With recent advances in generative AI, conversational models like ChatGPT
have become feasible candidates for TAs. We investigate the practicality of
using generative AI as TAs in introductory programming education by examining
novice learners' interaction with TAs in a subgoal learning environment. To
compare the learners' interaction and perception of the AI and human TAs, we
conducted a between-subject study with 20 novice programming learners. Learners
solve programming tasks by producing subgoals and subsolutions with the
guidance of a TA. Our study shows that learners can solve tasks faster with
comparable scores with AI TAs. Learners' perception of the AI TA is on par with
that of human TAs in terms of speed and comprehensiveness of the replies and
helpfulness, difficulty, and satisfaction of the conversation. Finally, we
suggest guidelines to better design and utilize generative AI as TAs in
programming education from the result of our chat log analysis.

最近 AI 生成模型的进展使得像 ChatGPT 这样的对话模型成为可行的助教候选人。我们通过研究初学者与助教在子目标学习环境中的互动，来探讨在初级编程教育中使用生成 AI 作为助教的实际性。为了比较学习者与 AI 和人类助教的互动和感知，我们进行了一项针对 20 名初级编程学习者的受试研究。学习者通过在助教的指导下制定子目标和子解决方案来解决编程任务。我们的研究表明，学习者在比分和 AI 助教相当的情况下能够更快地解决任务。学习者对 AI 助教的感知在回复的速度和全面性、对话的有益性、困难度和满意度方面与人类助教相当。最后，我们通过对聊天记录的分析，提出了更好地设计和利用生成 AI 作为编程教育助教的指导原则。

借鉴助教的分阶段学习编程：探索 AI 助教的潜力

Learning from Teaching Assistants to Program with Subgoals: Exploring  the Potential for AI Teaching Assistants

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。