FEAT-writing: An Interactive Training System for Argumentative Writing
Yuning Ding, Franziska Wehrhahn, Andrea Horbach
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.
BibTeX
@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://aclanthology.org/2025.coling-demos.22/",
pages = "217--225"
}