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This paper introduces a triadic collaboration system involving LLMs, teachers, and students for K-12 writing instruction, and evaluates its efficacy using a large-scale dataset of 57,954 essays. The study finds that strategic division of labor, with LLMs generating content and teachers providing pedagogical oversight, improves writing quality. However, the research also identifies a ceiling effect where excessive LLM-driven linguistic expansion yields diminishing returns, suggesting the need for adaptive collaboration strategies.
LLMs can boost K-12 writing skills, but over-reliance on AI feedback hits a ceiling, revealing the sweet spot for human-AI collaboration in education.
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.
