Reflexa: Uncovering How LLM-Supported Reflection Scaffolding Reshapes Creativity in Creative Coding

TL;DR

This paper introduces Reflexa, a system that leverages large language models to scaffold reflection in creative coding, enhancing exploration, control, and originality in human-AI co-creative processes.

Contribution

It presents a novel integrated reflection scaffolding system based on design principles derived from expert studies, improving creative outcomes in coding with LLM support.

Findings

Structured reflection mediates AI interaction and creative quality

Reflection trajectories increase perceived control and exploration

Enhanced originality and aesthetic quality in creative coding

Abstract

Creative coding requires continuous translation between evolving concepts and computational artifacts, making reflection essential yet difficult to sustain. Creators often struggle to manage ambiguous intentions, emergent outputs, and complex code, limiting depth of exploration. This work examines how large language models (LLMs) can scaffold reflection not as isolated prompts, but as a system-level mechanism shaping creative regulation. From formative studies with eight expert creators, we derived reflection challenges and design principles that informed Reflexa, an integrated scaffold combining dialogic guidance, visualized version navigation, and iterative suggestion pathways. A within-subject study with 18 participants provides an exploratory mechanism validation, showing that structured reflection patterns mediate the link between AI interaction and creative outcomes. These reflection trajectories enhanced perceived controllability, broadened exploration, and improved originality and aesthetic quality. Our findings advance HCI understanding of reflection from LLM-assisted creative practices, and provide design strategies for building LLM-based creative tools that support richer human-AI co-creativity.