An artificial intelligence use framework for nursing education: Bridging policy and pedagogical implementation.
Abstract
Background
Artificial intelligence (AI) is transforming higher education and healthcare, yet nursing faculty lack practical guidance for determining appropriate AI use in specific academic tasks. Institutional AI policies establish boundaries but rarely address learning outcomes, task purposes, or nursing-specific responsibilities such as patient privacy, clinical judgment development, and professional accountability.
Purpose
To present the AI Use Framework for Nursing Education, a six-category pedagogical framework that operationalizes responsible AI integration at the task level.
Methods
A 10-member taskforce adapted the Artificial Intelligence Assessment Scale through targeted review of existing frameworks, iterative refinement guided by four principles (transparency over detection, learning outcome alignment, nursing-specific contextualization, and developmental scaffolding), and consultation with faculty across pre-licensure through doctoral programs.
Results
The framework provides six categories: (a) No AI, (b) AI-Assisted Editing and Formatting, (c) AI-Assisted Planning and Ideation, (d) AI-Assisted Content Creation with limited and extensive subcategories, (e) AI System Evaluation and Research, and (f) AI Application Design and Development. Three distinguishing features include embedded protected health information/personally identifiable information safeguards, differentiated process evidence recommendations enabling faculty to tailor documentation requirements to learning outcomes and task complexity, and Bloom's-aligned separation of AI evaluation from AI creation activities.
Conclusion
The framework bridges institutional policy and pedagogical practice through healthcare-specific adaptations. Implementation requires comprehensive infrastructure including AI literacy education, institutional guidelines, and faculty support. This structured, task-level guidance positions nursing education to lead intentional AI integration while preparing students for AI-enabled practice environments.