of the general self-efficacy scale for use with artificial intelligence (GSE-6AI) among university students (2024) · cites this paper as Moodi et al. (2023)
The results confirm the clarity and applicability of the adapted scale across different cultural contexts and populations.
“The results confirm the clarity and applicability of the adapted scale across different cultural contexts and populations, in line with research like that of Moodi et al. (2023), who analyzed the psychometric characteristics of a readiness scale for AI in medical students, demonstrating the usefulness of having specific assessment tools for different areas of AI application.”
From Adaptation and psychometric properties of a brief version of the general self-efficacy scale for use with artificial intelligence (GSE-6AI) among university students (2024) · cites this paper as Moodi et al. (2023)
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References 41 · Cited by 29
- Authors
- Moodi Ghalibaf, A., Moghadasin, M., Emadzadeh, A., & Mastour, H.
- Affiliations
- Kharazmi University (IR)
- Mashhad University of Medical Sciences (IR)
- Corresponding author
- Mastour, Haniye
- Venue
- BMC Medical Education
- Published
- 2023-08-15
- Volume
- 23
- Issue
- 1
- Pages
- 577-577
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC10428571
- OpenAlex
- W4385835312
- Cited by
- 29
- References
- 41
- Citations by year
- 2026: 13 · 2025: 12 · 2024: 4
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Mashhad University of Medical Sciences (992291)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- AI readiness, medical students, Persian translation, cross-cultural adaptation, psychometric validation, construct validity, reliability statistics, medical education, artificial intelligence in healthcare, Iran
- MeSH terms
- Adult, Artificial Intelligence, Female, Humans, Iran, Male, Psychometrics, Surveys and Questionnaires, Students, Medical, Reproducibility of Results, Young Adult
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