Aug 2026· The Journal of Korean Academic Society of Nursing Education· Vol 32, pp. 393-404· 0 citations
TL;DR
This analysis provides a theoretical foundation for generative AI-based learning support in nursing education and distinguishes this concept from conventional technological assistance by emphasizing cognitive facilitation and reflective learning.
Abstract
Purpose: This study aimed to elucidate the concept of generative artificial intelligence (AI)-based learning support in nursing education using Walker and Avant’s concept analysis method.Methods: A comprehensive review of scholarly articles published between 2020 and 2025 from RISS (Research Information Sharing Service), DBpia, KISS (Korean studies Information Service System), PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Scopus was conducted. Following Walker and Avant’s framework, the concept’s attributes, model cases, antecedents, consequences, and empirical referents were identified.Results: Generative AI-based learning support was defined as “a reflective teaching-learning process in which learners reconstruct knowledge and refine clinical reasoning competencies through dynamic interaction with AI, reflecting individual needs and clinical contexts”. Five defining attributes were identified: adaptive personalization, contextual presence, cognitive facilitation, conversational interactivity, and reflective instrumentality. Antecedents comprised technological infrastructure and guidelines, learners’ AI and information literacy, ethical awareness and responsibility, and instructors’ educational design competency and willingness to adopt generative AI. Consequences included strengthened critical thinking and clinical reasoning, improved learning outcomes and self-directed learning, enhanced clinical practice readiness, internalized digital literacy and ethical attitudes, and the potential enhancement of nursing care quality and patient safety.Conclusion: This analysis provides a theoretical foundation for generative AI-based learning support in nursing education. It distinguishes this concept from conventional technological assistance by emphasizing cognitive facilitation and reflective learning. The findings can inform curriculum development, instructor training, and future research, with the potential to support nursing care quality and patient safety through clinical reasoning and responsible AI use.
BACKGROUND
Although Artificial Intelligence (AI) has been explored for decades in nursing education, the existing body of knowledge provides no clear definition of the phenomenon. As technological advancements continue to shape teaching and learning environments, establishing a clear and robust definition of AI in nurs...
A. A. C. Araújo, Lucas Gardim, S. Pramila-Savukoski et al.· Nurse Education Today· 0 citations
BACKGROUND
Artificial intelligence (AI) is rapidly transforming nursing and health professions education. Although these technologies offer opportunities for innovation, they also raise concerns related to professional identity, ethical judgment, depersonalization, and burnout.
METHOD
This conceptual article synthesi...
Bonnie Soik· The Journal of Nursery Educa...· 0 citations
The findings showed that AI can enhance clinical teaching, improve nursing students' self-efficacy, and support teaching and learning and that the use of AI in nursing education is instrumental in improving the acquisition of clinical skills and teaching and learning.
S. Khunou, Carine Prinsloo· Indonesian Contemporary Nurs...· 0 citations
BACKGROUND
Generative artificial intelligence (AI) is increasingly shaping nursing education. Although many studies have examined AI use among nursing faculty and students, little is known about how clinical preceptors, who supervise students in practice settings, understand, perceive, and use these tools. As students...
Nursing academics appear to adopt AI selectively, prioritising preservation of core professional values while embracing applications perceived to enhance, rather than replace, educational practice, providing evidence for nursing education programs globally regarding faculty development, institutional policy frameworks,...
Natasha Hawkins, Anthea Fagan, Yumiko Coffey et al.· Journal of Advanced Nursing· 0 citations
This study employed a scoping review methodology guided by Arksey and O’Malley’s framework to systematically map and synthesize the evolving body of literature addressing AI-supported learning environments in nursing education, contributing to a more coherent understanding of AI’s pedagogical, structural, and equity-re...
Beulah Joy K. Damasco· Multidisciplinary Reviews· 0 citations
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