@inproceedings{ding-etal-2025-feat,
    title = "{FEAT}-writing: An Interactive Training System for Argumentative Writing",
    author = "Ding, Yuning  and
      Wehrhahn, Franziska  and
      Horbach, Andrea",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven  and
      Mather, Brodie  and
      Dras, Mark",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-demos.22/",
    pages = "217--225",
    abstract = "Recent developments in Natural Language Processing (NLP) for argument mining offer new opportunities to analyze the argumentative units (AUs) in student essays. These advancements can be leveraged to provide automatically generated feedback and exercises for students engaging in online argumentative essay writing practice. Writing standards for both native English speakers (L1) and English-as-a-foreign-language (L2) learners require students to understand formal essay structures and different AUs. To address this need, we developed FEAT-writing (Feedback and Exercises for Argumentative Training in writing), an interactive system that provides students with automatically generated exercises and distinct feedback on their argumentative writing. In a preliminary evaluation involving 346 students, we assessed the impact of six different automated feedback types on essay quality, with results showing general improvements in writing after receiving feedback from the system."
}