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.

翻译：近期，大语言模型（LLMs）在辅助编程方面的潜力已被广泛应用。然而，当前研究并未深入探讨在艺术家与AI协作的创意编程中，LLMs所蕴含的艺术家潜能。本研究探究了艺术家在此类协作创作过程中的反思类型。我们比较了两种常见的协作方式：调用完整程序与执行多个子任务。研究结果表明，艺术家在这两种不同方式中激发了不同类型的反思。此外，通过实验数据与定性访谈两种方法，我们的发现还揭示了两种协作方式中反思类型与用户表现、用户满意度及主观体验之间的相关性。由此，本研究揭示了LLM在创意编程中的艺术潜能，并从艺术家视角提供了对人机协作的批判性审视，同时为未来AI辅助创意任务的设计提出了建议。