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Receiving immediate and personalized feedback is crucial for second-languagelearners, and Automated Essay Scoring (AES) systems are a vital resource whenhuman instructors are unavailable. This study investigates the effectiveness ofLarge Language Models (LLMs), specifically GPT-4 and fine-tuned GPT-3.5, astools for AES. Our comprehensive set of experiments, conducted on both publicand private datasets, highlights the remarkable advantages of LLM-based AESsystems. They include superior accuracy, consistency, generalizability, andinterpretability, with fine-tuned GPT-3.5 surpassing traditional gradingmodels. Additionally, we undertake LLM-assisted human evaluation experimentsinvolving both novice and expert graders. One pivotal discovery is that LLMsnot only automate the grading process but also enhance the performance of humangraders. Novice graders when provided with feedback generated by LLMs, achievea level of accuracy on par with experts, while experts become more efficientand maintain greater consistency in their assessments. These results underscorethe potential of LLMs in educational technology, paving the way for effectivecollaboration between humans and AI, ultimately leading to transformativelearning experiences through AI-generated feedback.

From Automation to Augmentation: Large Language Models Elevating Essay Scoring Landscape