What are we sharing? Faculty use of generative AI and the questions we have not yet asked in nursing education.

Abstract

Generative AI has entered nursing education rapidly and pervasively. While considerable scholarly attention has been directed toward student use of these tools, including concerns about academic integrity, citation fabrication, and the design of AI-resistant assessments. Far less attention has been paid to faculty practice. Yet many of us now routinely use generative AI in the work of teaching itself: generating grading rubrics, simplifying assignment instructions, aligning course materials to program outcomes, drafting feedback on student work, and refining policy language. This practice has spread without institutional guidance, without faculty disclosure norms, and with little professional conversation about what it means. This paper invites that conversation. It begins from the author's own practice and observations among colleagues, then examines three sets of questions raised by faculty AI use that have not yet been adequately engaged in the nursing literature. First, what is actually transferred when faculty paste assignments, rubrics, scenarios, and student work into commercial AI systems, and how do those transfers align, or fail to align, with the data practices of the platforms involved? Second, on whose behalf do faculty make these disclosure decisions, given that course materials embed the contributions of colleagues, clinical partners, prior developers, and students? Third, what does it mean for the development of nursing pedagogy when work once central to the formation of educators, including assessment design, feedback, and curricular alignment, is increasingly mediated through commercial generative tools? The paper does not argue that faculty should stop using generative AI. It argues that we should subject our own practice to the same reflection we have begun to ask of our students, and it offers a starting framework for that collective examination.