Large language models (LLMs) have recently reshaped Automated Essay Scoring (AES), yet prior studies typically examine individual techniques in isolation, limiting understanding of their relative merits for English as a Second Language (L2) writing. To bridge this gap, we presents a comprehensive comparison of major LLM-based AES paradigms on IELTS Writing Task~2. On this unified benchmark, we evaluate four approaches: (i) encoder-based classification fine-tuning, (ii) zero- and few-shot prompting, (iii) instruction tuning and Retrieval-Augmented Generation (RAG), and (iv) Supervised Fine-Tuning combined with Direct Preference Optimization (DPO) and RAG. Our results reveal clear accuracy-cost-robustness trade-offs across methods, the best configuration, integrating k-SFT and RAG, achieves the strongest overall results with F1-Score 93%. This study offers the first unified empirical comparison of modern LLM-based AES strategies for English L2, promising potential in auto-grading writing tasks. Code is public at https://github.com/MinhNguyenDS/LLM_AES-EnL2

翻译：大语言模型（LLMs）近期重塑了自动作文评分（AES）领域，然而先前研究通常孤立地考察单项技术，限制了对这些技术在英语作为第二语言（L2）写作评估中相对优势的理解。为弥补这一空白，本研究在雅思写作任务2上对主流基于LLM的AES范式进行了全面比较。在此统一基准上，我们评估了四种方法：（i）基于编码器的分类微调，（ii）零样本与少样本提示，（iii）指令微调与检索增强生成（RAG），以及（iv）监督微调结合直接偏好优化（DPO）与RAG。研究结果揭示了不同方法在准确率、成本与鲁棒性之间存在明确的权衡关系，其中结合k-SFT与RAG的最佳配置取得了最强的综合性能（F1分数达93%）。本研究首次为英语L2写作提供了现代基于LLM的AES策略的统一实证比较，展现了其在写作任务自动评分中的潜力。代码已公开于https://github.com/MinhNguyenDS/LLM_AES-EnL2