A Curriculum Framework for Embedding Artificial Intelligence Literacies in Pre-Registration Nursing Education
Artificial Intelligence (AI) is rapidly transforming healthcare, offering unprecedented opportunities for enhancing patient care, optimizing clinical workflows, and supporting decision-making. As AI technologies become more integrated into clinical settings, nursing education must evolve to prepare future nurses to work confidently and safely with AI tools. Embedding AI literacies in pre-registration nursing education is no longer optional—it is essential.
Why AI Literacy Matters for Nurses
Nurses are often the frontline users of healthcare technologies, from electronic health records to decision support systems. AI literacies equip nursing students with the knowledge to:
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Understand AI concepts such as machine learning, predictive analytics, and natural language processing.
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Interpret AI outputs critically and make informed clinical decisions.
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Recognize limitations and biases in AI systems to ensure ethical and equitable patient care.
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Collaborate effectively with multidisciplinary teams, including data scientists and IT professionals.
By fostering these skills early in their education, nurses are better prepared to embrace technology-driven healthcare without compromising the human-centered care that defines the profession.
Key Components of an AI Curriculum Framework
A robust curriculum framework for AI literacies in nursing education should include the following elements:
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Foundational AI Knowledge
Introduce students to basic AI concepts, terminology, and real-world healthcare applications. This may include understanding how AI algorithms work and the types of data used in clinical AI tools. -
Ethical and Legal Considerations
Educate students on patient privacy, data security, and the ethical use of AI in healthcare. Nursing students should understand issues like bias in algorithms, informed consent, and accountability. -
Practical Skills and Simulations
Provide hands-on learning through simulation labs, case studies, and AI-powered clinical decision tools. This helps students develop confidence in using AI technologies in safe and controlled environments. -
Interdisciplinary Collaboration
Encourage collaboration with computer science, engineering, and healthcare informatics students. This fosters a holistic understanding of AI implementation and problem-solving in healthcare contexts. -
Critical Thinking and Decision-Making
Emphasize the importance of combining AI insights with clinical judgment. Nurses must learn to critically evaluate AI recommendations rather than follow them blindly.
Strategies for Implementation
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Integrated Curriculum: Embed AI literacy modules throughout the nursing program, rather than as a standalone course.
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Faculty Development: Provide training for educators to confidently teach AI concepts.
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Assessment and Evaluation: Use case-based assessments, simulations, and reflective exercises to evaluate students’ AI competencies.
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Continuous Updates: Keep the curriculum adaptable to incorporate emerging AI technologies and healthcare innovations.
Conclusion
As AI continues to reshape healthcare, nursing education must adapt to equip future nurses with essential AI literacies. A thoughtfully designed curriculum framework ensures that nurses not only understand AI but also use it responsibly to enhance patient care. By integrating AI education early in pre-registration nursing programs, we prepare a workforce capable of navigating the complexities of modern healthcare with confidence, competence, and compassion.
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