  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.
