Background/Objectives: Artificial intelligence (AI) is transforming nursing, with generative AI (GenAI) tools such as ChatGPT offering opportunities to enhance education through personalized learning pathways. This study aimed to explore nursing students’ use of generative artificial intelligence (GenAI) and their perceptions of its use in nursing education, including its advantages, disadvantages, and perceived support needs. Methods: This study employed an online survey. The participants were 99 undergraduate nursing students in New York City. Data was collected online through self-report measures using semi-structured, open-ended questions. The data was analyzed using content analysis. Results: Most participants (92%) used GenAI tools to access accurate information, clarify nursing concepts, and support clinical tasks such as diagnoses and health assessments, as well as schoolwork, grammar checks, and health promotion. They valued GenAI as a quick, accessible resource that simplified complex information and supported learning through definitions, practice questions, and writing improvements. However, the participants noted drawbacks, such as subscription costs, over-reliance, information overload, and accuracy issues, leading to trust concerns. The participants suggested financial support, early guidance, and instructional modules to better integrate AI into nursing education. Conclusions: The results indicate that GenAI positively impacts nursing education and highlight the need for guidelines on critical evaluation. To integrate GenAI effectively, educators should consider introductory sessions, support programs, and a GenAI-friendly environment, promoting responsible AI use and preparing students for its application in nursing education.

Introduction: The shift to online learning owing to the coronavirus disease 2019 pandemic is posing an additional challenge to academic success, particularly for students who speak English as a Second Language (ESL). This study aimed to examine the factors that contribute to academic self-efficacy among ESL nursing students. Method: This was a cross-sectional study using path analysis with 113 undergraduate ESL nursing students in New York City. Data were collected online using self-report measures of the study variables: academic self-efficacy, perfectionistic concerns, acculturative stress, and e-learning stress. A hypothetical path model was tested using AMOS 26.0. Results: Perfectionistic concerns and acculturative stress directly affected academic self-efficacy. Furthermore, perfectionistic concerns and e-learning stress indirectly affected academic self-efficacy via the acculturative stress path. Discussion: Results indicate the importance of developing a multifaceted intervention that considers diverse cultural and psychological factors to help ESL nursing students enhance their academic self-efficacy in e-learning environments.

Low health literacy is closely related to a higher rate of hospitalization and of emergency services usage, leading to billions of dollars in avoidable health care costs and contributing to individuals’ poor physical and mental health. While Korean Americans (KAs) have a high prevalence of general health and mental health problems, relatively little is known about their health and mental health literacy, specifically their depression literacy. This study aims to evaluate KAs’ health literacy and depression literacy, as related to sociodemographic characteristics. An exploratory study was conducted with 681 community-residing adult KAs using the Center for Epidemiologic Studies Depression (CES-D) Scale, the Depression Literacy Questionnaire (D-Lit), and the One-Question Health Literacy Scale. Data analyses were performed using one-way analysis of variance and ordinal logistic regression of health literacy, a multiple linear regression model of depression literacy. Findings indicate a positive correlation between depression literacy and health literacy. Health literacy was also significantly related to religion, English language proficiency, income, education, and perceived physical health. Additionally, depression literacy was significantly related to age, Korean language preference over English, and education. Health literacy and depression literacy education are warranted to address low health literacy and depression literacy among KAs to reduce their health and mental health disparities.

Despite strenuous efforts to increase vaccination rates against human papillomavirus (HPV), the rate of vaccination remains low in the U.S. The COVID-19 pandemic poses an additional challenge to HPV vaccination. This study aimed to explore the factors that contribute to HPV vaccination among Asian immigrant college students, based on the Theory of Planned Behavior (TPB), with HPV vaccine literacy and HPV vaccine hesitancy as additional constructs. This cross-sectional survey used convenience sampling, with133 Asian immigrant college students from New York City as study participants. Data were collected online using self-reported measures of the following study variables: HPV vaccine literacy, attitudes, subjective norms, self-efficacy, vaccine hesitancy, vaccination intention, and HPV vaccination. The data were analyzed using path analysis. Vaccine literacy and vaccination intention directly affected HPV vaccination. Vaccine subjective norms and literacy directly affected vaccination intention. Moreover, vaccine attitudes and self-efficacy directly and negatively affected vaccine hesitancy. These results indicate the importance of developing a multifaceted intervention that considers vaccine literacy and subjective norms to promote HPV vaccination of Asian immigrant college students. Interventions to decrease vaccine hesitancy during the COVID-19 pandemic should focus on improving positive attitudes and self-efficacy toward HPV vaccination.

Introduction: Persistent exposure to the COVID-19 pandemic has subjected frontline nurses to heightened levels of collective traumatic syndromes, including stress, burnout, and emotional distress. Resilience and self-compassion may serve as protective factors in mitigating stress and burnout stemming from the pandemic. Moreover, these factors may contribute to posttraumatic growth (PTG), which has been recognized as a potential outcome for nurses caring for patients. This study aimed to investigate the factors contributing to PTG among registered nurses, with perceived stress, burnout, resilience, and self-compassion. Methods: This cross-sectional observational survey used convenience sampling with 228 registered nurses from New York. Data were collected online using self-reported measures of the following study variables: perceived stress, burnout (sub-dimensions: emotional exhaustion, depersonalization, and personal accomplishment), resilience, self-compassion, and PTG. The data were analyzed using path analysis. Results: Resilience and self-compassion directly and positively affected PTG, while perceived stress and burnout indirectly affected PTG via resilience and self-compassion. Among three sub-dimensions of burnout, PTG was indirectly affected by emotional exhaustion via resilience, depersonalization via self-compassion, and personal accomplishment via resilience and self-compassion. Discussion: This study identifies resilience and self-compassion as key psychological resources that directly enhance nurses’ PTG, while perceived stress and burnout undermine it indirectly through these traits. Each burnout dimension had distinct mediating pathways: emotional exhaustion through resilience, depersonalization through self-compassion, and low personal accomplishment through both. These findings emphasize the need for targeted interventions that strengthen resilience and self-compassion while addressing specific burnout symptoms. Limitations include the cross-sectional, self-reported nature of the data and limited generalizability. Longitudinal studies are needed to confirm these pathways and inform intervention design. Conclusion: The findings of this study suggest that resilience and self-compassion are essential in promoting PTG among nurses, helping them better cope with COVID-related stress and burnout. This study investigates the dynamic role of resilience alongside self-compassion, demonstrating how these factors interact with three distinct dimensions of burnout to influence PTG outcomes. Therefore, the study emphasizes the need for multifaceted interventions, such as mindfulness programs, resilience training, and self-compassion initiatives, to enhance nurses' resilience and self-compassion. These interventions effectively reduce stress and burnout while promoting PTG both during and beyond the COVID-19 pandemic.

Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into nursing education, yet structured AI literacy training and ethical guidance remain limited. Consequently, nursing students often rely on informal learning, resulting in variability in AI readiness, confidence, and responsible use. Aims: This study was conducted to examine (1) whether AI literacy was positively associated with AI self-efficacy and AI attitudes and (2) whether AI self-efficacy mediated the relationship between AI literacy and AI attitudes. Methods: A cross-sectional survey using convenience sampling was conducted with 100 prelicensure nursing students in New York City. Data were collected using the AI Literacy Scale (AILS), AI Self-Efficacy Scale (AISES), and Generative AI Attitude Scale (GAIAS). Correlation and path analyses were performed using SPSS and Amos 30.0. Results: The participants had a mean age of 30.25 years, and 71% were women. AI literacy and AI self-efficacy were both positively associated with AI attitudes (all p < 0.001). Path analysis showed that AI literacy significantly predicted AI self-efficacy (β = 0.39, p < 0.001) and AI attitudes (β = 0.28, p = 0.003). AI self-efficacy significantly predicted AI attitudes (β = 0.31, p = 0.001) and partially mediated the relationship between AI literacy and AI attitudes. Conclusions: AI self-efficacy partially mediated the relationship between AI literacy and AI attitudes. Nursing curricula may benefit from structured AI education that integrates guided GenAI practice, case-based learning, and faculty feedback. Such educational frameworks warrant further empirical investigation regarding their potential to foster AI literacy, AI self-efficacy, and positive attitudes toward responsible AI integration, particularly through longitudinal studies assessing subsequent behavioral outcomes.