This paper looks at the ability of large language models to participate in
educational guided reading. We specifically, evaluate their ability to generate
meaningful questions from the input text, generate diverse questions both in
terms of content coverage and difficulty of the questions and evaluate their
ability to recommend part of the text that a student should re-read based on
the student's responses to the questions. Based on our evaluation of ChatGPT
and Bard, we report that,
1) Large language models are able to generate high quality meaningful
questions that have high correlation with the input text, 2) They generate
diverse question that cover most topics in the input text even though this
ability is significantly degraded as the input text increases, 3)The large
language models are able to generate both low and high cognitive questions even
though they are significantly biased toward low cognitive question, 4) They are
able to effectively summarize responses and extract a portion of text that
should be re-read.

本篇研究探讨了大型语言模型在参与教育指导阅读方面的能力，着重评估它们从输入文本中生成有意义问题和多样化问题的能力，以及根据学生对问题的回答生成要求他们重新阅读一部分文本的能力。通过我们对 ChatGPT 和 Bard 的评估，我们得出结论：1）大型语言模型能够生成高质量的有意义问题，与输入文本有很高的相关性，2）它们生成覆盖输入文本大多数主题的多样化问题，尽管这种能力会随着输入文本的增加而明显降低，3）大型语言模型能够产生低和高认知问题，尽管它们明显偏向于低认知问题，4）它们能够有效地总结回答并提取应重新阅读的部分文本。