# WrAFT: a Modularized Automated Writing Evaluation System for Argumentative Essays

arXiv: https://arxiv.org/abs/2607.14524 · HTML: https://mlsanity.com/paper/2607.14524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation](https://mlsanity.com/paper/2404.15845/) (2024)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [Can Large Language Models Automatically Score Proficiency of Written Essays?](https://mlsanity.com/paper/2403.06149/) (2024)
- [GLEU Without Tuning](https://mlsanity.com/paper/1605.02592/) (2016)
- [Prompting open-source and commercial language models for grammatical error correction of English learner text](https://mlsanity.com/paper/2401.07702/) (2024)
- [Rationale Behind Essay Scores: Enhancing S-LLM's Multi-Trait Essay Scoring with Rationale Generated by LLMs](https://mlsanity.com/paper/2410.14202/) (2024)
- [Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency](https://mlsanity.com/paper/2402.15930/) (2024)
- [ReFF: Reinforcing Format Faithfulness in Language Models across Varied Tasks](https://mlsanity.com/paper/2412.09173/) (2024)

## Abstract (arXiv)

This study presents WrAFT, a Writing Assessment and Feedback Tool, that delivers both accurate and reliable scores and effective comprehensive feedback to argumentative essays. WrAFT adopts a modular design by dividing automated writing evaluation (AWE) tasks into scoring, surface-level feedback, and deep-level feedback. In building the system, various Large Language Models (LLMs) have been evaluated, including LLaMA-3.3-70B-Instruct, GPT-4o, and Claude 3.7, through both direct prompting and supervised fine-tuning approaches. A proprietary dataset of 480 TOEFL Independent Writing essays with official benchmark scores was utilized. Benchmark-based evaluation shows that WrAFT achieves state-of-the-art performance in scoring, with a quadratic weighted kappa (QWK) of 0.84 and a root mean square error (RMSE) of 0.44 against official scores on a scale of 0-5. Human evaluation of system-generated feedback also reveals high approval ratings: 96.14 percent for surface-level feedback, 93.03 percent for deep-level macro feedback, and 94.69 percent for deep-level micro feedback. An interactive user interface has been developed for the system and is publicly available and free to use.