审查学生与AI协作：在线研究生计算机科学学生的案例研究
Auditing Student-AI Collaboration: A Case Study of Online Graduate CS Students
AI总结：
本研究通过混合方法审计，探讨学生与AI协作的偏好，分析现有AI能力与学生期望之间的差距，以改进教育用AI系统。
摘要：
随着生成式AI深入高等教育，它日益影响学生完成学术任务的方式。尽管这些系统提供效率和支持，但仍存在过度自动化、学生自主性降低以及不可靠或幻觉输出的担忧。本研究通过混合方法审计，考察学生与AI协作的偏好，分析现有AI能力与学生期望的匹配程度。通过两次连续的补充调查，我们捕捉学生在使用AI时感知的益处、风险和首选界限。第一项调查利用现有基于任务的框架，评估AI在12项学术任务中的偏好和实际使用情况，以及主要关注点和使用原因。第二项调查基于第一项，探索AI系统如何设计以解决这些关注点。本研究旨在识别现有AI功能与学生规范期望之间的差距，以指导更有效和可信的教育用AI系统开发。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
