Generative AI · Education leadership

Actionable companion · Staff capability and AI literacy

From evidence to district action: A Meta-synthesis of nursing students’ experiences with generative artificial intelligence-assisted learning

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

Objective To systematically evaluate the real experiences of nursing students participating in generative artificial intelligence-assisted learning. Methods Electronic searches were conducted in the China National Knowledge Infrastructure (CNKI), VIP Database, Scopus, Wanfang Data, and the China Biomedical Literature Database (CBM), Web of Science, PubMed, Cochrane, Embase, and CINAHL databases for qualitative studies on the experiences of nursing students with generative AI-assisted learning from the establishment of the databases to March 2026. Qualitative studies on nursing students’ experiences with generative AI-assisted learning were screened, appraised, and synthesized using thematic synthesis. Results Eighteen studies were included in the synthesis. A total of 49 themes were identified and organized into 13 categories, leading to four integrated findings: (1) dual experience of empowerment and challenges, (2) internal conflict between technology and nursing humanism, (3) user experience differentiation amid Practical Constraints, (4) general demand for supporting systems and educational reform. Conclusion This study found that nursing students’ experiences with generative AI are shaped by both the opportunities and challenges associated with its use in learning. These findings highlight the need for nursing educators to strengthen students’ AI literacy, critical thinking, and ethical awareness, for curriculum designers to integrate AI-related competencies into nursing curricula while maintaining a strong emphasis on humanistic care, and for policymakers to establish clear governance frameworks and educational guidelines to support the responsible use of AI in nursing education. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261367837, identifier CRD420261367837.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Run a staff workshop using three authentic lesson-planning tasks. Ask participants to identify hallucinations, revise outputs and document when they reject the tool. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Measure error-detection accuracy before and after training, preparation time and the quality of the final lesson plans. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Shanshan Du; Sha Wang; Fengxia Yan; Di Gong; Quan Jiang; Junnan Lin. A Meta-synthesis of nursing students’ experiences with generative artificial intelligence-assisted learning. Frontiers in Medicine (2026-07-16). https://doi.org/10.3389/fmed.2026.1874158

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.