Using large language models (LLMs) for educational applications like
dialogue-based teaching is a hot topic. Effective teaching, however, requires
teachers to adapt the difficulty of content and explanations to the education
level of their students. Even the best LLMs today struggle to do this well. If
we want to improve LLMs on this adaptation task, we need to be able to measure
adaptation success reliably. However, current Static metrics for text
difficulty, like the Flesch-Kincaid Reading Ease score, are known to be crude
and brittle. We, therefore, introduce and evaluate a new set of Prompt-based
metrics for text difficulty. Based on a user study, we create Prompt-based
metrics as inputs for LLMs. They leverage LLM's general language understanding
capabilities to capture more abstract and complex features than Static metrics.
Regression experiments show that adding our Prompt-based metrics significantly
improves text difficulty classification over Static metrics alone. Our results
demonstrate the promise of using LLMs to evaluate text adaptation to different
education levels.

使用大型语言模型（LLMs）对教育应用，如基于对话的教学，进行研究是一个热门话题。本研究引入并评估了基于提示的文本难度度量，该度量方法相比静态度量具有更好的性能，并展示了利用 LLMs 评估文本对不同教育水平的适应的潜力。