# eRevise+RF: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback

arXiv: https://arxiv.org/abs/2501.00715 · HTML: https://mlsanity.com/paper/2501.00715/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Understanding Iterative Revision from Human-Written Text](https://mlsanity.com/paper/2203.03802/) (2022)
- [ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews](https://mlsanity.com/paper/2306.12587/) (2023)
- [Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond](https://mlsanity.com/paper/2205.11484/) (2022)
- [NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge](https://mlsanity.com/paper/2206.07106/) (2022)
- [CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions](https://mlsanity.com/paper/2403.00241/) (2024)
- [Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing](https://mlsanity.com/paper/2107.06990/) (2021)
- [Predicting the Quality of Revisions in Argumentative Writing](https://mlsanity.com/paper/2306.00667/) (2023)
- [eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing](https://mlsanity.com/paper/1908.01992/) (2019)
- [Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring](https://mlsanity.com/paper/2008.01809/) (2020)

## Built on by (most cited first, 8 of 8)

- [AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation](https://mlsanity.com/paper/2504.07532/) (2025)
- [Position: LLMs Can be Good Tutors in English Education](https://mlsanity.com/paper/2502.05467/) (2025)
- [Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction](https://mlsanity.com/paper/2510.00268/) (2025)
- [PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback](https://mlsanity.com/paper/2606.20287/) (2026)
- [FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing](https://mlsanity.com/paper/2511.22883/) (2025)
- [Intention-Adaptive LLM Fine-Tuning for Text Revision Generation](https://mlsanity.com/paper/2602.00477/) (2026)
- [Qayyem: A Real-time Platform for Scoring Proficiency of Arabic Essays](https://mlsanity.com/paper/2603.01009/) (2026)
- [Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education](https://mlsanity.com/paper/2605.21614/) (2026)

## Abstract (arXiv)

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.