This paper presents reports on a series of experiments with a novel dataset
evaluating how well Large Language Models (LLMs) can mark (i.e. grade) open
text responses to short answer questions, Specifically, we explore how well
different combinations of GPT version and prompt engineering strategies
performed at marking real student answers to short answer across different
domain areas (Science and History) and grade-levels (spanning ages 5-16) using
a new, never-used-before dataset from Carousel, a quizzing platform. We found
that GPT-4, with basic few-shot prompting performed well (Kappa, 0.70) and,
importantly, very close to human-level performance (0.75). This research builds
on prior findings that GPT-4 could reliably score short answer reading
comprehension questions at a performance-level very close to that of expert
human raters. The proximity to human-level performance, across a variety of
subjects and grade levels suggests that LLMs could be a valuable tool for
supporting low-stakes formative assessment tasks in K-12 education and has
important implications for real-world education delivery.

这篇论文讨论了使用大型语言模型（LLMs）对开放文本短答案问题进行评分的实验，研究了不同组合的 GPT 版本和提示工程策略在标记真实学生答案时的性能表现，并发现 GPT-4 在这方面表现良好与人类级别接近。这一研究对于支持 K-12 教育中的低风险形成性评估任务具有重要意义。