三思而后行：书面理由能否减少对AI写作建议的不加批判接受？
Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions?
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中文总结 AI 辅助
本研究通过随机实验（N=129）发现，要求学生在接受或拒绝AI写作建议时提供书面理由，可将采纳有缺陷建议的比例从65%降至41%，且不影响对合理建议的接受，但存在浅层参与问题。
中文摘要 AI 辅助
生成式AI可以为学生提供有用的反馈，但其价值取决于判断哪些建议是准确且相关的。先前研究表明，在人机交互过程中引入策略性摩擦可以促进批判性接受，但如何在学术情境中有效实施这种摩擦仍不清楚。我们考察了要求学生为其接受或拒绝AI建议的决定提供理由，是否能减少学术写作中的不加批判接受。在一项嵌入课程活动的随机实验中（N=129），学生撰写数据分析提案，收到质量参差不齐的AI修订建议，并决定接受或拒绝这些建议。被要求提供书面理由的学生采纳有缺陷建议的可能性降低了24个百分点（65%对41%），而对合理建议的接受率没有下降（81%对86%）。然而，主题分析揭示了理由任务中的浅层参与，以及元认知监控和领域知识方面的不足。
英文摘要
Generative AI can offer students useful feedback, but its value depends on judging which suggestions are accurate and relevant. Prior research shows that strategic friction during human-AI interactions can promote critical uptake, but how to effectively implement such friction in academic contexts remains unclear. We examine whether requiring students to justify decisions to accept or reject AI suggestions can mitigate uncritical uptake in academic writing. In a randomized experiment embedded in a course activity (N=129), students wrote a data analysis proposal, received mixed-quality AI revision suggestions, and decided whether to accept or reject them. Students required to provide written justifications were 24 percentage points less likely to adopt flawed suggestions (65% vs. 41%), with no reduction in acceptance of sound suggestions (81% vs. 86%). However, thematic analysis revealed superficial engagement in the justification task and gaps in metacognitive monitoring and domain knowledge.
发表机构
- Cornell University（康奈尔大学）
- University of Vienna（维也纳大学）
机构由 AI 辅助整理，请以论文原文为准。
