Research on the Construction of Moral Boundaries and Ethical Norms of AI Nursing in an Aging Society

Kevin Meng

Pinnacle Academic Press Proceedings Series, 2026, vol. 10, 297-304

Abstract: With the acceleration of global population aging, artificial intelligence (AI) nursing has become an important approach to addressing the rapidly growing demand for care services. However, the ethical risks and moral uncertainties associated with AI-assisted care require systematic clarification and normative guidance. This study investigates the construction of moral boundaries and ethical norms for AI nursing in an aging society. Drawing on the ethics of care, technology ethics, and responsibility ethics, it analyzes the mechanisms through which ethical risks emerge in AI nursing, including algorithmic bias, opaque decision-making, data privacy vulnerabilities, and the potential erosion of genuine human care under the dominance of technical rationality. The paper argues that traditional nursing ethics, centered on human caregivers, faces challenges of subject generalization, role ambiguity, and unclear standards when AI systems participate in care practices. On this basis, the research demonstrates the necessity of integrating AI ethics into nursing ethics to promote a paradigm shift toward human-machine collaboration. It proposes the construction of a multidimensional nursing framework that embeds ethical review throughout the life cycle of AI nursing technologies, emphasizes algorithm interpretability and accountability, and safeguards the dignity, autonomy, and rights of older adults. The study aims to provide theoretical support and practical guidance for the healthy development of AI nursing, promote the deep integration of technological empowerment and humanistic care, and achieve both quality improvement and ethical balance in nursing services in an aging society.

Keywords: aging; artificial intelligence; nursing; ethics; care; governance (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:dba:pappsa:v:10:y:2026:i::p:297-304

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