Prompt Engineering for Nurse Educators.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2024-11-01 Epub Date: 2024-07-05 DOI:10.1097/NNE.0000000000001705

Grace H Sun

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引用次数: 0

Abstract

Background: The integration of generative artificial intelligence (AI) tools like OpenAI's ChatGPT into nursing education marks a transformative advance in personalized learning and interactive engagement.

Problem: Variability in faculty's experience with AI outputs highlights the need for well-crafted prompts that align with educational objectives, maximize learning outcomes, and ensure contextual relevance. Effective prompting is a key to eliciting accurate, relevant responses from AI, fostering a dynamic learning environment that bolsters student comprehension of complex topics.

Approach: This article examines the critical role of prompt engineering in optimizing AI-generated content's effectiveness within academic settings. With a detailed guide and strategies specifically designed for nursing education, the article prepares faculty to proficiently use generative AI.

Conclusions: By mastering prompt engineering, educators can leverage AI tools as powerful aids, potentially significantly enhancing teaching effectiveness, work efficiency, and student learning outcomes.

查看原文本刊更多论文

护士教育工作者的即时工程。

背景：问题：教师对人工智能输出结果的体验不尽相同，这突出表明需要精心设计与教学目标相一致的提示，最大限度地提高学习效果，并确保上下文的相关性。有效的提示是人工智能做出准确、相关反应的关键所在，它可以营造一个动态的学习环境，促进学生对复杂主题的理解：本文探讨了提示工程在优化学术环境中人工智能生成内容的有效性方面所起的关键作用。文章通过专门为护理教育设计的详细指南和策略，帮助教师熟练使用生成式人工智能：通过掌握提示工程，教育工作者可以利用人工智能工具作为强大的辅助工具，从而显著提高教学效果、工作效率和学生学习成果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464