研究学生用AI设计时如何判断其可靠性与责任归属。
Tracing the Invisible: Understanding Students' Judgment in AI-Supported Design Work
- 分析33个学生团队的反思，发现AI协作中出现新型判断类型。
- 提出代理权分配与可信度判断，反映学生对AI依赖的思考。
- 适合关注人机协同设计、教育AI应用的研究者与教师。
随着生成式AI融入设计流程，学生不再仅将其视为工具，更视作合作者。本研究通过对33个参与人机交互设计课程的学生团队进行反思分析，探讨他们在使用AI工具时所做出的设计判断。研究发现，除了既有的工具性、审美性和质量判断外，还涌现出两种新类型的判断：代理权分配判断与可信度判断。这些新判断体现了学生在创作责任与AI输出可靠性之间的权衡。研究揭示，生成式AI为设计推理引入了新复杂性，促使学生不仅关注AI产出的内容，更需思考何时以及如何依赖其结果。通过凸显这些判断机制，本文提供了一个理解学生在设计情境中与AI共同建构意义的概念框架。
原文摘要 · Abstract (English)
As generative AI tools become integrated into design workflows, students increasingly engage with these tools not just as aids, but as collaborators. This study analyzes reflections from 33 student teams in an HCI design course to examine the kinds of judgments students make when using AI tools. We found both established forms of design judgment (e.g., instrumental, appreciative, quality) and emergent types: agency-distribution judgment and reliability judgment. These new forms capture how students negotiate creative responsibility with AI and assess the trustworthiness of its outputs. Our findings suggest that generative AI introduces new layers of complexity into design reasoning, prompting students to reflect not only on what AI produces, but also on how and when to rely on it. By foregrounding these judgments, we offer a conceptual lens for understanding how students engage in co-creative sensemaking with AI in design contexts.
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