  As Computational Thinking (CT) continues to permeate younger age groups in
K-12 education, established CT platforms such as Scratch face challenges in
catering to these younger learners, particularly those in the elementary school
(ages 6-12). Through formative investigation with Scratch experts, we uncover
three key obstacles to children's autonomous Scratch learning: artist's block
in project planning, bounded creativity in asset creation, and inadequate
coding guidance during implementation. To address these barriers, we introduce
ChatScratch, an AI-augmented system to facilitate autonomous programming
learning for young children. ChatScratch employs structured interactive
storyboards and visual cues to overcome artist's block, integrates digital
drawing and advanced image generation technologies to elevate creativity, and
leverages Scratch-specialized Large Language Models (LLMs) for professional
coding guidance. Our study shows that, compared to Scratch, ChatScratch
efficiently fosters autonomous programming learning, and contributes to the
creation of high-quality, personally meaningful Scratch projects for children.
