AI 中文总结
本文针对学生使用LLM写作时脱离的问题，提出结构层面解释，指出当前界面引发程序崩溃，给出分解式交互等设计方向以支持AI辅助写作。
AI 中文摘要
当学生使用大语言模型（LLM）进行写作时，对其脱离（不执行）的主流解释是性格层面的：他们过度依赖模型，补救措施是搭建自我调节的支架。本文认为需要一种结构层面的解释，为设计干预提供替代基础，以支持适当的AI辅助写作。当前的LLM写作界面会引发程序崩溃：将迭代式、自定进度的写作过程替换为单一输出，使写作者的任务从生成转变为全面评估。由于这种评估成本高昂，浅层参与成为默认状态，写作本应产生的认知工作无法完成。该框架指出了减轻写作者自我调节负担的设计方向，包括分解式交互、将目标 elicitation（目标 elicitation 可译为“目标引出”）作为默认第一步、以及单层级输出，这些措施通过重构交互本身来补充元认知支架。
英文摘要
When students use large language models for writing, the dominant explanation for disengagement is dispositional: they are over-reliant, and the remedy is to scaffold self-regulation. We argue that a structural explanation is needed, offering an alternative basis for design interventions to support appropriate AI-assisted writing. Current LLM writing interfaces induce procedural collapse: the replacement of an iterative, self-paced writing process with a single output that shifts the writer's task from generation to comprehensive evaluation. Because that evaluation is costly, shallow engagement becomes the default, and the cognitive work writing was supposed to produce goes unperformed. The framework points toward design directions that reduce the burden on writers to self-regulate, including decomposed interaction, goal elicitation as a default first step, and single-level output. They complement metacognitive scaffolding by restructuring the interaction itself.
