Aug 2026· International Journal of Nursing Studies· Vol 183, pp.
105659
· 0 citations· 61 references
Medicine
TL;DR
Multiple factors shape nursing students' diverse patterns of interaction with large language models, and future interventions should be tailored to these specific personas to mitigate the risks of cognitive outsourcing and foster the responsible integration of large language models into nursing education.
Abstract
Background
With the integration of large language models into nursing education, nursing students' patterns of use have attracted growing attention and concern. While existing research focuses on determinants such as attitudes and intentions, it lacks insight into their actual patterns of use.
Objective
To identify the tailored behavioral personas of nursing students regarding large language models and explore the characteristics of interaction patterns within different personas, thereby clarifying the potential risks associated with these patterns of use.
Methods
From October to December 2025, a descriptive qualitative study was conducted involving 22 nursing students in China via semi-structured interviews. Purposive sampling with a maximum variation strategy was employed to select nursing students. Data were analyzed using content analysis. Through the extraction of interaction tags and behavioral dimensions, user personas were constructed to characterize students' patterns of use.
Results
Five key dimensions of students' interactions with large language models were extracted: cognitive relationships, interaction strategies, verification strategies, psychological experiences, and risk perceptions. Based on these dimensions, four user personas were identified: the efficiency-quality trade-off persona, the capability-compensating persona, the prudent-assistance persona, and the cognitive outsourcing persona.
Conclusion
Multiple factors shape nursing students' diverse patterns of interaction with large language models. Future interventions should be tailored to these specific personas, combining critical thinking training with technical and ethical support to mitigate the risks of cognitive outsourcing and foster the responsible integration of large language models into nursing education.
Objective To explore the intention to use large language models (LLMs) among clinical nurses with prior familiarity with or experience using LLMs, and to examine how relevant facilitators, constraints, perceived risks, and professional boundary considerations were reflected in such intention. Methods Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this descriptive qualitative study employed semistructured interviews with 17 clinical nurses from different hospital levels, specialties, and roles. Data were analyzed using directed content analysis. Results Six themes were identified: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, and professional boundaries and identity. Participants expressed a positive but conditional intention to use LLMs. Performance expectancy, effort expectancy, social influence, and facilitating conditions were reflected in participants’ accounts as perceived enabling conditions, whereas perceived risk and professional boundaries and identity were described as important sources of caution. Participants acknowledged the potential supportive value of LLMs in standardized and relatively low‐risk tasks, such as documentation, knowledge support, and teaching‐related work, but emphasized that LLMs should not replace nurses’ clinical judgment, accountability, or humanistic communication. Conclusion Among clinical nurses with prior familiarity with or experience using LLMs, intention to use these tools was reflected in participants’ accounts of perceived value, usability, organizational conditions, risk appraisal, and professional boundaries. Conditional intention should be understood as an interpretive finding rather than as a validated new construct or a formal extension of UTAUT. These findings provide context‐specific insights for cautious pilot testing, governance, and implementation of LLM‐supported nursing applications. Implications for Nursing Management Nurse managers may consider cautious pilot testing of LLM‐supported nursing applications in low‐risk and standardized tasks. Priorities include defining appropriate‐use boundaries, data security, and accountability requirements, ensuring human review, providing tiered training, and evaluating usability, workflow fit, safety concerns, and nurses’ feedback before wider implementation.
Xu Li, Xu Hu, Huiting Xu et al.· Journal of Nursing Managemen...· 0 citations
To explore how nursing graduate students in China experience and navigate generative artificial intelligence use in their research practice, including the conditions under which such use becomes dependency, a differentiated, stage-sensitive artificial intelligence governance frameworks tailored to nursing graduate education is advocated.
Furong Chen, Jingjing Cai, Shaoxue Li et al.· International Journal of Nur...· 0 citations
The nursing profession requires practitioners to possess the ability to engage in lifelong learning. As the core of the future nursing workforce, the self-directed learning abilities of nursing students born in the 2000s are of critical importance. This study aims to identify the characteristics of self-directed learning abilities and their influencing factors among different categories of nursing students through latent profile analysis, thereby providing a basis for developing targeted intervention strategies.
In April 2026, a cross-sectional survey was conducted using a structured questionnaire among nursing students at two medical universities in Liaoning and Shandong provinces. Latent profile analysis was used to identify latent categories of self-directed learning ability among nursing students born in the 2000s; independent samples t-tests, one-way ANOVA, or corresponding nonparametric tests (Mann-Whitney U test, Kruskal-Wallis test) were employed for intergroup comparisons; variables found to be statistically significant in the univariate analysis were included in an ordered logistic regression model to analyze the factors influencing the categories.
The self-directed learning abilities of nursing students born in the 2000s can be classified into three latent categories: the “Comprehensive Weakness Group” (18%), the “Balanced Development Group” (48%), and the “Comprehensive Leadership Group” (34%). Ordered logistic regression analysis revealed that educational background, whether serving as a student leader or class committee member, future plans to pursue a career in nursing, prior training in self-directed learning, academic burnout, and learning motivation were all significantly associated with the category of self-directed learning ability (all
P
< 0.05).
Differentiated educational interventions should be implemented for nursing students with different characteristics. For the “Comprehensive Weakness Group,” psychological support and training in structured self-directed learning skills should be strengthened; the “Balanced Development Group” should be provided with appropriate guidance to facilitate their transition to the “Comprehensive Leadership Group,” and the exemplary role of the “Comprehensive Leadership Group” should be fully leveraged to enhance the self-directed learning abilities of all post-2000s nursing students.
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Zuyao Xiao, Rui Cui, Jiaxin Liu et al.· BMC Nursing· 0 citations
This study examined multi-level factors influencing GenAI-supported experiential learning behavior in nursing education to highlight the importance of building AI confidence, fostering peer collaboration, and strengthening institutional readiness for the effective integration of GenAI into nursing education.
Nualyai Pitsachart, Vitsanu Nittayathammakul, Tepin Craivanich et al.· Journal of Education and Tra...· 0 citations
BACKGROUND
By 2024, 23% of China's population was aged 60 and above, with more than 2.3 million older adults residing in nursing homes. Understanding how service users evaluate nursing homes and the factors influencing their satisfaction can provide actionable recommendations for policymakers and nursing home managers. This helps better align care provision with the needs and expectations of older adults and their families.
METHODS
Utilizing data from Dianping.com, this study employed BERTopic for overall topic modeling, alongside LDA to compare variations in user concerns across different identities and different city tiers. Furthermore, multivariate regression and random forest models were integrated to identify the factors driving user satisfaction.
RESULTS
We analyzed 14,525 nursing home user reviews and identified four primary themes and twenty secondary themes of user concerns. Regression analysis and random forest modeling revealed that "Staff Attitude" and "Atmosphere" were the factors most strongly associated with user satisfaction. Thematic attention varies by user identity: prospective users prioritize physical infrastructure, relatives focus on clinical care, and actual residents emphasize daily activities. Furthermore, users in second- and third-tier cities express higher satisfaction than their counterparts in first-tier cities.
CONCLUSIONS
This study identifies staff attitude and atmosphere as important factors influencing satisfaction in Chinese nursing homes, with user concerns varying by identity and city tier. Nursing home operators should prioritize staff training to foster a warm living atmosphere, while policymakers should build user-centered evaluation systems and implement targeted regulation. Promoting personalized care for older adults nationwide will ultimately improve satisfaction with institutional care.
Zhilu Fan, Yuanshuo Ma, Lu Li et al.· BMC Health Services Research· 0 citations
There are numerous challenges for nursing students during the transition to college life, especially in adapting to new academic, social, and environmental contexts. The stress level among students has been going up dangerously due to the pressure of their academic or cultural activities. This descriptive research aims to assess the nursing student’s attitude towards the mentoring system and its role in facilitating their adaptation to college life. A quantitative research approach was employed. The study was conducted among 64 second-semester B.Sc. Nursing students at TMM College of Nursing, Kaviyoor. A structured questionnaire comprising of four sections—socio-demographic data, areas of adaptation, attitude towards mentorship, and expectations of the mentees—was used as the primary data collection tool. The academic experts established content validity of the tool and split-half method was used for reliability checking, that yielded a correlation score of 0.9 which shows positive correlation. Samples were selected using a convenient sampling technique and data collection was carried out via Google Forms. Findings were analyzed based on categorized Likert scale scores to determine levels of adaptation, attitude, and expectations. The results revealed that 70.3% of students demonstrated high levels of adaptation, while 29.7% showed moderate adaptation. All participants (100%) expressed a positive attitude toward the mentoring system, indicating strong acceptance and satisfaction. Furthermore, 99.0% of students reported high expectations regarding the mentorship experience, expecting academic, emotional and career guidance support. These results suggest that the mentoring system is positively accepted, highly effective, and considered a vital support structure that promotes students’ adjustment and holistic growth. The study recommends continued enhancement and structured implementation of mentorship programs within nursing education.
Aksa Anna Sabu, Aleena Siby, Anjaly Sethu et al.· Asian Journal of Nursing Edu...· 0 citations