Artificial intelligence anxiety among nursing and midwifery students: the role of resistance to change - a cross-sectional study

OBJECTIVE: This 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. METHODS: This 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. RESULTS: Students 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). CONCLUSION: Higher 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.