As an increasing number of students move to online learning platforms that
deliver personalized learning experiences, there is a great need for the
production of high-quality educational content. Large language models (LLMs)
appear to offer a promising solution to the rapid creation of learning
materials at scale, reducing the burden on instructors. In this study, we
investigated the potential for LLMs to produce learning resources in an
introductory programming context, by comparing the quality of the resources
generated by an LLM with those created by students as part of a learnersourcing
activity. Using a blind evaluation, students rated the correctness and
helpfulness of resources generated by AI and their peers, after both were
initially provided with identical exemplars. Our results show that the quality
of AI-generated resources, as perceived by students, is equivalent to the
quality of resources generated by their peers. This suggests that AI-generated
resources may serve as viable supplementary material in certain contexts.
Resources generated by LLMs tend to closely mirror the given exemplars, whereas
student-generated resources exhibit greater variety in terms of content length
and specific syntax features used. The study highlights the need for further
research exploring different types of learning resources and a broader range of
subject areas, and understanding the long-term impact of AI-generated resources
on learning outcomes.

本研究探讨了大语言模型（LLMs）生成学习资源的潜力，结果显示其生成的资源质量等同于同学们生成的资源，并可能成为特定背景下的补充教材。

人类与机器：学生生成与人工智能生成的教育内容比较

Human vs Machine: Comparison of Student-generated and AI-generated  Educational Content

Here, we provide a supplementary material for Takayuki Osogami, "Uncorrected
least-squares temporal difference with lambda-return," which appears in {\it
Proceedings of the 34th AAAI Conference on Artificial Intelligence} (AAAI-20).

本文提供了 Takayuki Osogami 关于 “未经校正的最小二乘时间差分与 lambda 回报” 的补充材料，发表在第 34 届 AAAI 人工智能会议上。