AiAWE: An Open-Source LLM Automated Writing Evaluation System Using LoRA-Adapted Instruction-Tuned Models

This study presents AiAWE, an open-source automated writing evaluation system that scores argumentative essays using a LoRA-adapted instruction-tuned large language model (Gemma-3-27B-it). Using a proprietary Educational Testing Service (ETS) dataset of 480 TOEFL Independent Writing essays, we fine-tune Gemma-3-27B and LLaMA-3.3-70B under identical LoRA configurations on a 120-essay training subset and evaluate on the remaining 360 essays under identical inference quantization. The fine-tuned Gemma model achieves a root mean square error of 0.474, a quadratic weighted kappa of 0.828, and an agreement rate of 90.56% within +/- 0.5 of the human score, outperforming both the larger LLaMA-3.3-70B model and the fine-tuned GPT-3.5 baseline reported in prior work on the same dataset. Three findings are of broader interest: open-weight LLMs can match or exceed proprietary fine-tuning for rubric-aligned scoring; model scale is not a reliable predictor of downstream performance under LoRA adaptation; and identical LoRA hyperparameters produce qualitatively different adaptation behaviors across architectures. The production system runs on a consumer-grade server and is publicly accessible at https://app.awade.gec.waseda.ac.jp. LoRA adapters, application code, and fine-tuning YAMLs are publicly available through their respective repositories.