AI disclosure uncertainty in nursing education: A pedagogical scaffold for professional learning

Generative artificial intelligence (AI) is rapidly reshaping how nursing students produce and communicate academic work, intensifying questions about authorship, accountability, and trust. Although AI disclosure is widely promoted in nursing education, disclosure practices remain uneven and contested, in part because expectations often outpace pedagogical support. Contemporary scholarship describes a disclosure paradox in which transparency invites skepticism while nondisclosure frequently goes undetected, thus creating incentives for silence. Learners and educators report that disclosure can be emotionally burdensome due to uncertainty about what counts as acceptable AI use. Evidence suggests that when AI is integrated intentionally and transparently, human-AI collaboration can facilitate and ameliorate creative and context-sensitive tasks; therefore, this paper argues that AI disclosure should be reframed as a professional identity formation practice grounded in transparency and accountability rather than treated primarily as a compliance statement. Drawing on established learning theory, we describe the AI Disclosure Coach (AiDiCo) as an illustrative pedagogical scaffold that guides learners to map AI contribution, document verification and revision decisions, and articulate how human judgment shaped the final product. We outline boundary conditions and ethical limits of disclosure scaffolds, identify instructional strategies to sustain meaningful engagement and counter performative disclosure, and consider implications for globally relevant implementation. In particular, we highlight the need for platform-agnostic disclosure pedagogy that remains adaptable across varied governance structures, languages, and resource conditions, thereby supporting sustainable approaches to ethical AI engagement. Framed as coevolution, this approach positions nursing education and generative AI as developing in tandem, with disclosure pedagogy strengthening accountability as AI capabilities and professional expectations continue to change.