Agentic education · Learning science

Actionable companion · Learning design & outcomes

From AI evidence to responsible practice: Rapid review on GenAI in nursing education

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Health-professions education findings do not establish effectiveness in K–12 classrooms.

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

Abstract Backround The use of generative AI, as represented by ChatGPT, holds promising potential for nursing education. This manifests itself in various areas, including personalized learning, simulation training and teaching process support. However, its integration requires careful consideration of ethical implications, adaptation of curricula and a high level of digital competence on the part of teachers. Only in this way can potential risks, such as the distortion of knowledge, bias and educational inequalities, be avoided. Methodes Relevant publications were identified between 2019 and 2025 as part of a comprehensive literature search in the specialist databases PubMed, Embase, CINAHL and Scopus. The search was conducted using combined search terms that included the terms “generative AI”, “ChatGPT” and “nursing”. After removing duplicates and screening (PRISMA-guided), 140 full texts were analysed and divided into two publications. This rapid overview focuses on the topic of generative AI in nursing education. Results As part of the analysis of the included studies, five thematic areas were identified, which were divided into the categories of nursing education, competence development and nursing skills, implementation possibilities, examination quality and ethical considerations, and evaluated. A key theme is the dual potential of this technology: it can enrich learning through features such as virtual tutors and improved exam preparation, but it also requires critical consideration of ethical issues such as plagiarism, data bias and the need for human oversight. Outlook In this context, the conclusion emphasises the urgent need to adapt curricula and provide targeted further training for teachers so that GenAI can be used responsibly and effectively—rather than, as is often the case at present, by banning it altogether. Keywords: artificial intelligence, digital competence, digital nursing, ethical reflection, nursing education

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

A structured systematic literature review was conducted using specific search terms including “generative AI”, “GenAI”, “ChatGPT”, and “nursing” across several databases: PubMed, Embase, CINAHL, and Scopus. For each search term, corresponding MeSH terms were employed and combined using Boolean operators (Table 1). The review was restricted to publications from the last five years (2019–2025), highlighting the growing relevance of GenAI in healthcare. Inclusion criteria for this review were focused on the application of generative intelligence in professional nursing and the enhancement of nursing qualifications through AI. Exclusion criteria included machine learning, deep learning, and applications of AI in specific disease contexts. The detailed search strategies for each database are outlined in the subsequent overviews.

Limitations and uncertainty · source passage

This paper was designed as a rapid review, explicitly foregoing any claim to completeness of all available studies. In the field of generative AI, particularly in the context of nursing education, there is a wealth of new research providing relevant insights. Technological developments and the multitude of application scenarios make it difficult to provide a definitive assessment. Furthermore, only articles that were published or accessible at the time of the research were taken into account. Future systematic reviews could build on this basis and include a broader spectrum of literature and perspectives.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Laura Hinsche; Martina Hasseler; Tim Tischendorf; Tom Schaal. Rapid review on GenAI in nursing education. Frontiers in Health Services (2026-01-15). https://doi.org/10.3389/frhs.2025.1725425

Companion prepared 2026-11-01. 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.