An artificial intelligence use framework for nursing education: Bridging policy and pedagogical implementation.

A Five-Step Faculty Development Checklist for Artificial Intelligence in Nursing Education.

BACKGROUND Artificial intelligence (AI) is increasingly available to faculty and students, yet adoption remains uneven. Faculty report uncertainty about capabilities, limitations, and ethical boundaries, and lack time for integration, while institutions grapple with policy, privacy, and equity concerns. The purpose of this article is to provide a concise, nursing faculty-centered AI development checklist for responsible AI integration in teaching and assessment. METHOD AI implementation guidance was synthesized into a five-step checklist for faculty AI competency development. RESULTS The checklist converts exploratory evidence into a logical faculty development plan, emphasizing practical steps that reduce cognitive load, standardize expectations, and support equitable access to enable rapid adoption and consistent implementation for individuals, programs, and institutions. CONCLUSION With a concise evidence-based checklist, nurses can achieve the benefits of AI while advancing equity and preserving the human-centered core of the nursing profession. This checklist simplifies steps for understanding new technologies, adds to research on competencies, and improves instructional efficiency.

An Interactive, Project-Based AI in Healthcare Curriculum for Clinicians and Medical Educators

This curriculum offers a generalizable, no-cost model for closing the AI evaluation skills gap in healthcare education, combining interactive correction with a mandatory, learner-defined capstone project.

Empowering Future Nurses: Integrating Artificial Intelligence into Nursing Education

: Artificial intelligence (AI) is increasingly transforming healthcare and creating new competency requirements for future nurses. This narrative review examines the applications, benefits, challenges, and curriculum implications of integrating AI into nursing education. Current evidence shows that AI can support personalised learning, intelligent tutoring, automated feedback, teaching preparation, virtual patients, and simulation-based training. These applications may improve students’ knowledge acquisition, engagement, communication, clinical reasoning, decision-making, and readiness for practice. However, AI integration also raises concerns about inaccurate or fabricated information, algorithmic bias, patient privacy, data security, academic integrity, accountability, and excessive dependence on automated tools. Differences in faculty readiness, institutional resources, and access to technology may further limit effective and equitable implementation. Preparing future nurses for AI-enabled healthcare therefore requires more than technical skills. Nursing students need AI literacy, critical evaluation skills, information verification ability, ethical awareness, data protection knowledge, and strong clinical judgement. AI content should be progressively integrated into nursing informatics, ethics, research, clinical, and simulation courses, supported by faculty development, clear institutional policies, and assessment methods that evaluate students’ reasoning processes. Future research should use longitudinal, experimental, and multicentre designs to examine the long-term effects of AI on clinical competence, patient safety, and professional practice. AI should ultimately enhance rather than replace nurses’ clinical judgement, ethical responsibility, communication, and compassionate care.

Guiding Doctoral Students in the Appropriate and Ethical Use of Artificial Intelligence for the Doctor of Nursing Practice Proposal Development.

Continuing Medical Education as a Workforce Learning System for Artificial Intelligence in Clinical Practice

It is argued that AI-focused CME should be designed as a longitudinal organizational learning system rather than a discrete instructional event, and implications are offered for technology integration, faculty development, and organizational learning in clinical education.

Artificial intelligence and digital health competencies in undergraduate nursing education: A scoping review.

The findings highlight the need for longitudinal, program-wide integration of AI and digital health competencies, explicit alignment with recognised competency frameworks, and investment in educator capability to prepare graduates for safe and effective practice in increasingly AI-enabled healthcare environments.