With their remarkable ability to generate code, large language models (LLMs)
are a transformative technology for computing education practice. They have
created an urgent need for educators to rethink pedagogical approaches and
teaching strategies for newly emerging skill sets. Traditional approaches to
learning programming have focused on frequent and repeated practice at writing
code. The ease with which code can now be generated has resulted in a shift in
focus towards reading, understanding and evaluating LLM-generated code. In
parallel with this shift, a new essential skill is emerging -- the ability to
construct good prompts for code-generating models. This paper introduces a
novel pedagogical concept known as a `Prompt Problem', designed to help
students learn how to craft effective prompts for LLMs. A Prompt Problem
challenges a student to create a natural language prompt that leads an LLM to
produce the correct code for a specific problem. To support the delivery of
Prompt Problems at scale, in this paper we also present a novel tool called
Promptly which hosts a repository of Prompt Problems and automates the
evaluation of prompt-generated code. We report empirical findings from a field
study in which Promptly was deployed in a first-year Python programming course
(n=54). We explore student interactions with the tool and their perceptions of
the Prompt Problem concept. We found that Promptly was largely well-received by
students for its ability to engage their computational thinking skills and
expose them to new programming constructs. We also discuss avenues for future
work, including variations on the design of Prompt Problems and the need to
study their integration into the curriculum and teaching practice.

通过引入 “Prompt Problem” 概念以及开发名为 Promptly 的工具，本文介绍了一种新的教学方法，可以帮助学生学习如何为大型语言模型构建有效的提示，通过一项实地研究发现，Promptly 大部分被学生们积极接受，并且能够激发他们的计算思维能力和接触新的编程结构，未来的研究方向包括 Prompt Problem 设计的改进以及将其整合进课程和教学实践中的研究。

迅速：使用引导问题教授学习者如何有效利用 AI 代码生成器

Promptly: Using Prompt Problems to Teach Learners How to Effectively  Utilize AI Code Generators

Reasoning about code and explaining its purpose are fundamental skills for
computer scientists. There has been extensive research in the field of
computing education on the relationship between a student's ability to explain
code and other skills such as writing and tracing code. In particular, the
ability to describe at a high-level of abstraction how code will behave over
all possible inputs correlates strongly with code writing skills. However,
developing the expertise to comprehend and explain code accurately and
succinctly is a challenge for many students. Existing pedagogical approaches
that scaffold the ability to explain code, such as producing exemplar code
explanations on demand, do not currently scale well to large classrooms. The
recent emergence of powerful large language models (LLMs) may offer a solution.
In this paper, we explore the potential of LLMs in generating explanations that
can serve as examples to scaffold students' ability to understand and explain
code. To evaluate LLM-created explanations, we compare them with explanations
created by students in a large course ($n \approx 1000$) with respect to
accuracy, understandability and length. We find that LLM-created explanations,
which can be produced automatically on demand, are rated as being significantly
easier to understand and more accurate summaries of code than student-created
explanations. We discuss the significance of this finding, and suggest how such
models can be incorporated into introductory programming education.

通过对大型语言模型进行评估，并将其与学生创建的代码解释进行比较，本文发现自动生成的 LLM 代码解释具有更高的准确性和易理解性，从而提供了一种解决教育中代码解释挑战的新方法。

比较由学生和大型语言模型创建的代码解释

Comparing Code Explanations Created by Students and Large Language  Models

Knowledge bases are employed in a variety of applications from natural
language processing to semantic web search; alas, in practice their usefulness
is hurt by their incompleteness. Embedding models attain state-of-the-art
accuracy in knowledge base completion, but their predictions are notoriously
hard to interpret. In this paper, we adapt "pedagogical approaches" (from the
literature on neural networks) so as to interpret embedding models by
extracting weighted Horn rules from them. We show how pedagogical approaches
have to be adapted to take upon the large-scale relational aspects of knowledge
bases and show experimentally their strengths and weaknesses.

本文探索了如何利用神经网络中的教育方法，从嵌入模型中提取加权 Horn 规则，以增强知识库的完整性和解释性。