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Author ORCID Identifier

https://orcid.org/0009-0006-8303-3212

Abstract

Generative artificial intelligence and large language models are influencing healthcare practice and education, offering benefits such as improved efficiency, decision support, and learning opportunities. However, concerns remain regarding the accuracy, bias, and ethical implications of AI-generated content, particularly in clinical decision-making contexts. Despite the growing presence of AI in healthcare, undergraduate nursing curricula often lack opportunities for students to develop AI literacy alongside clinical reasoning skills. This case study describes the design and implementation of an educational activity in which 22 children’s and general integrated nursing students used Microsoft CoPilot to generate and evaluate nursing care plans for children with renal and endocrine conditions. The activity used a blended learning approach incorporating self-directed learning, group work, and structured reflection. During the activity, students critically evaluated AI-generated nursing care plans, identifying inaccuracies, gaps, or inconsistencies in the AI-generated outputs, and revised the care plans using evidence-based resources. The conclusion is that LLMs can be integrated as valuable educational tools when used to support, rather than replace, clinical reasoning. Integrating AI literacy into nursing education is necessary to support safe, ethical, and accountable practice in increasingly digital healthcare environments.

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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