From adoption to accountable integration in nursing higher education: A SAFE-GenAI framework for nurse academics

To propose a nursing education-specific conceptual framework for the accountable integration of generative artificial intelligence in higher education.

Generative artificial intelligence is rapidly entering nursing higher education, yet adoption has outpaced the development of clear pedagogical and professional standards. In nursing, this matters because artificial intelligence can influence how students learn to reason, evaluate evidence, uphold academic integrity, and prepare for safe clinical practice. Although recent literature identifies emerging benefits, concerns remain regarding overreliance, weak verification, inequitable access, and limited guidance for nurse academics on responsible implementation.

Discussion paper.

This paper critically examines recent scholarship on generative artificial intelligence in nursing and higher education and uses it to develop a conceptual framework for practice, curriculum, and faculty use.

The SAFE-GenAI framework is proposed as a model for accountable integration of generative artificial intelligence in nursing higher education. The framework consists of four interdependent domains: stewardship, alignment, facilitation, and evaluation, with equity positioned as a cross-cutting principle. Stewardship emphasizes ethical oversight, transparency, privacy, disclosure, and human accountability. Alignment addresses the fit of generative artificial intelligence use with learning outcomes, assessment, and professional standards. Facilitation positions nurse academics as active pedagogical guides who scaffold prompt literacy, reflection, and critical appraisal. Evaluation focuses on verification of outputs, measurement of learning outcomes, and ongoing review.

The SAFE-GenAI framework shifts the conversation from adoption alone to accountable integration. It offers a nursing education-specific structure to guide curriculum development, faculty decision-making, and future research while protecting critical thinking, academic integrity, artificial intelligence literacy, and practice readiness.