ArticleBMC medical education2026
Clinical learning experiences and artificial intelligence-related anxiety among midwifery students: a cross-sectional study.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundArtificial intelligence (AI) is rapidly transforming healthcare practice and education, requiring students to adapt to technology-supported clinical environments. Although AI-related anxiety has been examined among nursing students, little is known about how clinical learning experiences relate to AI-related anxiety among midwifery students. This study aimed to examine the relationship between clinical learning experiences and AI-related anxiety in undergraduate midwifery education.
methodsThis cross-sectional study was conducted with 210 undergraduate midwifery students in Türkiye who were actively engaged in clinical training. Data were collected using validated measures assessing perceptions of the clinical learning environment and AI-related anxiety. Pearson correlation and simple linear regression analyses were performed to examine associations between clinical learning experiences and AI-related anxiety and its subdimensions.
resultsPerceptions of the clinical learning environment were negatively associated with overall AI-related anxiety, job displacement anxiety, socio-technical blindness, and AI configuration anxiety (p < 0.05). No significant association was observed between clinical learning experiences and learning-related AI anxiety. Clinical learning experiences significantly predicted lower levels of overall AI-related anxiety.
conclusionsClinical learning experiences appear to be associated with AI-related anxiety among midwifery students, particularly in relation to professional roles and system-level adaptation rather than learning difficulties. Strengthening clinical learning environments and integrating AI awareness into clinical education may support students' preparedness for AI-enhanced healthcare practice.
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