@inproceedings{wang-etal-2026-double,
    title = "Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic {LLM}-Teacher Collaboration for K-12 Writing at Scale",
    author = "Wang, Canran  and
      Yang, Yuwen  and
      Wang, Zhen  and
      MA, Ming  and
      Yu, Ding  and
      Wang, Chentai  and
      Huang, Keman  and
      Du, Xiaoyong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.597/",
    pages = "12291--12312",
    ISBN = "979-8-89176-395-1",
    abstract = "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."
}