Reflective writing is known to support the development of students' metacognitive skills, yet learners often struggle to engage in deep reflection, limiting learning gains. Although large language models (LLMs) have been shown to improve writing skills, their use as conversational agents for reflective writing has produced mixed results and has largely focused on providing feedback on reflective texts, rather than support during planning and organizing. In this paper, inspired by the Cognitive Process Theory of writing (CPT), we propose the first application of LLMs to the planning and translation steps of reflective writing. We introduce Pensée, a tool to explore the effects of explicit AI support during these stages by scaffolding structured reflection planning using a conversational agent, and supporting translation by automatically extracting key concepts. We evaluate Pensée in a controlled between-subjects experiment (N=93), manipulating AI support across writing phases. Results show significantly greater reflection depth and structural quality when learners receive support during planning and translation stages of CPT, though these effects reduce in a delayed post-test. Analyses of learner behavior and perceptions further illustrate how CPT-aligned conversational support shapes reflection processes and learner experience, contributing empirical evidence for theory-driven uses of LLMs in AI-supported reflective writing.

翻译：反思性写作已知有助于培养学生的元认知技能，但学习者往往难以进行深度反思，从而制约了学习效果。尽管大语言模型已被证明能提升写作技能，但其作为反思性写作对话代理的应用效果参差不齐，且主要集中于提供反思文本反馈，而非在规划与组织阶段提供支持。本文受写作认知过程理论启发，首次提出将大语言模型应用于反思性写作的规划与翻译阶段。我们开发了Pensée工具，通过对话代理构建结构化反思规划支架，并自动提取关键概念支持翻译，以探索显式AI支持在这些阶段的作用。在受控组间实验（N=93）中，我们操纵不同写作阶段的AI支持进行评估。结果表明，当学习者在CPT的规划与翻译阶段获得支持时，其反思深度和结构质量显著提升，但延迟后测中这些效果有所减弱。对学习者行为与感知的分析进一步揭示了CPT对齐的对话支持如何塑造反思过程与学习体验，为基于理论的大语言模型应用于AI辅助反思性写作提供了实证依据。