结构化的人类-LLM交互设计揭示了高等教育内容生成中的探索和利用动态
Pablo Flores Romero1, Kin Nok Nicholas Fung2, Guang Rong2
1Faculty of Educational Sciences, University of Helsinki, Helsinki, Finland. pablo.flores@helsinki.fi.
NPJ science of learning
|June 18, 2025
概括
大型语言模型 (LLM) 彻底改变了教育中的信息获取. 社会线索和计算思维影响着人工智能产生的内容的探索和利用.
科学领域:
- 教育中的人工智能
- 人与计算机的交互
- 信息科学 信息科学 信息科学
背景情况:
- 大型语言模型 (LLM) 为研究信息采集提供了新的方法.
- 教学内容创建是LLMs在教育中的一个关键应用领域.
- 了解用户如何与LLM进行信息搜索的交互至关重要.
研究的目的:
- 研究LLM技术如何影响教学内容创作中的信息食行为.
- 检查计算思维技能在塑造LLM驱动的食中的作用.
- 探索结构化提示符制作对用户行为的影响.
主要方法:
- 一项涉及25名博士生参加教育人工智能课程的研究.
- 利用可编辑的提示模板和社会来源的关键字来指导提示制作.
- 进行计算思维调查,以评估参与者的技能和特征.
主要成果:
- LLM使用影响参与者进行探索 (产生新信息) 和利用 (专注于特定内容).
- 社交线索增强了对各种信息的探索,而利用则缩小了对AI内容的关注.
- 计算思维特征,如合作和批判性思维,影响了内容利用和依赖个人利益.
结论:
- 可以设计LLM驱动的教育工具,以促进广泛的信息探索和专注的内容开发.
- 计算思维技能在调解用户与LLM的互动中发挥着重要作用,用于教育目的.
- 这些发现对未来人工智能驱动的教育技术的设计和实施有影响.
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