@inproceedings{liu-etal-2025-erevise,
    title = "e{R}evise+{RF}: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback",
    author = "Liu, Zhexiong  and
      Litman, Diane  and
      Wang, Elaine L  and
      Li, Tianwen  and
      Gobat, Mason  and
      Matsumura, Lindsay Clare  and
      Correnti, Richard",
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-demo.18/",
    pages = "173--190",
    ISBN = "979-8-89176-191-9",
    abstract = "The ability to revise essays in response to feedback is important for students' writing success. An automated writing evaluation (AWE) system that supports students in revising their essays is thus essential. We present eRevise+RF, an enhanced AWE system for assessing student essay revisions (e.g., changes made to an essay to improve its quality in response to essay feedback) and providing revision feedback. We deployed the system with 6 teachers and 406 students across 3 schools in Pennsylvania and Louisiana. The results confirmed its effectiveness in (1) assessing student essays in terms of evidence usage, (2) extracting evidence and reasoning revisions across essays, and (3) determining revision success in responding to feedback. The evaluation also suggested eRevise+RF is a helpful system for young students to improve their argumentative writing skills through revision and formative feedback."
}