Authorship, moral responsibility, and generative AI in nursing

Area of Science:

• Nursing Scholarship
• Academic Integrity
• Artificial Intelligence Ethics

Background:

• Generative artificial intelligence (AI) integration in academic writing raises ethical concerns about authorship, responsibility, and professional integrity.
• Current discourse often frames AI use as either deceptive or efficient, overlooking the core moral structure of its ethical implications.
• A fundamental question remains: under which conditions does AI-assisted writing uphold, rather than erode, moral responsibility and professional trust?

Purpose of the Study:

• To advance a normative analysis of generative AI in academic writing, grounded in principles of moral agency, responsibility, and authorship.
• To conceptually distinguish the roles of author, writer, editor, and assistant, and situate generative AI within this framework.
• To establish ethical permissibility criteria for AI-assisted writing in scholarly contexts.

Main Methods:

• Normative analysis based on first principles of moral agency, responsibility, and authorship.
• Conceptual distinction between author, writer, editor, and assistant roles, drawing parallels with existing scholarly practices.
• Analysis of generative AI as a delegated instrument, not a moral agent or author.

Main Results:

• Authorship is defined by accountability for claims, interpretations, and consequences, not solely by textual production.
• Generative AI is ethically permissible if and only if authorship, responsibility, and verification remain fully human and transparent.
• Ethical failures stem from the displacement, obscuring, or abdication of moral responsibility, not AI use per se.

Conclusions:

• Disciplined role clarity, verification, and transparency offer a robust ethical framework for AI-assisted writing in nursing scholarship.
• Addressing objections regarding authorship dilution, AI-human analogies, verification feasibility, and international norms is crucial.
• Maintaining human oversight and accountability is paramount for preserving professional trust and integrity in the face of AI integration.