ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12

Abstract

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.

29 pages, 7 figures, accepted by CHI 2024

References in corpus (2)

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

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• LitterBox+: An Extensible Framework for LLM-enhanced Scratch Static Code Analysis
• An Experimental Evaluation of an AI-Powered Interactive Learning Platform