Registered Nurses' Attitudes Towards ChatGPT and Self-Directed Learning: A Cross-Sectional Study.

Maintaining professional competence in modern healthcare requires continuous adaptation to rapid medical advancements and persistent staffing shortages that strain existing resources. It was already known that digital tools can foster independent educational engagement by providing immediate access to vast information repositories and interactive learning modules. Self-directed learning serves as a foundational pillar for lifelong clinical excellence within these increasingly complex environments characterized by high-stakes decision-making. While general educational technologies have been explored, the specific integration of large language models into nursing workflows remains under-examined in current literature. The potential for conversational Artificial Intelligence (AI) to motivate users suggests a transformative shift in how practitioners acquire new skills and maintain their licensure. However, the specific factors influencing how clinical staff perceive and use these tools are not well defined across different cultural contexts. This absence of evidence motivated the current investigation into the relationship between technological attitudes and educational autonomy among registered nurses.

This investigation evaluates the correlations between socio-demographic variables, perceptions of conversational artificial intelligence, and independent educational behaviors among nursing professionals in Taiwan. The researchers sought to determine how familiarity with emerging digital tools influences the drive for lifelong professional improvement in a rapidly changing technological landscape. By examining a diverse cohort of practitioners, the study identifies specific predictors that contribute to a nurse's willingness to engage in autonomous skill acquisition. Understanding these dynamics allows healthcare organizations to better integrate innovative technologies into their existing training frameworks and professional development pathways. The project specifically focuses on how work history and technological awareness shape the adoption of modern educational aids like large language models. Such insights are necessary for developing targeted interventions that address the unique needs of a multi-generational workforce facing increasing clinical demands. This study also examines how social influences and technological perceptions impact the overall readiness of nurses to adopt AI-driven learning strategies.

The researchers implemented a cross-sectional study design using an online survey distributed through social media platforms including Facebook and LINE, a widely used messaging application in East Asia. Recruitment efforts targeted five distinct digital groups to reach a broad spectrum of practitioners across various healthcare settings, eventually reaching over 1000 potential participants. Participants provided detailed socio-demographic information alongside responses to a dedicated self-directed learning scale and an assessment of their views on artificial intelligence. The analytical framework employed descriptive statistics, t-tests, and Pearson's correlation to identify significant relationships within the collected dataset. One-way analysis of variance and multiple linear regression analysis further clarified the predictive power of specific variables on educational outcomes and technological adoption. Adherence to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines ensured methodological transparency and rigor throughout the research process. The use of these specific statistical tools allowed for a robust evaluation of how different factors like work experience and awareness interact to influence learning.

Analysis of the 330 participants revealed that 46.7% had previously engaged with Generative Pre-trained Transformer (ChatGPT) for various professional or personal tasks, indicating a significant but not universal adoption. The data indicated that work experience and general awareness of the technology served as significant predictors of self-directed learning, accounting for 32.0% of the observed variance. Among the subset of nurses already familiar with the tool, nursing experience combined with the technological and social influence of the platform explained 35.3% of the variance in learning behaviors. Demographic findings showed that 50.6% of the cohort worked in hospital environments, while 51.8% possessed more than 15 years of clinical experience. Statistical breakdowns revealed that 78.2% of the respondents did not occupy supervisory roles, providing a clear view of the attitudes held by frontline staff rather than just management. These results highlight a moderate level of adoption alongside a strong correlation between professional maturity and the use of digital educational resources. The regression models specifically identified that technological influence is a key driver for those who have already integrated AI into their workflows.

The findings suggest that integrating ChatGPT-supported self-directed learning into nursing curricula requires a nuanced approach tailored to individual experience levels and technological backgrounds. Administrators should prioritize customized support and training programs that account for the varying degrees of technological comfort found within the diverse nursing workforce. Leveraging the social and technological influence of artificial intelligence can significantly enhance the effectiveness of continuous professional development initiatives in clinical settings. Future strategies must focus on bridging the gap between novice and expert practitioners to ensure equitable access to advanced educational tools. The study underscores the importance of nursing experience as a catalyst for professional growth when paired with modern digital aids like generative AI. Ultimately, fostering a culture of autonomous learning through innovative technology will be vital for maintaining high standards of patient care in evolving healthcare landscapes. These conclusions emphasize that a one-size-fits-all training model is insufficient for the complex needs of modern registered nurses.