构建师生模型三元协作系统，提升中小学写作教学效率与质量
Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale
- 设计三元协作机制，让模型生成、教师把关协同提升写作指导
- 覆盖120所学校5.8万篇作文，验证该系统显著改善写作质量
- 发现语言拓展过量会边际递减，需随学生水平动态调整协作
将大型语言模型（LLMs）融入基础教育写作教学面临双刃剑挑战，亟需建立模型、教师与学生之间的有效三元协作机制。本文开发了一套支持中小学写作学习的三元协作系统，并构建基于系统功能语言学和建议轨迹追踪的多维评估框架。研究基于两年间来自120所学校的10,195名学生撰写的57,954篇作文，形成大规模实证数据集。结果表明，该系统通过战略性分工显著提升写作质量：模型充当生成引擎缓解教师负担，教师则作为教学把关人与桥梁保障反馈质量。尽管模型与教师均对能力提升至关重要，但研究发现过度的语言扩展会产生边际效用递减的天花板效应，提示应根据学生熟练度动态调整模型与教师的协作方式。
原文摘要 · Abstract (English)
The double-edged sword of integrating Large Language Models (LLMs) requires an effective triadic collaboration mechanism among LLMs, teachers and students, especially for K-12 education. By developing a triadic collaboration system to support K-12 writing learning, a multidimensional evaluation framework grounded in Systemic Functional Linguistics and the suggestion trajectory tracing pipeline, this paper contributes a large-scale empirical dataset involving $57,954$ essays from $10,195$ students across $120$ schools over two years. Our findings confirm the efficacy of this system in improving writing quality through a strategic labor division: the LLM serves as a generative engine to mitigate teacher burnout, and the teacher acts as a pedagogical gatekeeper and bridge to guarantee feedback quality. While both LLM and teacher are critical for skill improvement, we uncover a ceiling effect where excessive linguistic expansion yields diminishing marginal utility. These suggest a dynamically adaptive LLM-teacher collaboration as student proficiency increases.
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