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Translation, cultural adaptation, and validation of the perceived Artificial Intelligence Literacy Questionnaire-6
Ammar Abdulrahman Jairoun1,2, Moyad Shahwan3,4, Abeer M Al-Ghananeem5
1Health and Safety Department, Dubai Municipality, Dubai, United Arab Emirates.
Background:
The growing use of artificial intelligence (AI) in healthcare and pharmacy has increased the need to determine whether pharmacists are prepared to understand, assess, and use AI-supported technologies responsibly. Arabic-speaking pharmacy settings currently lack a validated Arabic instrument specifically designed to assess pharmacists' perceived AI literacy. This study aimed to translate, culturally adapt, and validate the Arabic version of the Perceived Artificial Intelligence Literacy Questionnaire (PAILQ-6) and to examine demographic and professional factors associated with perceived AI literacy among pharmacists.
Methods:
A cross-sectional methodological validation study was conducted among 500 pharmacists practicing in Arabic-speaking countries. Data were collected using an anonymous online self-administered questionnaire. Translation and cultural adaptation followed the seven-step framework described by Sousa and Rojjanasrirat. Psychometric evaluation included confirmatory factor analysis using maximum likelihood (ML) and robust maximum likelihood estimation, internal consistency assessment, convergent validity testing, and measurement invariance analysis across gender. Multivariable linear regression was used to examine independent factors associated with perceived AI literacy.
Results:
The Arabic PAILQ-6 retained the original one-factor structure and demonstrated strong psychometric performance. The robust model showed excellent fit (Satorra-Bentler χ2 = 11.95, df = 9, p = 0.216; CFI = 0.994; NNFI = 0.990; RMSEA = 0.026). Standardized factor loadings ranged from 0.635 to 0.782. Internal consistency was high, with Cronbach's α = 0.862 and composite reliability = 0.863, and convergent validity was supported. Configural, metric, and scalar invariance were established across gender, indicating equivalent measurement among male and female pharmacists. In the adjusted regression model, previous AI experience (β = 0.131, p = 0.004) and industrial pharmacy practice (β = 0.157, p = 0.007) were independently associated with higher perceived AI literacy. Gender, age, years of professional experience, and other pharmacy practice settings were not significant independent predictors.
Conclusions:
The Arabic PAILQ-6 demonstrated satisfactory validity, reliability, and measurement invariance across gender for assessing perceived AI literacy among pharmacists in Arabic-speaking countries. The findings should be interpreted in light of the cross-sectional design, convenience sampling, and reliance on self-reported perceived AI literacy, which limit causal interpretation and may affect generalizability. The instrument may be useful for identifying training needs, supporting pharmacy curriculum development, evaluating educational initiatives, and assessing workforce preparedness for the responsible use of AI. Previous exposure to AI technologies was associated with higher perceived AI literacy.
