Aug 2026· Journal of Education and Research in Nursing· 0 citations· 26 references
TL;DR
Optimists and Realists appear to actively integrate AI tools into clinical practice and examination preparation and generally perceive them as beneficial for learning outcomes, highlighting the importance of adopting differentiated pedagogical approaches rather than a one-size-fits-all curriculum.
Abstract
Background: Artificial intelligence (AI) is increasingly integrated into nursing education; however, its use in pediatric nursing courses remains underexplored.
Aim: This study aimed to examine the use of artificial intelligence tools among nursing students enrolled in a pediatric nursing course and to identify homogenous student profiles based on their usage characteristics and theoretical adoption patterns.
Methods: This cross-sectional study included 241 nursing students enrolled in a pediatric nursing course. Data were collected using the AI in Pediatric Nursing Course Questionnaire and the General Attitude Toward Artificial Intelligence Scale (GAAIS). K-means cluster analysis was conducted, guided by Rogers’ Diffusion of Innovations Theory, to classify students into distinct profiles based on their GAAIS subscale scores, which were considered to reflect their fundamental attitudinal orientations toward AI.
Results: Cluster analysis identified three distinct student profiles: Optimists (n=95), Realists (n=102), and Traditionalists (n=44). Optimists demonstrated the highest levels of AI use, whereas Traditionalists demonstrated the lowest levels in both clinical practice (p=0.002) and preparation for clinical visits (p=0.039). Additionally, Optimists and Realists reported more positive perceptions of AI’s impact on examination success (p=0.002) and learning (p=0.001), whereas Traditionalists expressed significantly more negative attitudes toward AI overall (p<0.001) and reported the lowest levels of trust in the technology (p=0.040).
Conclusion: Optimists and Realists appear to actively integrate AI tools into clinical practice and examination preparation and generally perceive them as beneficial for learning outcomes. These findings highlight the importance of adopting differentiated pedagogical approaches rather than a one-size-fits-all curriculum.
In recent times, nursing students have been utilizing artificial intelligence (AI) technology, as they perceive it boosts learning outcomes and academic performance and transforms several facets of healthcare. This study aimed to explore nursing students' knowledge, attitudes and perceptions concerning the adoption of AI in their academic and clinical areas. It applied an exploratory study design to cover the study population of all undergraduate students, including interns from selected private nursing colleges in Tamil Nadu, India (N = 440). A self-designed online questionnaire was distributed via Google Forms to the target population and 317 responded. The results showed that 81.3% were familiar with the term "AI" (81.3%). 76.3% recognized that AI would revolutionize the nursing field. Most nursing students consented that AI should be included in undergraduate (67.2%) and postgraduate (71.3%) nursing curricula. 77.9% perceived that AI would be helpful for their future career. A significant variation was observed in nursing students' knowledge, attitude and perception scores across age categories, but not for gender and year of study. This study concluded that female nursing students, especially those aged 17-19, demonstrated strong knowledge, an optimistic attitude and had a better perception of AI. The findings suggest that nursing students in India possess adequate knowledge about AI, indicating a positive perception that AI plays a transformative function in nursing education and practice, with a need for more focused training and integration into the curriculum.
Arul Valan, Latha S Kannan, A. Subbarayalu et al.· International Research Journ...· 0 citations
Profile-tailored strategies may help nursing managers facilitate the effective and sustainable implementation of AI technologies in clinical practice and to explore the factors associated with profile membership with type affiliation.
BACKGROUND
Artificial intelligence (AI) is becoming an integral part of nursing education; however, the perspectives of graduate nursing students on its use remain underexplored.
AIM
This study aimed to examine graduate nursing students' views on AI in nursing education.
METHODS
A qualitative phenomenological design incorporating the photovoice method was adopted. Fifteen graduate nursing students from a state university were recruited between 01 March 2025 and 30 May 2025. Data were analyzed using thematic analysis in accordance with Braun and Clarke's approach.
RESULTS
Analysis yielded five main themes and 17 subthemes: (1) Areas of AI application in nursing education, (2) Perceived advantages of AI, (3) Perceived disadvantages of AI, (4) Recommendations for effective AI integration in nursing education, and (5) Future directions for AI in nursing education.
CONCLUSIONS
Participants viewed AI as a tool to enhance the quality and effectiveness of nursing education, while emphasizing the importance of ethical sensitivity and protection of professional identity. The use of photovoice method enriched and deepened these insights.
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 Artificial intelligence (AI) is quickly revolutionizing higher education; however, there is high heterogeneity regarding the adoption of AI amongst the students. In the context of nursing education, where successful learning, critical thinking skills, and professional accountability are crucial, it becomes essential to understand how the students interact with the AI applications. The current study was conducted to determine the AI-use profiles among nursing students and how they relate to academic outcomes and demographics. Methods We used a cross-sectional survey among nursing students. AI use for learning purposes was assessed using four binary indicators capturing use for understanding concepts, summarizing content, drafting assignments, and language support. Study engagement was measured using selected items from the Online Student Engagement framework and collapsed into three ordinal categories. We assessed academic performance using three self-reported items. We performed a latent class analysis to identify distinct student profiles based on AI use and engagement indicators. Multinomial logistic regression examined associations between demographic factors and class membership. Multiple linear regression assessed differences in academic performance across classes. Results The best model was a three-profile solution with a good classification accuracy. The profiles included strategic engagers, moderate users, and passive or low engagers. As expected, strategic engagers were characterized by high levels of engagement with all kinds of behaviors studied, whereas Passive used consistently reported low levels of engagement. Usage indicators of AI revealed little variation among classes. The performance scores of strategic engagers were significantly better compared with those of passive users (β = 2.39, 95% CI = 2.08–2.69). No significant association was found for demographic characteristics. Conclusions Nursing students can be categorized into distinct profiles based on their patterns of AI use and study engagement. Our findings showed that study engagement, but not AI use alone, was the key factor associated with academic performance. These findings highlight the importance of promoting effective learning strategies alongside AI integration. Educational interventions should focus on guiding students toward strategic and responsible use of AI to enhance learning outcomes.
Majed M. Aljabri, Bandar S. Alharbi, Endale Alemayehu Ali· Frontiers in Medicine· 0 citations
BACKGROUND
Artificial intelligence (AI) has emerged as one of the most rapidly evolving technologies in recent years and is increasingly being integrated into healthcare, education, and everyday life. Examining university students' attitudes toward this technology is important for understanding their future professional orientations and adaptation to technological change. This study aimed to identify medical students' attitudes toward artificial intelligence and the determinants of these attitudes, and to develop educational implications for medical training based on the findings.
METHODOLOGY
This descriptive cross-sectional study was conducted between January 22 and May 28, 2025, among 198 final-year medical students at Pamukkale University Faculty of Medicine in Denizli, Türkiye. Data were collected using a Descriptive Information Form, which assessed students' sociodemographic characteristics and their knowledge and experiences regarding AI, and the General Attitudes toward Artificial Intelligence Scale (GAAIS, Turkish version). Since the negative subscale is reverse-coded, higher scores in both indicate more positive attitudes toward AI. Data were analyzed using SPSS v25 with descriptive statistics, Mann-Whitney U, Kruskal-Wallis, and multiple linear regression analyses.
RESULTS
The mean age of participants was 24.47 ± 0.98 years; 56.1% were female. A total of 74.2% of the participants reported general knowledge about AI, and 76.3% reported experience using AI in daily life. A total of 63.1% viewed AI developments positively, 69.7% believed AI changes work and daily life, and 52.0% felt emotionally unaffected by it. The mean positive attitude score was 45.01 ± 9.17, and the negative attitude score was 26.41 ± 6.22. Multiple linear regression analysis showed that positive attitudes toward artificial intelligence were significantly associated with father's education level (university vs. primary school), having an interest in technology, perceptions regarding developments in artificial intelligence, and the belief that AI has an emotional impact (p < 0.05). For the negative attitude subscale, only perceptions regarding developments in artificial intelligence were found to be significantly associated (p < 0.05).
CONCLUSION
Medical students demonstrated generally positive attitudes toward AI. Higher paternal education, technological interest, perceiving AI as emotionally influential, and evaluating AI developments positively were predictors of favorable attitudes. In addition, more positive evaluations of AI-related developments were also associated with higher scores on the reverse-coded negative attitude subscale, indicating lower negative attitudes toward AI. These findings suggest that students' attitudes toward AI are shaped not only by technological interest but also by perceptual factors related to AI. Therefore, integrating clinically oriented AI content and awareness-building activities into medical education may support the development of more balanced and informed attitudes toward AI.
Batuhan Horasan, A. Ergin, Eda Şenarabacı· BMC Medical Education· 0 citations