Authorship,moral responsibility,and generative AI in nursing

Abstract

The growing integration of generative artificial intelligence into academic writing has generated ethical concern regarding authorship, responsibility, and professional integrity in nursing scholarship. Much existing discourse treats AI use as either inherently deceptive or inherently efficient, framing the ethical problem in terms of technological novelty rather than moral structure. This framing obscures a more fundamental normative question: under what conditions does AI-assisted writing preserve, rather than undermine, moral responsibility and professional trust? This paper advances a normative analysis grounded in first principles of moral agency, responsibility, and authorship. It argues that authorship is a moral status defined by accountability for claims, interpretations, and consequences, rather than by sole textual production. Drawing on established scholarly practices involving research assistants, statisticians, editors, technical writers, and other non-authorial contributors, the paper conceptually distinguishes the roles of author, writer, editor, and assistant, and situates generative AI within this long-standing division of academic labor. On this basis, AI is analyzed as a delegated instrument rather than an author or moral agent. The central normative claim is that AI-assisted writing is ethically permissible if and only if authorship, responsibility, and verification remain fully human and transparent. Ethical failure arises not from the use of AI itself, but from the displacement, obscuring, or abdication of moral responsibility. The paper addresses common objections concerning dilution of authorship, the analogy between AI and human assistants, the feasibility of verification, and the relevance of international variation in authorship norms. The analysis concludes by examining implications for nursing scholarship, faculty mentorship, editorial standards, and professional trust. It argues that disciplined role clarity, verification, and transparency provide a more ethically robust response to AI-assisted writing than prohibition, concealment, or reliance on technological exceptionalism.