Creativity is a vital skill in science, technology, engineering, and mathematics (STEM)-related education, fostering innovation and problem-solving. Traditionally, creativity assessments relied on human evaluations, such as the consensual assessment technique (CAT), which are resource-intensive, time-consuming, and often subjective. Recent advances in computational methods, particularly large language models (LLMs), have enabled automated creativity assessments. In this study, we extend research on automated creativity scoring to a flow-based music programming environment, a context that integrates computational and creative thinking. We collected 383 programming artifacts from 194 primary school students (2022-2024) and employed two automated approaches: an evidence-centred design (ECD) framework-based approach and an LLM-based approach using ChatGPT-4 with few-shot learning. The ECD-based approach integrates divergent thinking, complexity, efficiency, and emotional expressiveness, while the LLM-based approach uses CAT ratings and ECD examples to learn creativity scoring. Results revealed moderate to strong correlations with human evaluations (ECD-based: r = 0.48; LLM-based: r = 0.68), with the LLM-based approach demonstrating greater consistency across varying learning examples (r = 0.82). These findings highlight the potential of automated tools for scalable, objective, and efficient creativity assessment, paving the way for their application in creativity-focused learning environments.