Nurses’ knowledge, attitudes, and perceived challenges toward artificial intelligence applications in patient care: a descriptive-analytical cross-sectional study
The weak negative correlation between knowledge and attitudes suggests that greater awareness of AI may be accompanied by increased concerns regarding its use, and further educational initiatives are needed to enhance nurses’ preparedness for AI integration in clinical practice.
Abstract
Artificial intelligence (AI) is increasingly being integrated into healthcare systems; however, nurses’ knowledge, attitudes, and perceived challenges play a crucial role in its adoption in patient care. This study aimed to assess nurses’ knowledge, attitudes, and perceived challenges toward AI, examine the relationships among these variables, and explore their associations with demographic characteristics and prior AI training. A descriptive analytical cross-sectional study was conducted among 107 nurses working in intensive care, medical, and surgical units at Zagazig University Hospital. A purposive sampling technique was used. Data were collected over two months using structured instruments during morning shifts. Most participants were aged 25–34 years (55.1%), male (65.4%; reflecting the accessible sample composition), and held bachelor’s degrees (68.2%), with nearly half (49.5%) having 5–10 years of clinical experience. Overall, 68.2% of nurses achieved satisfactory knowledge scores, whereas 88.8% demonstrated positive attitudes toward AI applications. Perceived challenges were mainly related to technical and ethical concerns, particularly the need for continuous system updates, cybersecurity risks, and implementation costs. A statistically significant weak negative correlation was found between nurses’ knowledge and attitudes toward AI (r = -0.195, p = 0.044). No significant correlations were observed between knowledge and perceived challenges (r = -0.162, p = 0.095) or between attitudes and perceived challenges (r = 0.142, p = 0.145). Previous AI-related training was significantly associated with more positive attitudes toward AI (p = 0.019), whereas no significant associations were found with knowledge or perceived challenges. Educational level, workplace, and years of experience were not significantly associated with nurses’ knowledge, attitudes, or perceived challenges. Nurses demonstrated a satisfactory knowledge and generally positive attitudes toward AI applications in patient care, while perceiving moderate implementation challenges. Although previous AI-related training was associated with more positive attitudes, no significant associations were found with knowledge or perceived challenges. The weak negative correlation between knowledge and attitudes suggests that greater awareness of AI may be accompanied by increased concerns regarding its use. Further educational initiatives are needed to enhance nurses’ preparedness for AI integration in clinical practice.
BACKGROUND
Artificial intelligence (AI) is rapidly transforming healthcare. Current and future healthcare workforce, including nursing students, require sufficient understanding of responsible AI use. However, data about knowledge, attitudes, practices and ethics regarding AI use in this population is scarce. This national study comprehensively assessed AI-related knowledge, attitudes, practices and ethics among nursing students in China, including the socio-demographic and educational factors associated with these professional domains.
METHODS
A cross-sectional survey was conducted between June and August 2025 across 32 provinces, autonomous regions and municipalities in China. A total of 10,268 nursing undergraduates and vocational college students completed a newly developed 22-item Knowledge, Attitudes, Practices and Ethics questionnaire covering four domains (Cronbach's α = 0.79-0.96). Univariable analyses and multiple linear regression were used to examine socio-demographic factors of AI-related knowledge, attitudes, practices and ethics.
RESULTS
Nursing students reported moderate level of AI knowledge (18.17 ± 4.90, total score 24), moderately positive attitudes (11.96 ± 2.49, total score 20), relatively good ethical awareness (11.60 ± 3.27, total score 16), but only limited engagement in practice (8.29 ± 5.04, total score 28). Of the participants, only 6.8%-9.6% reported "often" and "always" using AI tools for academic and personal tasks. In multivariable models, male gender, urban residence, higher economic status, and intention to pursue nursing as a career were independently associated with higher scores across the knowledge, attitude, and ethics domains (all P < 0.05). Compared with vocational college students, undergraduates had significantly higher scores on attitude, practice and ethics domains (all P < 0.05). Age was positively associated with all domains, although only the 19-20-year group had significantly higher scores in the practice domain (P < 0.001).
CONCLUSION
The findings highlight the need for a supportive educational environment with guidance to enable nursing students to use artificial intelligence appropriately and responsibly when needed, particularly among vocational college students and those from socioeconomically disadvantaged backgrounds. Integrating AI competencies into nursing education and linking them to future career development and clinical practice may help bridge the gap between positive attitudes and limited practical use.
Hui-Ying Fan, Qing Zhou, Lili Deng et al.· BMC Nursing· 0 citations
Introduction: Global healthcare digitalisation requires nurses to embrace nursing informatics (NI). This study assessed NI knowledge and perceptions among staff nurses at Rumailah Hospital, Doha, Qatar.
Methods: A descriptive cross‑sectional study was conducted using convenience sampling. A modified Nurses’ Attitudes towards Computerisation (NATC) questionnaire captured demographics, NI knowledge, and perceptions. Chi‑square tests (with Cramér’s V effect sizes) examined associations (alpha = 0.05). Data were collected in 2016‑2017.
Results: Of 572 nurses (82.8% female, 75.4% aged 20‑30 years), 50% held a basic nursing certificate. Positive perceptions were high: 96.5% agreed NI improved care decisions; 100% agreed NI enhanced research implementation and consistent data recording. However, 38.3% noted NI was not used for student training. Significant associations with perception (p<0.001) were found for gender (χ²=28.73, V=0.23), academic qualification (χ²=102.31, V=0.30), years of experience (χ²=177.17, V=0.33), and ease of use understanding (χ²=30.18, V=0.23).
Conclusion: Nurses demonstrated strong positive NI knowledge and perceptions. Despite the age of the dataset, findings highlight enduring needs: targeted education for less‑qualified and more experienced nurses, and integration of NI into student training. These insights remain relevant for digital health transformation in Global South contexts.
Adeola Oyinloye, O. Olu-Abiodun, A. Adepoju· Global South Health Horizons· 0 citations
BACKGROUND
The growing integration of artificial intelligence (AI) into healthcare is transforming nursing practice and clinical decision-making. However, evidence regarding the relationship between nursing students' attitudes toward AI and ethical sensitivity remains limited.
AIM
This study examined the relationship between nursing students' attitudes toward AI and their ethical sensitivity in patient care and identified factors associated with these variables.
METHODS
This descriptive, cross-sectional correlational study was conducted with 278 nursing students at a public university in Türkiye. Data were collected through an online survey using the Ethical Sensitivity Questionnaire in Nurses (ESQN), the Artificial Intelligence Attitude Scale-Short Form (AIAS-4), and a sociodemographic form. Data were analyzed using non-parametric tests, Spearman's correlation, and multiple linear regression analyses.
RESULTS
The mean ESQ-N score was 39.82 ± 5.41, and the mean AIAS-4 score was 27.16 ± 6.82. Ethical sensitivity scores differed significantly according to AI ethics education (p = 0.008). AI attitudes differed significantly by academic year (χ2(3) = 8.04, p = 0.018), with fourth-year students reporting higher scores than first-year students (p_adj < 0.05). No significant correlation was found between ESQ-N and AIAS-4 scores (p > 0.05). Regression analyses showed that ethical sensitivity was predicted by academic year and AI ethics education (Adj. R2 = 0.13), whereas AI attitudes were predicted only by academic year (Adj. R2 = 0.15).
CONCLUSIONS
Attitudes toward AI and ethical sensitivity appear to represent distinct dimensions of professional competence. Integrating AI-related ethical content into nursing curricula may support the development of both digital and ethical competencies in future nurses.
M. G. Sezgin, H. Bektaş· Applied Nursing Research· 0 citations
Artificial intelligence (AI) is being gradually integrated into clinical nursing practice, where it plays an important role in improving nursing quality, reducing nurses’ workload, and promoting the intelligent transformation of nursing. Nurses’ attitudes toward AI applications in nursing directly affect the promotion and implementation of this technology. Understanding these attitudes and their heterogeneity is crucial for the successful implementation of AI technology. This study aimed to identify potential types of nurses’ attitudes toward the use of AI in nursing and to explore the factors associated with profile membership with type affiliation. A cross-sectional survey was conducted among 206 clinical nurses in Anhui Province, China, in July 2025. Data were collected using a general information questionnaire, the Attitudes Toward the Application of AI Technology in Nursing Scale, and the Multidimensional Nursing Generations Questionnaire. Latent profile analysis(LPA) was used to identify distinct attitude profiles. Univariate analyses and multinomial logistic regression were performed to explore associated factors. Three profiles were identified: positive acceptance (16.02%), ambivalent balance (9.71%), and cautious skepticism (74.27%). Multinomial logistic regression showed that educational level, computer proficiency, English proficiency, and generational characteristics were significantly associated with profile membership (all P < 0.05). Nurses showed moderate attitudes toward AI in nursing with substantial heterogeneity. Profile-tailored strategies may help nursing managers facilitate the effective and sustainable implementation of AI technologies in clinical practice.
BackgroundArtificial intelligence (AI) continues to emerge into nursing practice with much of the AI research being conducted in the acute care sector. Community health nurses (CHNs) have distinct skills and knowledge focusing on helping clients to live well in the community. Community practice is an essential part of healthcare yet often overlooked, creating a knowledge gap. This research aims to understand CHNs' knowledge and perceptions of AI to inform future practice.MethodAn explanatory sequential mixed methods design was conducted, a cross-sectional survey followed by focus groups to combine both sets of results for a comprehensive perspective.ResultsThere were 228 survey respondents and 27 participants forming 8 focus groups. The survey revealed professional concerns: AI giving a wrong recommendation (77.7%) or if a correct recommendation was dismissed (73.8%) with focus groups explaining they knew their responsibility in decision-making, their concerns focused on accepting AI recommendations blindly or using recommendations to assist with decision-making. Overall CHNs felt AI applications had usefulness (68.8%-88%), the focus groups further explained clinical relevance, user friendly and time efficiency were factors that would determine usefulness.ConclusionsDedicated time for AI education for CHNs is needed to address how recommendations are generated and the significance to give to AI recommendations. Clear policies and guidelines need to be established to inform CHNs use of AI. Success of AI in CHNs practice is dependent on applications that do not add time to their busy schedules.
M. H. Betkus, D. Banner, L. Currie et al.· The Canadian journal of nurs...· 0 citations
Background: Artificial intelligence (AI) is revolutionising healthcare, but its integration into Indian undergraduate medical curricula remains nascent. Understanding student perspectives is critical for effective pedagogical reform.
Objectives: This study aimed to assess AI awareness and knowledge, evaluate perceptions toward its integration in medical education, and identify perceived barriers to adoption among undergraduate medical students.
Methods: A cross-sectional, questionnaire-based study was conducted (January-March 2026) among 324 MBBS students (2nd to final year) at an Indian medical college. A structured, pre-validated questionnaire was used to evaluate awareness, usage patterns, perceptions, and barriers (5-point Likert scale). Descriptive statistics and chi-square tests were used for the statistical analysis, and the level of significance was set at p < 0.05.
Results: Participants (mean age 22.71 ± 1.59 years; 55.6% male) reported 77.5% prior AI exposure, though only 12.04% claimed high familiarity. AI chatbots were the primary tools used (92.3%). Students demonstrated favourable perceptions regarding AI’s utility in clinical decision-making and learning while largely rejecting the notion that it would replace educators. Key barriers included data privacy concerns and output reliability. Prior AI exposure significantly correlated with positive perceptions (x² = 11.948, p = 0.018). Most students advocated for formal AI training and ethical guidelines within the medical curriculum.
Conclusion: Indian medical students show positive attitudes toward AI but lack deep technical familiarity. To address ethical and infrastructural barriers, structured curricular integration focusing on foundational AI competencies is essential for preparing future physicians.
Key words: Artificial intelligence, medical education, students, attitude, technology adoption
Anjani Kumar Srivastava, Anjali Singh, Aparnesh Pandey et al.· International Journal of Hea...· 0 citations