Findings show that nursing students who feel more prepared for AI tend to be less anxious about technology and add 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.
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.
Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, offering significant opportunities to improve clinical decision-making, patient care, and healthcare efficiency. As AI applications continue to expand across healthcare settings, nursing students must develop adequate awareness and readiness to effectively utilize these technologies in future professional practice.
Aim: This study aimed to assess the level of awareness and readiness toward artificial intelligence among nursing students and to identify the factors associated with AI readiness.
Methods: A quantitative cross-sectional survey was conducted among 396 nursing students at Riyadh Elm University, Saudi Arabia. Data were collected using a structured questionnaire consisting of demographic characteristics, the Artificial Intelligence Awareness Scale, and the Artificial Intelligence Readiness Scale. Descriptive statistics, independent t-test, one-way ANOVA with Tukey HSD post hoc analysis, Pearson correlation, and multiple linear regression were performed using SPSS version 27. Statistical significance was set at p < 0.05.
Results: Nursing students demonstrated a moderate level of AI awareness (mean = 3.43 ± 0.44, 68.57%) and a high level of AI readiness (mean = 3.93 ± 0.45, 78.52%). Significant differences in AI awareness were observed according to academic level and self-rated computer skills. AI readiness was significantly associated with age, academic level, previous use of AI-related tools, and self-rated computer skills. A strong positive correlation was found between AI awareness and AI readiness (r = 0.685, p < 0.001). Multiple linear regression analysis identified AI awareness as the strongest independent predictor of AI readiness (B = 0.681, p < 0.001), with the model explaining 56.9% of the variance in AI readiness (R² = 0.569).
Conclusion: Nursing students demonstrated positive readiness toward artificial intelligence despite having only moderate awareness. Integrating AI-related education, practical training, and digital competency development into undergraduate nursing curricula may enhance students' awareness and strengthen their readiness to effectively utilize AI technologies in future nursing practice.
Saada Taktikh Alshammary, Fatimah Salman Zoghbi, D. Alanazi et al.· International Journal of Adv...· 0 citations
Initial evidence is provided that the NAIRS is a valid and reliable instrument for assessing nursing students' readiness for artificial intelligence across knowledge/awareness, willingness to use AI, self-efficacy, and ethical awareness domains and may be useful for educational needs assessment and curriculum planning in nursing education.
Sumeyye Akçoban, Gülay Koca, S. Berşe· BMC Nursing· 0 citations
Background The rapid advancement of artificial intelligence is driving an unprecedented technological transformation in nursing. However, the successful integration of these technologies depends largely on the proficiency and perspectives of registered nurses. Consequently, there is an urgent need to examine the psychological and behavioral responses of this workforce. Methods A multi-center, cross-sectional survey was conducted from March to May 2026. a stratified convenience sampling method was employed to recruit 1,392 registered nurses from tertiary hospitals, secondary hospitals, and community health centers in Chongqing. Data collection instruments included a general demographic questionnaire, the Artificial Intelligence Literacy Scale, the Artificial Intelligence Anxiety Scale, and the General Attitudes Towards Artificial Intelligence Scale. Statistical analyses, including descriptive statistics, Spearman correlation, and multiple linear regression. Results A total of 1,392 registered nurses participated in the study, with a mean age of 34.69 ± 7.13 years. AI literacy scored 5.27 ± 0.90 (75.29% scoring rate). AI anxiety was moderate (51.29%), with the highest concerns appearing in socio-technical blindness (56.43%) and job replacement (55.14%). Overall, nurses maintained a positive attitude toward AI (74.00%). AI literacy was negatively correlated with anxiety (r = −0.338, p < 0.001) and significantly positively correlated with attitude (r = 0.551, p < 0.001), anxiety and attitude were negatively correlated (r = −0.541, p < 0.001). Regression analysis indicated that AI literacy was a significant associated factor for both anxiety levels (B = -0.453, p < 0.001) and attitudes (B = 0.377, p < 0.001), explaining 9.9 and 29.4% of the variance, respectively. Additionally, a lower frequency of electronic device use (p = 0.024) and lack of proficiency in device operation (p = 0.033) were associated with higher anxiety levels, whereas male nurses demonstrated more positive attitudes compared to female nurses (p = 0.011). Conclusion Nurses demonstrated high AI literacy and positive attitudes; however, anxiety remained prominent. Enhancing AI literacy may alleviate psychological anxiety, with device accessibility and usage patterns also playing critical roles. To facilitate the effective integration of artificial intelligence into clinical practice, administrators should strengthen institutional support mechanisms alongside providing facility resources and conventional education, thereby promoting the full utilization and translation of available resources.
Yi Dai, Xili Zhao, Xiaochong Pan et al.· Frontiers in Public Health· 0 citations
The findings indicate that nursing students had generally positive levels of AI literacy and attitudes toward AI, and higher AI literacy was associated with more positive attitudes toward AI.
M. Çil, Berna Eren Fidancı, D. Yildiz· Journal of Education and Res...· 0 citations
In nursing practice, clinical decision-making is a critical competency that students must possess to ensure patient safety and the quality of nursing care. This process is influenced not only by cognitive abilities but also by emotional intelligence, which helps students remain objective when facing complex and high-pressure clinical situations. Although various studies have examined the relationship between the two, the results obtained remain inconsistent. Therefore, this study aims to analyze the relationship between emotional intelligence and clinical decision-making among students in the Professional Nursing Program using a descriptive correlational design and a cross-sectional approach. A total of 107 students from three nursing education institutions in Banda Aceh were selected as participants using consecutive sampling. Emotional intelligence was measured using the Schutte Self-Report Emotional Intelligence Test, while clinical decision-making ability was measured using the Clinical Decision-Making in Nursing Scale. Data analysis was performed using Spearman’s correlation test. The results of the study indicate a positive and significant relationship between emotional intelligence and clinical decision-making (r=0.259; p= 0.007). Among all dimensions of emotional intelligence, Understanding of Emotion showed the strongest and most significant relationship with all components of clinical decision-making (p = 0.001), whereas the dimensions of Utilisation of Emotion and Regulation of Emotion did not show a significant relationship. Emotional intelligence plays a role in supporting clinical decision-making abilities among students in the Professional Nursing Program. Therefore, the development of emotional intelligence needs to be integrated into nursing education to support the improvement of students’ clinical decision-making abilities.
Irfanita Nurhidayah, Yullyzar, Nani Safuni et al.· Jurnal Ners dan Kebidanan (J...· 0 citations
The Turkish version of the FtAIS is a valid and reliable instrument for assessing fear of AI among nursing students and demonstrates acceptable content and construct validity and good internal consistency.
Şebnem Bilgiç, İdi̇l Şahi̇n· Journal of Nursing Measureme...· 0 citations