零样本大模型自动批改作业，无需训练即可给出个性化反馈。
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education
- 用提示工程实现零样本评估，不需额外训练即可批改计算与解释题。
- 学生反馈显示动机、理解力和准备度显著提升，优于传统评分方式。
- 适合需要大规模、个性化反馈的高校教学场景，提升学习体验。
自动化评分在教育科技中日益重要，可高效评估大量学生作业，提供一致且无偏见的评价，并即时反馈以促进学习。然而现有系统存在显著局限：少样本学习方法依赖大量数据，缺乏个性化与可操作性反馈，且过度关注基准性能而忽视学生体验。为此，我们提出一种基于零样本大语言模型（LLM）的自动作业评分（AAG）系统。该框架通过提示工程评估学生的计算与解释性作答，无需额外训练或微调。AAG系统提供针对性反馈，突出个体优势与改进空间，从而提升学习成效。研究通过全面评估验证其有效性，包括来自高等教育学生的调查反馈，结果显示相较于传统评分方式，学生在动机、理解力和准备度方面均有显著提升。结果表明，该系统有望通过优先关注学习体验，实现可扩展、高质量的教育评估变革。
原文摘要 · Abstract (English)
Automated grading has become an essential tool in education technology due to its ability to efficiently assess large volumes of student work, provide consistent and unbiased evaluations, and deliver immediate feedback to enhance learning. However, current systems face significant limitations, including the need for large datasets in few-shot learning methods, a lack of personalized and actionable feedback, and an overemphasis on benchmark performance rather than student experience. To address these challenges, we propose a Zero-Shot Large Language Model (LLM)-Based Automated Assignment Grading (AAG) system. This framework leverages prompt engineering to evaluate both computational and explanatory student responses without requiring additional training or fine-tuning. The AAG system delivers tailored feedback that highlights individual strengths and areas for improvement, thereby enhancing student learning outcomes. Our study demonstrates the system's effectiveness through comprehensive evaluations, including survey responses from higher education students that indicate significant improvements in motivation, understanding, and preparedness compared to traditional grading methods. The results validate the AAG system's potential to transform educational assessment by prioritizing learning experiences and providing scalable, high-quality feedback.
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