Teaching Ethical AI Use in Graduate Nursing Programs: A Scaffolded Model for Professional Identity Formation

Background: Generative artificial intelligence (AI) has entered graduate nursing education faster than programs can respond, raising questions about professional identity formation, epistemic accountability, and academic integrity. Method: This conceptual article presents a scaffolded AI integration model for graduate nursing programs, organized in three developmental phases (Guided Transparency, Co-constructed Inquiry, and Independent Mastery) supported by four practical frameworks: CLEAR (Context, Length, Examples, Audience, Role), RACE (Role, Action, Context, Expectation), RISE (Recall, Inspect, Scrutinize, Explain), and APE (Attribute, Protect, Examine). The model is grounded in educational scaffolding theory, dialogic pedagogy and classical rhetoric, and American Association of Colleges of Nursing Essentials competencies. Results: The model provides faculty with a structured, phase-based approach to teaching ethical AI use. Each phase increases learner autonomy while maintaining transparency and professional accountability. Informal implementation observations suggest improvements in attribution practices, critical appraisal, and reflective engagement. Conclusion: Scaffolded AI integration positions nurse educators as epistemic mentors who shape how advanced practice nurses reason with, verify, and ethically govern AI tools in preparation for practice.