@inproceedings{moraes-maua-2026-automated,
    title = "Automated Reformulation of Argumentative Essays to Improve Argument Organization and Development",
    author = "Moraes, Naomi James Sutcliffe de  and
      Mau{\'a}, Denis Deratani",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.42/",
    pages = "425--435",
    ISBN = "979-8-89176-387-6",
    abstract = "This work presents a study of automated reformulation of argumentative essays written by college-bound native speakers of Brazilian Portuguese as a form of pedagogical feedback. We first evaluate the feasibility of using large language models (LLMs) to score argument quality with respect to three criteria: the defense of a point of view, organization, and development. We then employ an LLM to provide a reformulated version of the essay as feedback. As we discuss, the main challenge is to constrain the automated feedback to address only argument quality, rather than improving other aspects such as spelling or cohesion, and to modify the essay as little as possible. We achieve levels of agreement in automatic essay scoring comparable to human inter-rater agreement metrics, while increasing explainability. Instructing the LLM to add argument support (facts, examples, etc.) was the best way to get non-superficial changes to the arguments, and it was able to add true examples and facts to the essays even without being provided with background information on the topic."
}