A concept analysis of twenty-eight published studies has defined artificial intelligence and algorithmic literacy among nurses as a multidimensional competency that reaches well beyond technical operation or a simple willingness to adopt emerging tools. Conducted by researchers Liu Liu, Honglei Yang, Qing Yang, and Yonghong Lu, the study was published in the journal BMC Nursing to establish a rigorous conceptual foundation for nursing education, clinical practice, and responsible artificial intelligence governance. The authors concluded that AI and algorithmic literacy represents a nurse’s professional capacity to understand, critically interpret, oversee, and responsibly integrate algorithmic outputs into clinical judgment while steadfastly preserving patient safety and professional accountability. By clarifying this evolving concept, the investigation seeks to reposition nurses not merely as passive end users of technological systems, but as accountable professional actors who supervise automated recommendations and safeguard direct patient care.
To clarify how the concept has been conceptualized and applied across modern healthcare and academic settings, the authors selected Rodgers’ evolutionary concept analysis design. The analytical process entailed extracting, comparing, and synthesizing four primary structural elements from the included literature: defining attributes that characterize the concept, antecedents that must precede its development, consequences that result from its application, and related concepts that share conceptual boundaries with algorithmic literacy in nursing.
The research team executed a systematic literature search across four major electronic databases: Web of Science, PubMed, Embase, and the Cumulative Index to Nursing and Allied Health Literature, known as CINAHL. The systematic search strategy utilized terms specifically targeting nurses, nursing students, health workers, artificial intelligence literacy, algorithmic literacy, AI competence, AI readiness, and generative AI literacy. The screening window was restricted to articles published between January 2020 and June 2026. Following screening, twenty-eight published articles satisfied the selection criteria and were incorporated into the final concept analysis synthesis.
The synthesis identified four core defining attributes that delineate artificial intelligence and algorithmic literacy within the nursing profession. The first defining attribute is a conceptual understanding of AI-mediated nursing contexts, reflecting comprehension of how computational tools interact with healthcare environments. The second attribute involves the critical interpretation and risk recognition of algorithmic outputs, requiring nurses to actively identify potential hazards and limitations in automated results. The third defining attribute focuses on clinical integration and human oversight of artificial intelligence recommendations, emphasizing continuous human supervision when applying algorithmic suggestions to nursing judgment. The fourth defining attribute is ethical governance awareness grounded in professional accountability, ensuring that clinical reliance on algorithms remains tethered to professional ethics and nursing responsibility.
The investigation further extracted a distinct set of antecedents that precede and enable the development of algorithmic literacy among nursing professionals. The synthesis revealed that essential antecedents include dedicated AI education and training programs, solid digital or informatics foundations, and prior exposure to artificial intelligence technologies. At the individual and institutional levels, antecedents also encompass individual readiness to engage with technology, supportive organizational environments, faculty preparedness across nursing education programs, and structured governance environments that establish rules and expectations for technological integration.
In analyzing the downstream consequences that follow from established algorithmic literacy, the authors recorded multiple positive educational, clinical, and organizational impacts. Documented consequences include improved learning outcomes for students and practicing nurses, enhanced critical thinking capabilities, greater AI readiness and technological adoption, heightened patient safety awareness, more effective human-AI collaboration in healthcare settings, and broader professional innovation.
The concept analysis also differentiated nurse-specific AI and algorithmic literacy from several closely related concepts present in the health and technical literature. The researchers identified related concepts including general AI literacy, algorithmic literacy, digital literacy, nursing informatics competence, AI readiness, AI competence, and AI self-efficacy. By comparing these related constructs, the synthesis established that algorithmic literacy in nursing constitutes a distinct, multidimensional concept tailored to the clinical oversight and ethical responsibilities of nursing care.
Drawing these findings together, the authors formulated both a brief definition and an expanded, comprehensive conceptual definition of AI and algorithmic literacy among nurses. The researchers noted that clarifying this concept provides essential support for developing standardized nursing AI education curricula, formal competency frameworks, validated measurement tools, specialized clinical training programs, and comprehensive governance strategies. As artificial intelligence technologies become increasingly embedded across clinical nursing practice, the authors concluded that establishing clear definitions ensures nurses maintain the supervisory capability and professional accountability required to interpret algorithmic recommendations and protect patient safety.
Subject of Research: Concept analysis of artificial intelligence and algorithmic literacy in nursing
Article Title: AI and algorithmic literacy among nurses: a concept analysis
Article References: Liu, L., Yang, H., Yang, Q., & Lu, Y. (2026). AI and algorithmic literacy among nurses: a concept analysis. BMC Nursing. https://doi.org/10.1186/s12912-026-05518-7
Image Credits: AI Generated
DOI: 10.1186/s12912-026-05518-7
Keywords: nursing, artificial intelligence, algorithmic literacy, nursing informatics, clinical judgment, patient safety, Rodgers evolutionary concept analysis, health care governance, algorithmic, literacy, nurses, concept
News Source: Blake Davidson. (November 10, 2026). Concept Analysis Outlines AI and Algorithmic Literacy Framework for Nurses. Scienmag.
