Sep 2026· ANS. Advances in nursing science· 0 citations· 32 references
Medicine
TL;DR
Sources on AI literacy, responsiveness, and related nursing concepts were thematically synthesized and four defining attributes were identified: foundational understanding of AI, critical appraisal of AI outputs, ethical and accountable response, and integration with clinical judgment.
Abstract
Artificial intelligence (AI) responsiveness in nursing was conceptually analyzed to clarify its defining attributes, antecedents, consequences, and empirical referents. Guided by Walker and Avant's approach, sources on AI literacy, responsiveness, and related nursing concepts were thematically synthesized. Four defining attributes were identified: foundational understanding of AI, critical appraisal of AI outputs, ethical and accountable response, and integration with clinical judgment. Antecedents included exposure to AI-enabled tools, digital and informatics foundations, education and training, and organizational and regulatory support. AI responsiveness supports safer practice, stronger decision-making, ethical care, equity, readiness, and professional nursing accountability.
Artificial intelligence (AI) is rapidly transforming healthcare delivery and nursing education by supporting clinical decision-making, documentation, risk prediction, personalized learning, simulation, assessment, and administrative processes. The increasing availability of generative AI and large language models has f...
Natasha Mohit Mahajan· International Journal of Nur...· 0 citations
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
AI integration in nursing care may be supported by coordinated, ethically grounded, and context-sensitive strategies, and the proposed domains require further empirical evaluation across nursing settings.
D. Alrazeeni, Maryam Alharrasi, M. K. K. Rony et al.· International Nursing Review· 0 citations
BACKGROUND
Nursing educators have the responsibility to ensure human caring, the heart and soul of Professional Nursing, is preserved and evolves against pervasive technology supremacy.
PURPOSE
This paper offers a Human Intelligence (HI) Nursing Education Policy Guide for ethical complementary use of artificial intel...
Sara Horton-Deutsch, Christine Griffin, Jean Watson· Nursing Outlook· 0 citations
AIM
To synthesize evidence on artificial intelligence (AI) applications in nursing clinical practice and to develop and evaluate the content relevance of an evidence-informed, bedside-oriented mnemonic framework supporting nurses' safe and critical use of AI-supported recommendations.
BACKGROUND
AI is increasingly in...
O. Alqaisi, Safia Darwish, Mohammed Dibas et al.· Journal of Nursing Managemen...· 0 citations
A practical, system-level AI governance model grounded in risk mitigation, interdisciplinary partnership, and nursing leadership decision rights commonly enacted through nursing professional governance structures is presented.
Jing Wang, Yijiong Yang, David Marshall et al.· Nursing Administration Quart...· 0 citations
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