From leadership research to district action: NAIL-G: a conceptual best-fit framework for nursing ai literacy and governance anchored in socio-technical safety science

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Background Artificial intelligence is reshaping health care delivery and education, but nurses lack discipline-specific guidance that integrates artificial intelligence literacy with socio-technical safety and governance. Existing AI literacy and digital competence models are often generic and do not reflect nursing standards, work systems or responsibilities for patient safety and equity. Methods A best-fit framework synthesis and concept development were used to integrate scholarship on artificial intelligence literacy, digital competence, socio-technical safety, nursing competency standards and policy guidance on transparent use of artificial intelligence in scholarly work, with extracted constructs mapped to the AACN Essentials and socio-technical work-system dimensions. Results The Nursing Artificial Intelligence Literacy and Governance (NAIL-G) framework comprises five domains covering Foundations & Tools; Quality, Safety & Risk; Professionalism, Ethics & Law; Systems, Equity & Social Impact; and Scholarship, Communication & Governance. It positions artificial intelligence literacy as competency-plus-governance, emphasizing critical judgement, verification, disclosure and equity-aware practice across nursing education, clinical work and scholarly communication. Conclusions NAIL-G offers curriculum designers, educators, regulators and clinical leaders a structured way to embed artificial intelligence capabilities, safeguards and governance expectations across nursing practice and education. The framework can guide integration of artificial intelligence-related learning outcomes, assessment tasks and safety checks into curricula, support reflective use of artificial intelligence in clinical reasoning and documentation, and strengthen nurses’ confidence to raise concerns about unsafe or inequitable systems.

Study methods

This paper reports on a conceptual study using a best-fit framework synthesis combined with concept-development strategies drawn from nursing scholarship. Best-fit framework synthesis is useful when multiple candidate models exist, and the goal is to integrate them into a context-specific structure that offers high explanatory and practical fit. Concept development methods, such as those described by Walker and Avant and by Rodgers, support clarification of attributes, antecedents, and consequences of key constructs, and refinement of domain boundaries.

Limitations

Recent scholarship has articulated conceptualizations of AI literacy as encompassing knowledge, skills, attitudes, and ethical awareness needed to interact with AI critically and effectively [4, 5]. Digital competence frameworks such as DigComp 2.2 and the Jisc Digital Capabilities Framework extend this perspective by situating AI within broader digital skills for citizens and higher-education staff [6, 7].

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Fatma M. Ibrahim; Ghada Shahrour; Israa A. M. Abuijlan; Amal Mohammed Abdallah; Asmaa Alsuwayed; Abdulkarem Radhi Alanazi. NAIL-G: a conceptual best-fit framework for nursing ai literacy and governance anchored in socio-technical safety science. BMC Nursing (2026-04-11). https://doi.org/10.1186/s12912-026-04555-6

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.