Generative AI Ethics Toolkit for Clinical Prioritization in Nursing Education

Abstract

The rapid adoption of generative artificial intelligence (GenAI) in nursing education presents urgent ethical challenges, particularly as students employ these tools in high-stakes clinical prioritization tasks. Although general AI literacy initiatives exist, nursing-specific resources for addressing risks of bias, inequity, and misinformation in GenAI-mediated decision making remain limited. This study adapts an interdisciplinary, open-access AI ethics learning toolkit to develop a conceptual, nursing-focused mini-toolkit. The adaptation integrates three complementary ethical frameworks: Rest's Four-Component Model at the learner level (moral sensitivity, judgment, motivation, and character), cyberethics principles at the professional level (autonomy, nonmaleficence, beneficence, justice, and explicability), and Chan's ecological model at the institutional level (pedagogy, governance, and operations). Collectively, these frameworks scaffold ethical reasoning across individual, professional, and systemic domains. The resulting mini-toolkit includes case vignettes that simulate GenAI-supported prioritization scenarios, reflection prompts to cultivate bias recognition and accountability, and rubric criteria to guide faculty in assessing ethical reasoning and oversight. Rather than prescribing fixed answers, the toolkit creates structured opportunities for dialogue, inquiry, and professional judgment. By embedding ethical reasoning into prioritization pedagogy, the toolkit positions faculty and students as critical evaluators rather than passive users of GenAI, reinforcing nursing's commitment to equity, justice, and patient-centered care. Situated within a broader global movement toward responsible and human-centered AI integration, this study contributes to nursing ethics by translating abstract principles into pedagogically actionable tools and modeling a methodology for adapting interdisciplinary frameworks into nursing-specific applications. Future work should pilot and evaluate the toolkit in authentic educational contexts, examining its impact on ethical reasoning, bias recognition, and collaborative decision making.

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