Cross-cultural adaptation and psychometric evaluation of the artificial intelligence learning intention scale (Ailis) among medical sciences students

A methodological study

Bibliographic Data

ID21650337
AuthorsAmir Hossein Taghiparast (0009-0009-1484-7118, Student Research Committee, Nursing Care Research Center, Health Research Institute, Babol University of Medical Sciences), Amaneh Mahmoudian (0000-0003-1855-8208, Nursing Care Research Center, Health Research Institute, Babol University of Medical Sciences), Maryam Ghaemi-Amiri (0000-0003-2807-7790, Social Determinants of Health Research Center, Health Research Institute, Education Development Center, Department of Medical Sciences Education Development, Babol University of Medical Sciences), Fatemeh Ghaffari (0000-0003-1940-1579, Nursing Care Research Center, Health Research Institute, Babol University of Medical Sciences, corresponding author)
Year2026
Volume31
Issue1
Pages2690326-2690326
Publication date2026-12-31
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueMedical Education Online (JOURNAL)
Journal identifiersISSN: 1087-2981 • E-ISSN: 1087-2981
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10872981.2026.2690326
PMID42316367
OpenAlexW7165144827
LanguageEN
References cited46

BACKGROUND: This study aimed to adapt and psychometrically evaluate the Artificial Intelligence Learning Intention Scale (AILIS) among Iranian medical sciences students. METHODS: This methodological psychometric study was conducted among 800 medical sciences students at Babol University of Medical Sciences, Iran. The AILIS was translated and culturally adapted into Persian using a standardized cross-cultural adaptation process. Construct validity was examined through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability was assessed using Cronbach's alpha, McDonald's omega, and the intraclass correlation coefficient (ICC). Convergent and discriminant validity were evaluated using composite reliability (CR) and average variance extracted (AVE). Measurement invariance across gender and structural stability were assessed using multi-group confirmatory factor analysis and exploratory graph analysis (EGA). RESULTS: EFA supported a four-factor structure consisting of Epistemic Capacity, Psychological Attitudes, Facilitating Environments, and Psychological and Behavioral Outcomes, explaining 44.55% of the total variance, with factor loadings greater than 0.40. Confirmatory factor analysis indicated an acceptable model fit, supporting the proposed factor structure (χ2/df = 3.42, RMSEA = 0.078, CFI = 0.932). Internal consistency and test-retest reliability were satisfactory, with Cronbach's alpha ranging from 0.854 to 0.904, McDonald's omega from 0.854 to 0.899, and ICC from 0.750 to 0.860. Composite reliability values were adequate, while AVE values indicated acceptable but partially limited evidence of convergent validity. Discriminant validity was generally supported. In addition, multi-group analysis demonstrated measurement invariance across gender, and exploratory graph analysis confirmed the structural stability of the four-factor model. CONCLUSION: The Persian AILIS is a valid and reliable instrument for assessing AI learning intention among medical sciences students. Beyond its psychometric robustness, the instrument may support curriculum planning, evaluation of AI-related educational interventions, and evidence-based decision-making in medical sciences education by providing a standardized measure of students' intentions to engage with AI learning

Confirmatory factor analysis · Construct validity · Discriminant validity · Exploratory factor analysis · Psychometrics · Structural equation modeling · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · Educational Games and Gamification

Tools

Open DOIOpen Access