Actionable companion · Assessment and evidence of learning

From evidence to district action: The strengths, weaknesses, opportunities, and threats of generative artificial intelligence: a qualitative study of undergraduate nursing students

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.

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:

Background While Generative Artificial Intelligence (Gen AI) is increasingly applied in nursing education, research on undergraduates’ perceptions, experiences, and impacts remains limited. Objective This study aims to explore undergraduate nursing students’ perceptions of the strengths, weaknesses, opportunities, and threats (SWOT) associated with Gen AI through qualitative research methods. Methods Using the SWOT analysis framework as the theoretical basis, data were collected through semi-structured interviews with nursing undergraduates via convenience sampling from May to July 2025 until saturation, and analyzed using Colaizzi’s phenomenological method for thematic extraction. Results A total of 36 nursing undergraduates were interviewed, from whom four main themes and 16 sub-themes were identified. These were categorized into internal and external factors. Internal positive factors (Strengths) included personalized learning assistance, skill training and curriculum support, efficiency and cognitive expansion, and data processing and learning capability. Internal negative factors (Weaknesses) involved ethical and legal risks, the generation of low-quality or inaccurate outputs, technical barriers, and cognitive and learning risks. External opportunities comprised policy and resource support, technological advancement and evolution, interdisciplinary integration and collaboration, and emerging career opportunities. External threats included technological adaptation and cost risks, digital divide and equity gap, job displacement risk, and educational integrity risk. Conclusion Undergraduate nursing students regard generative AI as a double-edged sword—its strengths in boosting learning efficiency, broadening knowledge access and simulating clinical decisions are offset by ethical, technological and equity challenges. Nursing education must therefore strengthen technical guidance, ethics training and resource optimization to maximize its strengths and opportunities while minimizing its weaknesses and threats.

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

You Yuan; Jing Fu; Lanlan Leng; Zhuosi Wen; Xiaoman Wei; Die Han; Xinyang Hu; Yu Liang; Qian Luo; Xia Zhang; Rujun Hu. The strengths, weaknesses, opportunities, and threats of generative artificial intelligence: a qualitative study of undergraduate nursing students. Frontiers in Public Health (2025-09-04). https://doi.org/10.3389/fpubh.2025.1672140

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.