School of Science, Engineering and Technology, RMIT University, Viet Nam.
School of Mathematics and Statistics, University of Melbourne, Australia.
Nurse Educ Pract. 2024 Aug;79:104079. doi: 10.1016/j.nepr.2024.104079. Epub 2024 Jul 20.
The aim of this paper is to investigate the incorporation of visual narratives, such as comics and graphics, into nursing education using Generative Artificial Intelligence (GAI) models like DALL-E.
Visual narratives serve as a powerful method for communicating intricate concepts in nursing education. Despite their advantages, challenges in creating effective educational comics persist due to the need for expertise in graphic design and the associated time and resource constraints.
This study examines existing literature that highlights the efficacy of visual narratives in education and demonstrates the potential of GAI models, specifically DALL-E, in creating visual narratives for nursing education.
We analyze the potential of GAI models, specifically DALL-E, to create visual narratives for educational purposes. This was demonstrated through illustrative examples addressing sensitive topics, illustrating research methodology and designing recruitment posters for clinical trials. Additionally, we discussed the necessity of reviewing and editing the text generated by DALL-E to ensure its accuracy and relevance in educational contexts. The method also considered legal concerns related to copyright and ownership of the generated content, highlighting the evolving legal landscape in this domain.
The study found that GAI, specifically DALL-E, has significant potential to bridge the gap in creating visual narratives for nursing education. While offering cost-effectiveness and accessibility, GAI tools require careful consideration of challenges such as text-related errors, misinterpretation of user prompts and legal concerns.
GAI models like DALL-E offer promising solutions for enhancing visual storytelling in nursing education. However, their effective integration requires a collaborative approach, where educators engage with these tools as co-pilots, leveraging their capabilities while mitigating potential drawbacks. By doing so, educators can harness the full potential of GAI to enrich the educational experience for learners through compelling visual narratives.
本文旨在探讨利用 DALL-E 等生成式人工智能（GAI）模型将视觉叙事（如漫画和图形）融入护理教育。
视觉叙事是在护理教育中传达复杂概念的有力方法。尽管它们具有优势，但由于需要图形设计方面的专业知识以及相关的时间和资源限制，因此在创建有效的教育漫画方面仍然存在挑战。
本研究考察了强调视觉叙事在教育中的功效的现有文献，并展示了 GAI 模型（特别是 DALL-E）在为护理教育创建视觉叙事方面的潜力。
我们分析了 GAI 模型（特别是 DALL-E）在为教育目的创建视觉叙事方面的潜力。通过针对敏感主题的说明性示例、说明研究方法和设计临床试验招募海报，展示了这一点。此外，我们还讨论了审查和编辑 DALL-E 生成的文本的必要性，以确保其在教育环境中的准确性和相关性。该方法还考虑了与生成内容的版权和所有权相关的法律问题，强调了该领域不断发展的法律格局。
研究发现，GAI（特别是 DALL-E）在为护理教育创建视觉叙事方面具有很大的潜力。虽然提供了成本效益和可及性，但 GAI 工具需要仔细考虑与文本相关的错误、对用户提示的误解和法律问题等挑战。
DALL-E 等 GAI 模型为增强护理教育中的视觉叙事提供了有希望的解决方案。然而，要有效地整合这些工具，教育工作者需要以共同飞行员的身份参与其中，利用它们的功能，同时减轻潜在的缺点。通过这样做，教育工作者可以利用 GAI 的全部潜力，通过引人入胜的视觉叙事丰富学习者的教育体验。
