从教与学的学术研究角度出发审视人工智能诚信应用的建议
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https://doi.org/10.30827/relieve.v29i2.29295关键词:
人工智能, 生成式人工智能(GenAI), 大语言模型, 学术诚信, 教与学的学术研究, 系统方法摘要
人工智能(AI)领域的新发展促进了生成式人工智能(GenAI)的出现,这种新技术可以创造出与人类能力相似的文本。针对这一现象,高等教育阶段的教育者们反应迅速地表达了他们的担忧。众多的专家和组织通过建议的方式展示他们的忧虑。因此,在这篇理论性论文中,我们通过“基于教与学学术研究的学术诚信模型“来审视十一篇关于生成式人工智能诚信应用的文章,并在此基础上进行相应的讨论。我们识别出适用于个人(微观)、部门/项目(中观)、机构(宏观)、机构间/国家/国际(巨型)层面的建议,其中建议涉及模型的两个核心元素:具有深远影响力的个人及小组专业化学习、地方级别领导力和微观文化。在微观和中观层面缺少关于“学术、研究及调查”的核心元素建议。而在中观、宏观和巨型层面欠缺关于“学习空间、教学法及技术”的核心元素建议。我们发现这些建议都聚焦于学习,且涉及多个利益相关者,并不仅仅限于学生的行为,这与现在的学术诚信方法一致。然而我们也发现仍有差异需要更深入的研究和探索。在与学术诚信相关的生成式人工智能问题上,我们坚信应该为教育利益相关者们提供更具体更实用的指导和资源。我们也坚持提倡为高等教育网络和领导者提供更好的支持,为符合道德规范地使用生成式人工智能创建条件,强调对平等、包容及多样化的生成式人工智能的需求。
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