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Artificial Intelligence Responsiveness in Nursing: A Concept Analysis for Advancing Nursing Science
Areej Al-Hamad1, Yasin M Yasin, Lujain Yasin
1Author Affiliations: Daphne Cockwell School of Nursing, Faculty of Community Services, Toronto Metropolitan University, Toronto, Ontario, Canada (Drs Al-Hamad and Yasin); and Faculty of Nursing and Health Sciences, University of New Brunswick, Fredericton, New Brunswick, Canada (Dr M. Yasin).
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.
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