  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.
