Role reconstruction among double-qualified nursing educators in the generative AI era: a qualitative study

Background: Generative AI (GenAI) is rapidly integrating into nursing education, acting as a novel tool for knowledge mediation. While it offers new learning opportunities, its application risks disrupting essential social interactions and clinical contextualization. Double-qualified nursing educators (DQNEs) play a pivotal role in navigating this technological shift. Objectives: This study adopts a social constructivist framework to examine the pedagogical functional boundaries of GenAI in nursing education and to analyze how DQNEs reconstruct their roles to facilitate knowledge co-construction and clinical meaning-making. Design: A qualitative study using semi-structured focus group interviews. Methods: The study was conducted at a medical college in eastern China. Eighteen DQNEs with experience in GenAI integration participated. Data were collected through three focus group discussions and analyzed using thematic analysis to identify key themes. Results: Three key themes emerged: (1) Application value of GenAI in nursing education, where GenAI served as a scaffolding tool to trigger cognitive conflict, support differentiated instruction, and bridge theory with simulated scenarios; (2) Core obstacles to GenAI application, revealing challenges of tool dependency displacing human interaction, erosion of teacher authority, and institutional ambiguity; and (3) Adaptive pedagogical strategies for anchoring learning in clinical practice, where teachers employed deconstruction and reconstruction strategies and enforced social rules to anchor AI-generated plans in clinical practice. Conclusion: GenAI functions as a double-edged mediating tool that can expand the zone of proximal development but also threatens to social learning. To mitigate epistemic risks, DQNEs must evolve from information transmitters to contextual anchors, guiding students to validate GenAI outputs against clinical reality.