超越边界:在研究生,在线科学课程中探索生成人工智能作业.
Rohini Ganjoo1, James Rankin1, Benjamin Lee1
1The George Washington University, Ashburn, Virginia, USA.
Journal of microbiology & biology education
|November 4, 2024
概括
研究生医学实验室科学课程中的生成人工智能 (GAI) 显示了高学习成果的实现. 学生们发现GAI有用,但对准确性和学术诚信提出了担忧.
科学领域:
- 医学 实验室科学 教育 医学 实验室科学 教育
- 教育中的人工智能
- 数字教学数字教学.
背景情况:
- 生成型人工智能 (GAI) 为可访问和个性化的学习提供了机会.
- 有关GAI在学术界存在不准确性,偏见和不道德应用的潜力存在担忧.
- 将GAI整合到教育环境中需要仔细考虑其好处和局限性.
研究的目的:
- 为了评估一个新的研究论文任务,要求研究生利用GAI.
- 评估与GAI整合和批判性评估相关的学生学习成果.
- 探索学生对GAI实用性的看法,对学习的影响以及道德考虑.
主要方法:
- 在三个在线,异步的医学实验室科学研究生课程中实施了GAI集成的研究论文任务.
- 通过评估 GAI 内容整合,与同行评审来源进行批判性评估和合成技能的标题来评估学生的学习.
- 管理一项16项调查,收集关于GAI利用,感知学习贡献和道德问题的数据 (n=32).
主要成果:
- 高整体标题分数表明学习成果的成功实现.
- 93.8%的学生将GAI产生的内容评为"非常好"或"优秀",但28.1%的学生认为内容不准确.
- 68.8%的人同意GAI应该成为授权的学术工具,但只有28.1%的人支持在没有指导员明确许可的情况下使用它.
结论:
- 开发的任务有效地促进了研究生医学实验室科学课程中GAI生成的内容的学习和批判性评估.
- 学生们认为GAI是一种提高效率和理解的工具,但对学术完整性和引用实践表示严重担忧.
- 教育学生和教职员工关于GAI的伦理使用和固有的局限性,对于负责任地融入高等教育至关重要.
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