Aug 2026· International Journal of Advanced Multidisciplinary Research and Studies· 0 citations
TL;DR
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.
Abstract
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.
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.
Enhancing AI literacy may alleviate psychological anxiety, with device accessibility and usage patterns also playing critical roles, and administrators should strengthen institutional support mechanisms alongside providing facility resources and conventional education.
Yi Dai, Xi-Li Zhao, Xiaochong Pan et al.· Frontiers in Public Health· 0 citations
Background The growing integration of artificial intelligence (AI) into healthcare and medical education has created an urgent need to evaluate how prepared undergraduate students are to engage with these technologies. Medical graduates will increasingly encounter AI-driven tools across clinical and educational settings, yet systematic assessment of their readiness and perceptions remains limited, particularly in India. This study aimed to assess AI readiness and perceptions among undergraduate medical students and to examine how readiness varied in relation to sociodemographic characteristics, prior AI training, and patterns of AI tool utilization. Methods This cross-sectional study enrolled 310 undergraduate Bachelor of Medicine, Bachelor of Surgery (MBBS) students at a tertiary care teaching institution in Surendranagar, Gujarat, between September and November 2025. AI readiness and perception were assessed using the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) and a separately developed, validated 10-item questionnaire, respectively. Descriptive statistics were summarized as means, standard deviations, frequencies, and percentages. Participants were categorized as having poor (≤66), average (67-78), or good (≥79) readiness using cut-offs derived from the 33.33rd and 66.67th percentiles of the observed MAIRS-MS score distribution. The same percentile-based approach was applied to each MAIRS-MS domain score. Pearson's chi-square test or Fisher's exact test, as appropriate, was used to examine associations between categorical variables, while Spearman's rank correlation coefficient was used to assess relationships between readiness scores and selected variables. A two-sided p-value of less than 0.05 was considered statistically significant. Results The mean age of the study participants was 20.03 ± 1.65 years, and 182 (58.71%) were female subjects. Using the 33.33rd and 66.67th percentiles of the observed MAIRS-MS score distribution, 169 of 238 participants (71.0%) were classified as having average AI readiness. Previous exposure to AI training (χ² =6.33, p=0.042) was significantly associated with the ethics domain of AI readiness. Total AI readiness showed extremely weak positive correlations with age (r=0.087, p=0.181) and academic year of study (r=0.057, p=0.381), with neither relationship reaching statistical significance. Among all 310 participants, more than half of the students perceived AI as useful for several educational purposes, including teaching (n=165, 53.23%), assignment preparation (n=162, 52.26%), self-learning (n=166, 53.55%), understanding complex concepts (n=170, 54.84%), and clinical case scenarios (n=161, 51.93%). Substantial proportions also expressed concerns regarding misleading information (n=151, 48.70%), potential effects on clinical skills and critical thinking (n=151, 48.71%), and data privacy (n=133, 42.90%). Conclusion Overall, undergraduate medical students demonstrated an average level of AI readiness and generally mixed-to-positive perceptions toward artificial intelligence, with a considerable proportion of students remaining neutral across several items. Previous AI training or exposure was significantly associated with the ethics domain of AI readiness. An extremely weak positive correlation was observed between the total readiness score and both age and academic year of study.
Kumarjiv K. Shreshthi, Jay H. Nimavat, Milind Makwana et al.· Cureus· 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
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
Background: Artificial intelligence (AI) is increasingly being applied in medicine for diagnosis, treatment, and decision-making. While enthusiasm for AI training among healthcare workers has been reported globally, little is known about awareness and attitudes in Afghanistan, where limited access to advanced diagnostic tools makes AI particularly valuable. Objective: This study aimed to assess the knowledge, attitude, and practices of AI among healthcare workers in Kabul City. Methods: A cross-sectional survey was conducted from January to February 2025 among 256 healthcare staff, including physicians, nurses, technicians, and administrative personnel. Data were collected using a structured questionnaire distributed online and in paper format. Responses were recorded on a three-point Likert scale. Statistical analysis was performed using SPSS version 26, employing descriptive statistics, chi-square tests, regression, and correlation analyses. Reliability was assessed using Cronbach’s alpha. Results: Of the participants, 71.1% were male (28.9% were female), and 43.8% were aged 21–29 years. Knowledge of AI was limited: only 1.6% demonstrated good knowledge, 44.7% poor knowledge, and 53.9% had insufficient knowledge. Attitudes were more favorable, with 47.3% expressing positive views, 37.5% somewhat agreeing, and 15.2% expressing negative views. Regression analysis revealed that age was significantly associated with knowledge scores, and knowledge strongly predicted positive attitudes. Reliability analysis confirmed acceptable internal consistency across domains (α ≥ 0.72). Conclusion: Knowledge of AI among medical staff in Kabul is limited, but attitudes are generally favorable. Structured training programs, conferences, and AI-enabled systems are needed to strengthen Afghanistan’s healthcare sector.
Ahmad Mustafa Rahimi, Abdul Bashir Bashari, M. Mohammadi et al.· Annals of Medicine and Surge...· 0 citations
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