Leadership Considerations for the Integration of AI in Nursing Education

Abstract

Artificial intelligence (AI)-based technologies are increasingly impacting health care systems and professional practices. As the sciences undergirding these innovations continue to advance, the AI-based tools available for practical applications become more numerous and sophisticated. Still, all technical tools require human oversight. AI models are not suitable for all situations and can “drift” in their performance over time. Nurses will encounter outputs from many AI systems and contribute inputs to many AI databases through their work activities. Therefore, nursing education must rapidly evolve to prepare professionals to work ethically and effectively with AI-based tools. Several national nursing organizations have recently convened workgroups and released formal statements regarding the critical integration of AI into nursing education.1-3 Working together, nursing education leadership can demonstrate a positive vision for the use of AI to support professional practice and human health. These technologies have great potential but must be used thoughtfully in alignment with the core values of nursing. To accomplish this goal, we advocate 3 pillars for AI education in nursing: (1) prioritize AI investments and collaboration across nursing education, (2) ensure basic AI competencies for all nurses, and (3) provide just-in-time training for clinically specific AI competencies. Prioritize AI Investments and Collaboration in Nursing Education Significant investments are needed in people and programs across nursing education and nursing science to match the scope of technological change. Just like the concept of “team lifting” in clinical practice, this is a bigger job than 1 person or 1 school can manage. We need to identify areas for extended collaboration across nursing schools, other health professions education, universities, and health centers. Seeking shared priority areas for research and development in nursing will help to expand to the pertinent knowledge base. Nursing informatics faculty with joint clinical-educational appointments can foster collaboration and embedded expertise within the clinical setting. The current applications of AI in clinical practice can be translated directly into the nursing education context. This alignment can help students understand the clinical application of the AI knowledge and skills they are developing. Within nursing education, it is critical to consider existing AI skills in nursing school staff and students as well. Many people have advanced knowledge about AI-based tools from usage in other settings. Humility and open-mindedness among faculty and senior leaders can go a long way when we are all learning together. Ensure Basic AI Competencies for All Nurses Leaders should encourage “AI basics” alignment between health systems and nursing education. Nursing schools should be expected to ensure basic competencies related to the use of AI in clinical scenarios. Nursing curricula need to be updated to incorporate principles of AI literacy, responsible use, and approaches to continuous improvement in this area. AI education programs should align with nursing essentials and prioritize the core values of caregiving.4 Development of microlearnings and rigorously assessed competency credentials can establish foundational standards and encourage individual growth. Students learn in many ways and are on unique competency trajectories. Assessment of basic AI skills should be incorporated in all degree programs, but different pathways for learning should be encouraged. Assessment of basic and more advanced AI skills can be built into simulation activities and clinical readiness boot camps. As these tools, and necessary basic competencies, are changing rapidly, professional development should be available for all current nursing faculty and practicing nurses. Provide Just-in-Time Training for Clinically Specific AI Competencies Basic AI literacy, essentials, and/or core competencies should be required to practice nursing, but clinically specific competencies will also be necessary and evolving. Each clinician will interact with a different set of AI tools inside a different organizational infrastructure. They will need to be trained on the specifics of those tools and processes. For optimal outcomes, nurses must be included as core members of AI governance and educational teams within health systems. Professional development for evolving uses of AI in clinical practice will benefit from pervasive collaboration between education and health systems. Notes on Confidence and Safety The core goals of AI education should be to equip nurses to use these tools safely and confidently in clinical practice. Appropriate professional confidence comes from having background knowledge and experience and knowing how to handle different situations involving AI tools and outputs. Confidence and skill also come from working within competent systems. Nurses can monitor and contribute to safety, but AI use also requires systems-level safety measures, such as ongoing education, regulation, and institutional oversight. There is a large gap in evidence-based practice related to AI confidence and safety in nursing practice. Professional nursing needs ongoing scientific study, including dedicated funding, around how professional nursing activities and AI systems can best evolve together in health care. We will have to learn many new things and unlearn some old things simultaneously as AI reshapes evidence, processes, and professional practices. Uptake is uneven and can be contentious. While some people are enthusiastic about AI, others are fundamentally opposed to AI in any form. Even without resistance, it takes extra time on the front end for people to learn new technologies. Reimagining and then successfully implementing changes to workflows, processes, and daily patterns is a heavy lift. Additionally, nursing leaders must consider the budgetary, personnel, and legal implications of the technology costs and governance structures related to AI adaptation and usage. Conclusions We must evolve nursing education and continuous professional development for educators, researchers, and clinicians to ensure AI will be used ethically and effectively in the current and future health care landscape. Decisions about the use of AI-based tools and their implementation in health care systems should always include the guidance of nurses. Nursing education leaders have a responsibility to ensure all nurses are prepared to contribute to the responsible use of these powerful new tools and technologies.

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