ArticleBMC medical education2025
Artificial intelligence and academic writing questionnaire (AI-AWQ): development and validation among medical students' experiences using exploratory factor analysis.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Generative AI-Assisted Academic Writing Experience and University Students' Academic Self-Efficacy: Parallel Indirect Associations Through Academic Emotions.Journal of Intelligence · 2026Article
- Psychometric Evaluation of the Artificial Intelligence Perception Scale for Cancer Patients: A Structural Equation Modeling Approach.Journal of evaluation in clinical practice · 2026Article
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5 authors.
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Abstract
introductionThis study aimed to develop and validate the Artificial Intelligence and Academic Writing Questionnaire (AI-AWQ) to assess participants' perceptions with AI. The primary focus was to explore the factors that influence attitudes toward AI in educational settings.
methodsThis study utilized a mixed-methods approach to develop and validate the psychometric properties of the AI-AWQ. The questionnaire, consisting of 30 items rated on a 5-point Likert scale (1 = Never, 5 = Always), was administered to a sample of 252 medical and dental students at Shiraz University of Medical Sciences, Iran, who had taken the academic writing course during the 2023-2024 academic year. Data were analyzed using Exploratory Factor Analysis (EFA), with Varimax rotation employed to clarify the underlying factors.
resultsA total of 252 completed questionnaires were analyzed, of which 59.5% were from Iranian students and the remaining respondents were international students. The results of the exploratory factor analysis demonstrated satisfactory sampling adequacy (KMO = 0.930) and a significant Bartlett's test of sphericity (P < 0.001), confirming the suitability of the data for factor analysis. Construct validity testing led to the extraction of five distinct factors-Perceived Effectiveness of AI, Ethical and Authenticity Concerns, AI-Supported Writing Process, AI Feedback and Writing Enhancement, and Affective and Motivational Impact-which together accounted for 77.99% of the total variance. The questionnaire demonstrated strong validity and reliability, with a Content Validity Index (CVI) of 0.903, a Content Validity Ratio (CVR) of 0.882, and an overall internal consistency confirmed by a Cronbach's alpha of 0.883.
conclusionThe findings suggest that the AI-AWQ provides preliminary evidence of reliability and validity for measuring perceptions of AI, offering insights into the multi-faceted nature of AI and academic writing. This study contributes to understanding the factors shaping individuals' views on AI in educational contexts and provides a foundation for further research.
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