Recently, the potential of large language models (LLMs) has been widely used
in assisting programming. However, current research does not explore the artist
potential of LLMs in creative coding within artist and AI collaboration. Our
work probes the reflection type of artists in the creation process with such
collaboration. We compare two common collaboration approaches: invoking the
entire program and multiple subtasks. Our findings exhibit artists' different
stimulated reflections in two different methods. Our finding also shows the
correlation of reflection type with user performance, user satisfaction, and
subjective experience in two collaborations through conducting two methods,
including experimental data and qualitative interviews. In this sense, our work
reveals the artistic potential of LLM in creative coding. Meanwhile, we provide
a critical lens of human-AI collaboration from the artists' perspective and
expound design suggestions for future work of AI-assisted creative tasks.

大型语言模型的艺术潜力在创意编码中的艺术家与人工智能协作中被广泛利用，我们的研究揭示艺术家创作过程中这种协作方式中的反思类型，比较了整个程序和多个子任务两种常见的协作方式，并通过实验数据和定性访谈展示了艺术家在两种不同方法中的不同反思激发，并显示了反思类型与用户表现、用户满意度和主观体验的相关性。同时，我们从艺术家的视角提供了人工智能协作的关键观点，并为未来 AI 辅助创意任务的设计提供了建议。