医学教育中的快速工程

IF 1.6 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

International Journal of Medical Education Pub Date : 2023-08-31 DOI:10.3390/ime2030019

Thomas F. Heston, Charya Khun

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引用次数: 8

摘要

人工智能驱动的生成语言模型(glm)，如ChatGPT, Perplexity AI和Google Bard，有可能提供个性化学习，无限的练习机会，以及全天候的互动参与，并提供即时反馈。然而，要充分利用glm，适当制定的指令是必不可少的。即时工程是一种系统的方法，可以有效地与glm进行沟通，以达到预期的结果。精心设计的提示可以从GLM获得良好的响应，而构造不良的提示将导致不满意的响应。除了快速工程方面的挑战外，在医学教育中使用glm还存在重大问题，包括确保准确性、减轻偏见、维护隐私和避免过度依赖技术。未来的方向包括开发更复杂的提示工程技术，将glm与其他技术集成，创建个性化的学习途径，以及研究glm在医学教育中的有效性。

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Prompt Engineering in Medical Education

Artificial intelligence-powered generative language models (GLMs), such as ChatGPT, Perplexity AI, and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate feedback. However, to fully utilize GLMs, properly formulated instructions are essential. Prompt engineering is a systematic approach to effectively communicating with GLMs to achieve the desired results. Well-crafted prompts yield good responses from the GLM, while poorly constructed prompts will lead to unsatisfactory responses. Besides the challenges of prompt engineering, significant concerns are associated with using GLMs in medical education, including ensuring accuracy, mitigating bias, maintaining privacy, and avoiding excessive reliance on technology. Future directions involve developing more sophisticated prompt engineering techniques, integrating GLMs with other technologies, creating personalized learning pathways, and researching the effectiveness of GLMs in medical education.

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来源期刊

International Journal of Medical Education EDUCATION, SCIENTIFIC DISCIPLINES-

CiteScore

3.90

自引率

3.20%

发文量