Scratch Copilot: Supporting Youth Creative Coding with AI
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Abstract (arXiv)
Creative coding platforms like Scratch have democratized programming for children, yet translating imaginative ideas into functional code remains a significant hurdle for many young learners. While AI copilots assist adult programmers, few tools target children in block-based environments. Building on prior research citedrugahow2021,druga2023ai, druga2023scratch, we present Cognimates Scratch Copilot: an AI-powered assistant integrated into a Scratch-like environment, providing real-time support for ideation, code generation, debugging, and asset creation. This paper details the system architecture and findings from an exploratory qualitative evaluation with 18 international children (ages 7--12). Our analysis reveals how the AI Copilot supported key creative coding processes, particularly aiding ideation and debugging. Crucially, it also highlights how children actively negotiated the use of AI, demonstrating strong agency by adapting or rejecting suggestions to maintain creative control. Interactions surfaced design tensions between providing helpful scaffolding and fostering independent problem-solving, as well as learning opportunities arising from navigating AI limitations and errors. Findings indicate Cognimates Scratch Copilot's potential to enhance creative self-efficacy and engagement. Based on these insights, we propose initial design guidelines for AI coding assistants that prioritize youth agency and critical interaction alongside supportive scaffolding.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 7 of its 57 references that are in the map. First posted on arXiv 6 May 2025.
Builds on
7 of 57 references.
References · 7
- 2023Teaching Large Language Models to Self-Debugcited by 988
- 2024Large Language Models for Education: A Survey and Outlookcited by 188
- 2022Automatic Generation of Programming Exercises and Code Explanations using Large Language Modelscited by 126
- 2023Scratch Copilot Evaluation: Assessing AI-Assisted Creative Coding for Familiescited by 5
- 2016Mosaic: Designing Online Creative Communities for Sharing Works-in-Progresscited by 4
- 2024Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design Activitiescited by 4
- 2024Investigating Youths' Everyday Understanding of Machine Learning Applications: a Knowledge-in-Pieces Perspectivecited by 1
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Citing papers per year: 2026: 3.
3 papers.