Imagine a world where grading papers is instantaneous, freeing up educators to focus on what truly matters: teaching. This isn't science fiction; it's the potential of Large Language Models (LLMs) like those powering ChatGPT. Recent research delves into how these powerful AI tools can be used for automatic scoring and feedback generation, potentially revolutionizing how we assess learning. One of the biggest hurdles with LLMs is their massive size and computational hunger. Fine-tuning them for specific tasks like grading requires significant resources. However, this research explores clever techniques like quantization, which slims down these models, making them run faster and more efficiently. Specifically, they used a method called 4-bit quantization on the LLaMA-2 model. Think of it like compressing a large image file; you reduce the size without losing too much detail. The results are impressive. These quantized LLMs achieved remarkably low error rates when predicting grades, even outperforming existing methods. They were also tested on generating feedback, and again, the quantized LLaMA-2 models shone, producing feedback comparable to human graders. The researchers even found that giving the LLM the predicted grade alongside the student's answer improved the quality of the feedback even further. This research opens exciting doors for the future of education. By automating time-consuming tasks like grading, educators can dedicate more time to personalized instruction and student interaction. While challenges remain, such as exploring the effectiveness of even larger LLMs and different quantization levels, this study demonstrates the potential of AI to transform how we assess and provide feedback on student learning.