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。