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.
Abstract
Background
The rapid integration of artificial intelligence (AI) into healthcare has amplified the need for nurses who can engage with AI-supported systems safely and effectively. Assessing nursing students' AI readiness through a psychometrically sound instrument is essential for guiding curriculum design and targeted educational interventions. This study aimed to develop and psychometrically evaluate the Nursing Artificial Intelligence Readiness Scale (NAIRS) for nursing students.
Methods
This methodological scale development study was conducted with 416 nursing students enrolled during the 2025-2026 academic year at a public university in Türkiye. To avoid conducting exploratory and confirmatory factor analyses on the same dataset, the full sample was randomly split into two independent subsamples (EFA: n = 200; CFA: n = 216). The two subsamples were comparable in key demographic and AI-related characteristics. An initial 40-item draft was generated from the literature and reviewed by 10 faculty experts using a content validity evaluation; the mean CVR was 0.88. Following expert review, five items were removed, reducing the pool to 35 items. The revised draft was then pilot tested with 50 nursing students to assess item clarity, readability, comprehensibility, and administration flow, and minor revisions were made prior to the main application. The final 20-item scale was structured across four theoretical domains: Knowledge/Awareness, Willingness to Use AI, Self-efficacy, and Ethical Awareness. Construct validity was examined using EFA (minres extraction; Promax rotation) and first-order CFA (ML estimation) with multiple model fit indices. Convergent and discriminant validity were assessed using CR/AVE and the Fornell-Larcker criterion, respectively. Internal consistency was evaluated with Cronbach's alpha in both subsamples, and temporal stability was tested via a 2-week test-retest application in a subgroup (n = 45) using ICC (two-way random, single measures).
Results
The final scale demonstrated a four-factor structure explaining 52.08% of the total variance, with factor loadings ranging from 0.351 to 0.909. CFA supported the proposed model with good fit (χ²/df = 1.426, CFI = 0.971, TLI = 0.967, GFI = 0.910, RMSEA = 0.044, SRMR = 0.049). Internal consistency was high: subscale alphas ranged 0.801-0.828 (EFA sample) and 0.858-0.902 (CFA sample), and the total scale alpha was 0.911. Test-retest reliability indicated strong stability, with ICC values ranging 0.896-0.963 across subscales and 0.952 for the total scale.
Conclusions
The findings provide initial evidence 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. The scale may be useful for educational needs assessment and curriculum planning in nursing education.
CLINICAL TRIAL NUMBER
Not applicable.
Artificial Intelligence (AI) has emerged as a valuable educational tool that supports nursing students’ learning
processes, clinical preparation, and independent learning. However, the mechanism by which AI use contributes to the
development of clinical competence remains underexplored. This study aimed to determine the mediating role of selfregulated learning in the relationship between the use of artificial intelligence and clinical competence among nursing
students. The study was conducted among Bachelor of Science in Nursing students enrolled during Academic Year 2025–
2026 at a higher education institution in Western Mindanao, Philippines. An explanatory sequential mixed-methods design
was utilized. The quantitative phase involved 252 nursing students selected through simple random sampling, while the
qualitative phase involved eight (8) purposively selected participants who participated in semi-structured interviews. Data
were collected using three researcher-adapted questionnaires measuring AI utilization, self-regulated learning, and clinical
competence, along with an interview guide for the qualitative component. Quantitative data were analyzed using Jamovi
software, including frequencies, percentages, means, standard deviations, Pearson product-moment correlations, and
mediation analyses.
Irina Xanthia S. Ramirez, Christine Grace S. Duaves, Genelyn R. Baluyos· International Journal of Inn...· 0 citations
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/Objectives: As artificial intelligence (AI) becomes increasingly integrated into healthcare, AI literacy and critical thinking disposition are recognized as important competencies for nursing students in rapidly changing clinical environments. This study aimed to examine the associations of AI literacy and critical thinking disposition with clinical competence among nursing students. Methods: A descriptive cross-sectional study was conducted with 200 nursing students with clinical practice experience who were enrolled in nine nursing colleges located in D City and K Province, Republic of Korea. Data were collected using a structured questionnaire and analyzed using IBM SPSS/WIN 25.0. Statistical analyses included descriptive statistics, t-tests, analysis of variance (ANOVA), Scheffé post hoc tests, Pearson’s correlation coefficients, and multiple regression analysis. Results: Satisfaction with clinical practice (β = 0.35, p < 0.001), critical thinking disposition (β = 0.34, p < 0.001), AI literacy (β = 0.23, p = 0.001), and academic year (β = 0.15, p = 0.009) were significantly associated with clinical competence. The model explained 40% of the variance in clinical competence (R2 = 0.40, adjusted R2 = 0.39, F = 26.11, p < 0.001). Conclusions: The findings indicate that critical thinking disposition and AI literacy were significantly associated with clinical competence among nursing students. Nursing education may therefore consider strategies that address both competencies.
Y. Jeong, Nayoung Kim, Seongha Park et al.· Nursing Reports· 0 citations
BACKGROUND
With the rapid integration of artificial intelligence (AI) into the nursing field, AI literacy has emerged as a critical competency for nursing students. However, evidence regarding the current status and influencing factors of AI literacy among Chinese nursing students remains limited.
AIM
This cross-sectional study aimed to examine AI literacy levels across four dimensions (Awareness, Application, Evaluation, Ethics) among Chinese nursing students, identify its independent influencing factors, and generate localized empirical evidence for AI curriculum construction in vocational nursing education.
METHODS
A cross-sectional study was conducted between November 1, 2025 and January 20, 2026. The research institution has not set up an institutional ethics committee; the whole protocol was ethically reviewed by the supervisor team in line with the Declaration of Helsinki before recruitment. A total of 178 sophomore nursing students from Henan Vocational University of Science and Technology were enrolled via convenience sampling. Data were collected using the Artificial Intelligence Literacy Scale (AILS, Wang et al., 2023). This scale is publicly accessible for non-commercial academic research; the original authors have granted open-use permission for educational cross-sectional surveys without additional formal written authorization. After data cleaning, 155 valid questionnaires were included (effective response rate = 86.9%). Nonparametric tests and ordinal logistic regression were performed via IBM SPSS 27.0 for statistical analysis.
RESULTS
Participants demonstrated a moderate level of overall artificial intelligence literacy (Median = 60.00, IQR = 21.00, SD = 13.18). The Application dimension scored highest. Gender and interest in AI were independent influencing factors. Strong positive correlations were identified among all four dimensions.
CONCLUSIONS
Study findings suggest that nursing education should integrate AI content and targeted training to strengthen students' AI awareness, critical evaluation, and ethical awareness, while providing tailored support for female students and boosting AI interest.
This study aimed to translate and culturally adapt the Medical Artificial Intelligence Readiness Scale for Medical Students into Korean and to examine its validity and reliability among nursing students. The scale designed in this methodological and cross-sectional study was translated and adapted using standard procedures and validated with 317 nursing students in South Korea. Experts reviewed the content validity. Structural validity was examined through exploratory and confirmatory factor analyses. Construct validity was evaluated through hypothesis testing using an external criterion measure. Reliability was evaluated using Cronbach's alpha and the split-half method. The final Korean version of the Medical Artificial Intelligence Readiness Scale (K-MAIRS) comprised 15 items across four factors-cognition, ability, vision, and ethics-explaining 66.88% of the variance. The K-MAIRS can be a valid and reliable instrument for assessing artificial intelligence readiness among Korean nursing students. This instrument supports global initiatives to incorporate artificial intelligence competencies into nursing education using culturally tailored assessment tools. It also supports educators and institutions in designing targeted strategies for artificial intelligence education within nursing curricula that enhance nurses' preparedness in artificial intelligence-driven healthcare environments.
Minjae Lee, Nayeon Yi, Seunghyeon Lee et al.· Nursing and Health Sciences· 0 citations