Aycan Kucukkaya

Integrating artificial intelligence (AI) into healthcare is rapidly expanding, yet research on nursing students' AI literacy (AIL) remains limited. This study assessed AIL levels, identified factors associated with AIL, and explored students' perceptions of AI use. The mixed-methods study used a non-probability snowball sampling technique, involving 383 undergraduate nursing students in Türkiye, and utilized the Descriptive Characteristics Form and the Artificial Intelligence Literacy Scale (AILS). Quantitative analyses included t-tests, ANOVA, Pearson correlations, and multiple linear regression. Qualitative data underwent thematic analysis. The mean age of participants was 21.52 (SD ± 3.31; range, 18-40), and 83.3% of them were female. Participants' mean AILS score was 110.80 (SD ± 40.98; range = 31-216) with 41.22 ± 19.67 for technical understanding, 40.49 ± 15.57 for critical appraisal, and 29.09 ± 10.68 for practical application. Higher comfort with new technologies (β = 0.352; t = 7.06; p < 0.001) and having witnessed/experienced ethical concerns (β = 0.094; t = 1.98; p = 0.048) were significantly associated with higher AIL. Seven themes were identified: (1) Support and Convenience, (2) Time Management, (3) Learning and Academic Development, (4) Ethical and Security Concerns, (5) Misinformation and Trust, (6) Laziness and Dependency, and (7) Creativity and Thinking. Enhancing AIL skills among nursing students is essential for the ethical and rational use of AI. Educators should guide nursing students to develop strategies to improve their technical, appraisal, and practical AIL skills. The present study may contribute to identifying educational strategies to support nursing students' readiness for AI in nursing education. Not applicable.

Background: Adopting generative artificial intelligence (GenAI) in education rapidly transforms learning environments, yet nursing students’ acceptance and anxiety toward these technologies remain underexplored in Middle Eastern contexts. This study extends the Technology Acceptance Model (TAM) by incorporating constructs such as Facilitating Conditions (FC) and Social Influence (SI). It investigates the moderating role of Anxiety on Behavioral Intention to Use (BIU) generative AI tools. Methods: A cross-sectional study was conducted among 1,055 undergraduate nursing students across four Middle Eastern countries, including Egypt, Jordan, Saudi Arabia, and Yemen. Data were collected using a structured questionnaire comprising the Generative Artificial Intelligence Acceptance Scale and the Artificial Intelligence Anxiety Scale. Structural equation modeling was employed to evaluate relationships among Performance Expectancy (PE), Effort Expectancy (EE), FC, SI, and BIU, with Anxiety as a moderator. Descriptive statistics, confirmatory factor analysis, and path analysis were performed using SPSS and Python’s semopy library. Results: The model demonstrated strong explanatory power, with 75.09% of the variance in BIU explained by the TAM constructs and Anxiety. Path coefficients revealed significant positive relationships between PE (β = 0.477, p < 0.001), EE (β = 0.293, p < 0.001), FC (β = 0.189, p < 0.001), and SI (β = 0.308, p < 0.001) and BIU. Anxiety had the strongest moderating effect (β = 0.552, p < 0.001), indicating its critical role in shaping behavioral intentions. Gender, year of study, and access to technology emerged as significant demographic variables influencing acceptance and anxiety levels. Conclusions: This study emphasizes the importance of reducing anxiety and enhancing support systems to foster GenAI acceptance among nursing students. The findings provide actionable insights for designing culturally tailored educational interventions to promote the effective integration of AI in nursing education. Clinical trial number: Not applicable.

Artificial intelligence (AI)-driven chatbots hold promise for improving patient care and healthcare efficiency, but integrating equality, diversity, and inclusion (EDI) remains challenging. This discussion paper explores the potential for EDI-focused chatbots, emphasizing the need for ongoing assessment, diverse datasets, and collaboration among healthcare providers, technologists, and policymakers. While acknowledging current limitations such as algorithmic bias, the paper also emphasizes the potential of AI to support and extend human decision-making, particularly through real-time analytics and scalable patient support. Embedding EDI principles helps reduce bias, enhances fairness, and requires cross-disciplinary collaboration to ensure AI delivers equitable, inclusive healthcare for all.

ChatGPT gibi yapay zekâ (YZ) araçlarının hemşirelik eğitiminde kullanımı güncel ve önemli bir tartışma konusudur. ChatGPT, hemşirelik eğitimini dönüştürme potansiyeline sahip olmakla birlikte hızlı bilgi erişimine olanak tanıyan, yeni bilgileri öğrenme, soru sorma, hemşirelik öğrencilerinin kuramsal ve klinik becerilerini geliştirme ve etkileşimli öğrenme imkânı gibi çeşitli olanaklar sunan bir YZ aracıdır. Ancak, ChatGPT bu yararlarının yanı sıra akademik dürüstlük ve intihal ile ilgili endişeleri de beraberinde getirmekte, öğrencilerin haksız kazanç elde etmelerine ve mezuniyette hedeflenen önemli eğitim çıktılarından biri olan eleştirel düşünme ve karar verme becerilerini olumsuz yönde etkileyebilmektedir. Bu nedenle, ChatGPT gibi YZ araçlarının hemşirelik müfredatına entegre edilebilmesi için, etik ve sorumlu kullanımı amacıyla rehberler, politikalar geliştirilmesi, hemşire eğitimcilerin ve öğrencilerinin YZ okur yazarlılıklarını artırılması gerekmektedir. Bu derlemede, ChatGPT’nin potansiyeli, hemşirelik eğitimine katkıları, sınırlılıkları ve zorlukları tartışılmaktadır.

Objectives: Transplantation is a form of treatment that requires long-term pharmacotherapy. After transplantation, patients may have difficulty adapting to medication use for various reasons, and this may result in rejection. The aim of this study is to determine participants’ medication compliance and the factors affecting it. Methods: The research was conducted with a sequential explanatory mixed method. In the study, quantitative data were collected using the Turkish Immunosuppressive Medication Adherence Scale, and qualitative data were collected using the In-Depth Individual Interview Guide. Quantitative data were analyzed using statistical methods, and qualitative data were examined according to Braun and Clarke's thematic analysis framework. Results: In this study, 62.3% of the participants were male, 37.0% were 50 years old and over, 71.3% lived with their spouse, 54.0% had primary and secondary school education, and 42.0% could not work due to their current health condition. From a clinical perspective, it was determined that 78% of the transplants were kidney transplants, and 41.3% were more than 4 years after transplantation. 74.3% of the transplants were from living donors. The mean score of the immunosuppressive medication compliance scale was determined to be 40.91±4.09. In the qualitative data analysis of the study, factors affecting medication adherence were examined and the themes of "individual factors", "complexity of the regimen" and "social support resources" were obtained. The sub-themes of the individual factors theme are reluctance, hopelessness and addiction; Sub-themes of the complexity of the regimen theme are drug side effects and polypharmacy; The sub-themes of the social support resources theme are loneliness and family pressure. Conclusions: The factors influencing medication adherence among organ transplant recipients have been investigated, revealing that adherence levels vary significantly depending on various factors. These findings underscore the importance of tailored care strategies and individualized support approaches.

Introduction: Investigating water consumption behaviors and perceptions of water sustainability among nursing students is crucial for effective resource management. This study employs machine learning (ML) techniques to analyze these factors in detail.Methods: This descriptive cross‐sectional study involved 182 senior nursing students from an accredited faculty in Istanbul, Turkey, during the 2023–2024 academic year. Data were collected through an online survey, including an information form, a visual analog scale toward foresight about water scarcity (VAS), and a water consumption behavior scale. Advanced ML techniques were utilized to identify intricate patterns and correlations in the students’ behaviors and perceptions.Results: The survey revealed that nursing students primarily rely on packaged water and perceive an imminent threat of water scarcity, though they exhibit limited awareness of their personal water footprint. High VAS scores indicated a strong awareness of global water crises but skepticism about local water scarcity. The ML model identified “Domestic Water Use Efficiency” as the primary factor influencing attitudes toward water sustainability, with “Water Awareness” and “Sustainable Water Ethics” also playing significant roles.Conclusions: The study highlights the need to integrate sustainable water management education into nursing curricula and demonstrates nursing students’ awareness and preparedness for sustainable practices.