调研在线研究生使用AI完成学术任务的偏好与真实体验差距。
Auditing Student-AI Collaboration: A Case Study of Online Graduate CS Students
- 通过两轮问卷，分析学生对AI在12类学术任务中的使用偏好。
- 发现学生希望保持自主权，担忧AI生成内容不可靠。
- 提出设计建议，帮助AI系统更符合教育场景的协作期待。
随着生成式AI融入高等教育，其日益影响学生完成学术任务的方式。尽管这些系统能提升效率并提供支持，但过度自动化、学生主体性减弱以及输出不可靠或虚构内容等问题仍引发关注。本研究采用混合方法，通过两轮互补的问卷调查，考察学生与AI协作时对自动化程度的期望与实际使用情况之间的匹配度。第一轮调查基于现有任务框架，评估学生在12类学术任务中对AI的使用偏好、实际使用情况、主要顾虑及使用原因。第二轮调查基于首轮结果，通过开放式问题探讨如何设计AI系统以缓解这些关切。研究旨在识别现有AI功能与学生对协作规范预期之间的差距，为构建更高效、可信赖的教育AI系统提供依据。
原文摘要 · Abstract (English)
As generative AI becomes embedded in higher education, it increasingly shapes how students complete academic tasks. While these systems offer efficiency and support, concerns persist regarding over-automation, diminished student agency, and the potential for unreliable or hallucinated outputs. This study conducts a mixed-methods audit of student-AI collaboration preferences by examining the alignment between current AI capabilities and students' desired levels of automation in academic work. Using two sequential and complementary surveys, we capture students' perceived benefits, risks, and preferred boundaries when using AI. The first survey employs an existing task-based framework to assess preferences for and actual usage of AI across 12 academic tasks, alongside primary concerns and reasons for use. The second survey, informed by the first, explores how AI systems could be designed to address these concerns through open-ended questions. This study aims to identify gaps between existing AI affordances and students' normative expectations of collaboration, informing the development of more effective and trustworthy AI systems for education.
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