BMC Nurs. 2026 Jul 18. doi: 10.1186/s12912-026-05053-5. Online ahead of print.
ABSTRACT
INTRODUCTION: As artificial intelligence (AI) becomes more common in healthcare, nursing students need to be both mentally and emotionally ready to use it. But feeling anxious about technology might hold them back. This study looked at whether there is a link between AI Readiness and technology anxiety among nursing students.
METHODS: We carried out a descriptive-correlational study with 297 nursing students at Qom University of Medical Sciences during the 2024-2025 academic year. We used census sampling, meaning we invited all eligible students to take part. Data were collected using a demographic form, the Abbreviated Technology Anxiety Scale (ATAS), and the Medical AI Readiness Scale for Medical Students (MAIRS-MS). Data were analyzed using descriptive statistics, Pearson correlation, independent t-tests, and multiple linear regression to identify predictors of AI Readiness.
RESULTS: The mean scores were 3.76 ± 0.54 for technology anxiety and 3.68 ± 0.61 for AI Readiness, indicating moderate-to-high levels. A strong inverse correlation was found between AI Readiness and technology anxiety (r = – 0.648, p < 0.001). Multiple linear regression showed that AI Readiness was a significant negative predictor of technology anxiety (B = – 0.32, p < 0.001), explaining 42% of the variance (R² = 0.420). No significant differences were observed based on gender, age, or marital status. Students in higher academic years reported lower technology anxiety (r = – 0.121, p = 0.026), and master’s students demonstrated significantly higher AI Readiness compared to bachelor’s students (p = 0.011).
CONCLUSION: These findings show that nursing students who feel more prepared for AI tend to be less anxious about technology. Adding AI training more consistently throughout the nursing curriculum and building digital skills may help reduce fear and make it easier for students to use AI tools in the future. Further longitudinal and interventional studies are needed to better understand causal relationships and to identify effective educational strategies.
PMID:42471688 | DOI:10.1186/s12912-026-05053-5
