探索GitHub Copilot对健康信息学教育的影响
Sanja Avramovic1, Ivan Avramovic2, Janusz Wojtusiak1
1Deprtment of Health Administration and Policy, College of Public Health, George Mason University, Fairfax, Virginia, United States.
Applied clinical informatics
|September 13, 2024
概括
GitHub Copilot协助医疗信息学编程任务,在简单的问题上表现良好,但在复杂的问题上扎. 详细的解释提高了它在具有挑战性的编码任务中的有效性.
科学领域:
- 医疗信息学 医疗信息学
- 计算机科学教育计算机科学教育
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 驱动的代码完成工具,如与Visual Studio集成的GitHub Copilot,对健康信息学教育产生了重大影响.
- 这些工具对于在这个领域学习SQL和Python等编程语言的学生来说尤为重要.
研究的目的:
- 评估AI代码完成工具在解决健康信息学课程问题的有效性.
- 评估GitHub Copilot在各种问题复杂性的表现,从简单到高级.
主要方法:
- 评估了GitHub Copilot的代码生成能力,用于介绍健康信息学编程课程的问题.
- 根据提供的问题陈述评估生成代码的正确性.
- 研究了详细解释对工具解决问题的有效性的影响.
主要成果:
- GitHub Copilot成功地为基本编程问题生成了正确的代码.
- 代码的正确性和有效性随着问题的复杂性增加而下降.
- 虽然该工具在处理高度复杂的问题时遇到了困难,但通过更详细的输入解释,性能得到了改善.
结论:
- 人工智能代码完成工具与健康信息学编程教育相关,但需要学生进行批判性评估.
- 教师必须适应人工智能编程辅助工具的快速发展.
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