Worked examples (solutions to typical programming problems presented as a
source code in a certain language and are used to explain the topics from a
programming class) are among the most popular types of learning content in
programming classes. Most approaches and tools for presenting these examples to
students are based on line-by-line explanations of the example code. However,
instructors rarely have time to provide line-by-line explanations for a large
number of examples typically used in a programming class. In this paper, we
explore and assess a human-AI collaboration approach to authoring worked
examples for Java programming. We introduce an authoring system for creating
Java worked examples that generates a starting version of code explanations and
presents it to the instructor to edit if necessary.We also present a study that
assesses the quality of explanations created with this approach

介绍了一种用于创建 Java 编程的实例的制作系统，通过人工智能与人类教师的合作，以生成初始版本的代码解释，并根据需要进行编辑。还进行了一项研究，评估了使用该方法创建的解释的质量。

人工智能与人类共同创造的编程课程范例

Human-AI Co-Creation of Worked Examples for Programming Classes

Successful content moderation in online platforms relies on a human-AI
collaboration approach. A typical heuristic estimates the expected harmfulness
of a post and uses fixed thresholds to decide whether to remove it and whether
to send it for human review. This disregards the prediction uncertainty, the
time-varying element of human review capacity and post arrivals, and the
selective sampling in the dataset (humans only review posts filtered by the
admission algorithm).
In this paper, we introduce a model to capture the human-AI interplay in
content moderation. The algorithm observes contextual information for incoming
posts, makes classification and admission decisions, and schedules posts for
human review. Only admitted posts receive human reviews on their harmfulness.
These reviews help educate the machine-learning algorithms but are delayed due
to congestion in the human review system. The classical learning-theoretic way
to capture this human-AI interplay is via the framework of learning to defer,
where the algorithm has the option to defer a classification task to humans for
a fixed cost and immediately receive feedback. Our model contributes to this
literature by introducing congestion in the human review system. Moreover,
unlike work on online learning with delayed feedback where the delay in the
feedback is exogenous to the algorithm's decisions, the delay in our model is
endogenous to both the admission and the scheduling decisions.
We propose a near-optimal learning algorithm that carefully balances the
classification loss from a selectively sampled dataset, the idiosyncratic loss
of non-reviewed posts, and the delay loss of having congestion in the human
review system. To the best of our knowledge, this is the first result for
online learning in contextual queueing systems and hence our analytical
framework may be of independent interest.

在线平台上成功的内容管理依赖于人工智能协作方法，这篇论文介绍了一种模型来捕捉内容管理中的人工智能相互作用，提出了一种近乎最优的学习算法来平衡选择性采样数据集的分类损失、未审核帖子的独特损失和人工审核系统的延迟损失。