arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2603.28596cs.HCcs.AIcs.CL

超越评审:将语言模型应用于反思中的规划与翻译

Moving Beyond Review: Applying Language Models to Planning and Translation in Reflection

EPFL(瑞士联邦理工学院洛桑);KTH Royal Institute of Technology(瑞典皇家理工学院)

Seyed Parsa Neshaei, Richard Lee Davis, Tanja Käser

AI总结:

本文提出利用语言模型支持反思写作的规划与翻译阶段,通过实验发现AI支持能提升反思深度和结构质量,但效果在延迟测试中减弱。

摘要:

反思写作被证实能促进学生元认知技能的发展,但学习者常难以深入反思,限制了学习成效。尽管大型语言模型(LLMs)已被证明能提升写作能力,但其作为对话代理用于反思写作的效果不一,主要集中在对反思文本的反馈,而非规划和组织阶段。本文受写作认知过程理论(CPT)启发,首次将LLMs应用于反思写作的规划与翻译步骤。我们介绍了Pensée,一种探索显式AI支持在这些阶段影响的工具,通过对话代理搭建结构化反思规划,并通过自动提取关键概念支持翻译。在受控的组间实验(N=93)中,我们操纵AI支持在写作阶段的不同程度。结果表明,当学习者在规划和翻译阶段获得支持时,反思深度和结构质量显著提高,但这些效果在延迟后测中有所减弱。学习者行为和感知的分析进一步说明了CPT对齐的对话支持如何塑造反思过程和学习者体验,为LLMs在AI支持下的反思写作中的理论驱动应用提供了实证依据。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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