评估视觉语言和大语言模型用于印度尼西亚课堂自动学生评估
Evaluating Vision-Language and Large Language Models for Automated Student Assessment in Indonesian Classrooms
AI总结：
研究评估了视觉语言和大语言模型在印尼课堂中的自动学生评估效果，发现VLM在手写识别上存在困难，但LLM反馈仍具教学价值。
摘要：
尽管视觉语言和大语言模型（VLMs和LLMs）在技术上取得了快速进展，但它们在真实世界、代表性不足的课堂中用于AI驱动的教育评估的有效性仍鲜有研究。我们评估了最先进的VLMs和LLMs，使用来自印度尼西亚四年级超过14,000份手写答案，涵盖与当地国家课程对齐的数学和英语科目。与之前在干净数字文本上的工作不同，我们的数据集包含真实课堂中自然弯曲、多样化的手写体，带来了现实的视觉和语言挑战。评估任务包括评分和生成基于评分标准的个性化印尼语反馈。结果表明，VLM在手写识别上存在困难，导致LLM评分中的误差传播，但LLM反馈仍具教学价值，尽管视觉输入不完美，揭示了个性化和情境相关性的局限性。
英文摘要：
Despite rapid progress in vision-language and large language models (VLMs and LLMs), their effectiveness for AI-driven educational assessment in real-world, underrepresented classrooms remains largely unexplored. We evaluate state-of-the-art VLMs and LLMs on over 14K handwritten answers from grade-4 classrooms in Indonesia, covering Mathematics and English aligned with the local national curriculum. Unlike prior work on clean digital text, our dataset features naturally curly, diverse handwriting from real classrooms, posing realistic visual and linguistic challenges. Assessment tasks include grading and generating personalized Indonesian feedback guided by rubric-based evaluation. Results show that the VLM struggles with handwriting recognition, causing error propagation in LLM grading, yet LLM feedback remains pedagogically useful despite imperfect visual inputs, revealing limits in personalization and contextual relevance.
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