Tseng, Seoror, Adda, Factor, Darabi, Matschke, Fu, Lin, Maram & Sinha (2026) — arXiv preprint.
Building upon found examples is a popular way people learn to code, especially in creative coding communities where sharing projects and remixing are common practices. But effectively doing so requires being able to 1) understand how existing code works, and 2) extend it by writing code that implements your own ideas, practices that can be challenging for new creative coders. We explored how to support these two processes through the design of Flowcode, a creative coding programming environment that integrates a flowchart for visualizing code structure and a chat interface tailored to support learning to code over vibe coding. We share how we iterated on the design of Flowcode over two studies with new creative coders, reflecting on the roles visualization and friction may play in enabling productive AI-use in computing education.
Flowcode pairs a code-structure flowchart with a learning-oriented (not vibe-coding) chat to help novice creative coders understand and extend found examples; two studies show visualization and productive friction can steer AI use toward learning rather than bypass.
This work connects to core wiki themes: scaffolding generative-ai feedback-loop active-learning student-experience. It highlights how generative-AI tooling is reshaping both what learners do and how educators structure support, reinforcing the need for design that preserves authentic engagement rather than enabling shallow bypass.
Related Pages
- scaffolding — Structured support that fades as learners progress.
- generative-ai — LLM-based educational tools.
- feedback-loop — Iterative AI feedback.
- active-learning — Learning by doing.
- student-experience — The learner perspective.