The Relational Care-AI Alignment Framework: An Ethical Model for Artificial Intelligence Involvement in Person-Centred Fundamental Care.

AIM: To propose an ethical decision-making framework for artificial intelligence (AI) involvement in person-centred fundamental care, organised around the relational dependency of care activities and grounded in the Fundamentals of Care Framework and the Caring Life Course Theory. DESIGN: Discursive paper integrating care ethics, the Fundamentals of Care Framework, the Caring Life Course Theory, person-centred care theory and technology ethics scholarship. METHODS: Peer-reviewed literature (primarily 2006-2026, plus seminal earlier care-ethics works) from nursing, bioethics, technology ethics and health informatics was searched across CINAHL Complete, PubMed, PsycINFO and Scopus. Policy documents from the World Health Organisation (WHO), the International Council of Nurses (ICN) and the European Union (EU) were also reviewed. RESULTS: The Relational Care-Artificial Intelligence Alignment (RCAA) framework operates within the three established dimensions of the Fundamentals of Care Framework (relationship, integration of care and context) and classifies fundamental care activities by relational dependency into three zones: Zone 1 (high dependency), where AI serves as background support; Zone 2 (moderate dependency), where collaborative human-AI partnership is appropriate; and Zone 3 (low dependency), where autonomous AI operation under human oversight is acceptable. Five ethical principles guide zone placement: relational autonomy, non-maleficence of depersonalisation, universal access to person-centred fundamental care, transparency and explicability and proportionality. Each is grounded in established care ethics, bioethics or AI governance traditions. Establishing the nurse-patient relationship and eliciting patient preferences sit at the entry point of the framework. CONCLUSION: The framework offers a structured approach for determining where AI can participate in fundamental care while preserving the relational essence of nursing. It is offered as a heuristic to facilitate discussion and action across clinical, educational and policy settings. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The framework can inform institutional AI adoption policies, guide nursing curricula and support regulatory standards for technology deployment in care. IMPACT: What problem did the study address? The absence of a systematic ethical framework, anchored in established person-centred fundamental care theory, for determining appropriate boundaries of artificial intelligence involvement in fundamental nursing care. What were the main findings? A relational dependency-based classification, situated within the Fundamentals of Care Framework, can guide artificial intelligence involvement through three zones of human-machine collaboration while preserving patient choice and the centrality of the nurse-patient relationship. Where and on whom will the research have an impact? Nurses, healthcare organisations, policymakers and technology developers working through artificial intelligence integration into person-centred care. PATIENT OR PUBLIC CONTRIBUTION: This study did not include patient or public involvement in its design, conduct or reporting. We acknowledge this as a limitation and discuss it explicitly in the Strengths and Limitations section.