Generative AI (GenAI) tools improve productivity in knowledge workflows such as writing, but also risk overreliance and reduced critical thinking. Cognitive forcing functions (CFFs) mitigate these risks by requiring active engagement with AI output. As GenAI workflows grow more complex, systems increasingly present execution plans for user review. However, these plans are themselves AI-generated and prone to overreliance, and the effectiveness of applying CFFs to AI plans remains underexplored. We conduct a controlled experiment in which participants completed AI-assisted writing tasks while reviewing AI-generated plans under four CFF conditions: Assumption (argument analysis), WhatIf (hypothesis testing), Both, and a no-CFF control. A follow-up think-aloud and interview study qualitatively compared these conditions. Results show that the Assumption CFF most effectively reduced overreliance without increasing cognitive load, while participants perceived the WhatIf CFF as most helpful. These findings highlight the value of plan-focused CFFs for supporting critical reflection in GenAI-assisted knowledge work.

翻译：生成式人工智能（GenAI）工具提升了写作等知识工作流程的生产力，但也存在过度依赖和批判性思维减弱的风险。认知强制函数（CFFs）通过要求用户主动参与AI输出处理来缓解这些风险。随着GenAI工作流程日益复杂，系统越来越多地提供执行计划供用户审阅。然而，这些计划本身由AI生成，同样容易引发过度依赖，且将CFFs应用于AI计划的有效性仍缺乏深入探索。我们开展了一项对照实验：参与者在完成AI辅助写作任务时，需在四种CFF条件下审阅AI生成的计划——假设分析（论证分析型）、情景推演（假设检验型）、混合型以及无CFF对照组。后续的出声思维与访谈研究对这些条件进行了定性比较。结果表明：假设分析型CFF在未增加认知负荷的情况下最能有效降低过度依赖，而参与者认为情景推演型CFF最具助益。这些发现凸显了以计划为核心的认知强制函数在支持GenAI辅助知识工作中开展批判性反思的重要价值。