ArticleBMC medical education2026
Artificial intelligence anxiety among nursing and midwifery students: the role of resistance to change - 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. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Compassion and Self-Compassion in Relation to Nursing Students' Attitudes Toward Artificial Intelligence.Nursing open · 2026Article
- The relationship between AI anxiety and academic motivation among university students: the mediating role of emotion regulation and the moderating role of gender.Frontiers in psychology · 2026Article
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Authors and funding
2 authors.
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Abstract
objectiveThis study aimed to determine the levels of resistance to change and artificial intelligence (AI) anxiety among nursing and midwifery students and to examine the association between these variables.
methodsThis cross-sectional, descriptive-correlational study was conducted with 413 nursing and midwifery students enrolled at a state university during the 2024–2025 academic year. Data were collected using a Personal Information Form, the Resistance to Change Scale, and the Artificial Intelligence Anxiety Scale. Descriptive statistics, independent samples t-tests, one-way ANOVA, Pearson correlation analysis, and multiple linear regression analysis were performed.
resultsStudents demonstrated moderate levels of resistance to change (Mean = 39.15 ± 7.99) and moderate levels of AI anxiety (Mean = 44.62 ± 11.04). Based on the recommended cut-off score of 48, 45.8% of participants (n = 189) were classified as experiencing high AI anxiety, while 54.2% (n = 224) had low-to-moderate levels of AI anxiety. Among AI anxiety subdimensions, the highest mean score was observed in sociotechnical blindness. Resistance to change was positively associated with AI anxiety (r = 0.296, p < 0.01). In the regression model, resistance to change showed the strongest association with AI anxiety (B = 0.47, p < 0.001), while gender and academic department were also significantly associated. The final model explained 14.1% of the variance in AI anxiety (R² = 0.141).
conclusionHigher levels of resistance to change are associated with increased AI anxiety among nursing and midwifery students. These findings suggest that resistance to change represents a modifiable psychological factor that may be addressed through educational interventions, alongside technical AI skill development, within digital transformation initiatives in health professions education.
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