Artificial intelligence (AI) is changing how knowledge is generated, interpreted, taught, and applied across healthcare. For nursing science, this moment brings both opportunity and responsibility. AI will influence not only what we study, but also how we teach, mentor, and prepare the next generation of nurses.
In academic settings, AI can support personalized learning, strengthen simulation-based education, help students develop clinical reasoning, and assist faculty in creating learning experiences that are adaptive, interactive, and evidence-informed. Yet its greatest educational value may be in prompting us to rethink pedagogy itself: how students question information, evaluate evidence, recognize bias, protect privacy, and use emerging AI tools, chatbots, and robots – all without weakening professional judgment. AI should not replace critical thinking; it should deepen it.
AI’s potential is especially important in areas where nursing has long led: health promotion, disease prevention, preventive services, chronic illness management, care coordination, patient education, symptom science, health equity, and population health.
The future value of AI will depend on whether nurses help shape the questions being asked, the data being used, the tools being adopted, and the ways AI-generated outputs are translated in healthcare. Nursing science has always been grounded in understanding and treating the whole person within the context of family, community, culture, environment, and systems of care. That perspective is urgently needed in the age of AI and the Fourth Industrial Revolution. AI algorithms can identify patterns in large datasets, summarize complex information, and generate predictions, but they do not inherently understand the lived experience, social context, structural inequities, or the meaning of health and illness in a person’s life. Nursing science brings that essential component to patient care, ensuring that technological innovation remains grounded in human experience, equity, and evidence.
AI can support scholarship in several important ways. It can help researchers analyze large volumes of clinical data, identify patterns in outcomes, examine communication between clinicians and patients, and generate insights from a broad range of data sources. AI can also assist with literature reviews, data management, survey analysis, qualitative coding, and predictive modeling. Used wisely, these tools can help nursing scholars ask more comprehensive, systems-oriented questions, study larger and more diverse populations, advance precision health, and move evidence into practice more quickly.
What was the AI model designed to do? Who was represented in the model training data? Who may be missing or underrepresented? What historical bias may be embedded in the data? Who is accountable for the AI model’s output?
AI’s potential is especially important in areas where nursing has long led: health promotion, disease prevention, preventive services, chronic illness management, care coordination, patient education, symptom science, health equity, and population health. AI may help identify patients overdue for immunizations, screenings, wellness visits, or other evidence-based preventive services. AI can also detect gaps in follow-up care, flag patients at risk for avoidable hospitalization, and personalize health education and care based on literacy level, language, culture, and patient preferences. In workforce research, AI can help us better understand staffing patterns, burnout, moral distress, and the organizational conditions that support high-quality healthcare.
Still, the future of AI in nursing science cannot be measured or defined by the speed of implementation. It must be guided by rigor, ethics, transparency, equity and appropriate regulation. AI tools are only as trustworthy as the data, assumptions, and design decisions behind them. Too often, AI can function as a “black box,” producing recommendations or predictions without making clear how those conclusions were reached. When AI model training data reflect unequal access, biased treatment, or missing voices, AI can reinforce existing inequities and extend their effects across decisions, systems, and patient care. Nurses are well-positioned to ask the questions that matter: What was the AI model designed to do? Who was represented in the model training data? Who may be missing or underrepresented? What historical bias may be embedded in the data? Who is accountable for the AI model’s output?
The next generation of nursing science will not be defined by technology alone. It will be defined by how well we leverage technology to advance population health, healthcare, and the nursing profession.
Academia has a central role in preparing students, faculty and the profession for this future. AI literacy should be part of nursing education, not as a replacement for scientific reasoning, but as an extension of it. Future nurse scientists will need to understand how AI tools work, how to evaluate their limitations, and how to integrate them responsibly into research, education, practice and policy. AI discovery and implementation will also require strong interprofessional partnerships and science teams with data scientists, engineers, informaticians, clinicians, ethicists, patients, families, and communities. Notably, AI should not narrow the scope of nursing science; it should expand it.
The next generation of nursing science will not be defined by technology alone. It will be defined by how well we leverage technology to advance population health, healthcare and the nursing profession. If nurses lead with curiosity, scientific rigor, ethical commitment, and a focus on equity and person-centered care, AI can become a powerful tool for discovery and transformation. The future of AI in healthcare needs nursing to lead, not only to drive learning health systems, but to make AI implementation and regulation more humane, just, and responsive to the people we serve.