AI助教陪孩子画画学科学，实时给反馈还留自主空间。
Draw2Learn: A Human-AI Collaborative Tool for Drawing-Based Science Learning
- AI设计结构化绘图任务，动态提供视觉提示
- 支持学习者自主探索，同时监测进度并多维度反馈
- 适合教育科技研究者和数字学习产品设计者
绘画能通过外化心智模型促进学习，但规模化提供及时反馈仍具挑战。本文提出Draw2Learn系统，探索如何让AI在绘画式学习中充当协作伙伴。设计将学习原理转化为具体交互模式：AI生成结构化绘图任务，提供可选视觉支架，监控学习进展，并给予多维反馈。开发过程中收集了形成性用户反馈及开放性评论，结果显示用户对可用性、实用性和体验均评价积极，核心主题包括AI支架的价值与学习者自主性的平衡。本工作贡献了一个面向协作型AI的生成式学习设计框架，并指明未来研究的关键考量。
原文摘要 · Abstract (English)
Drawing supports learning by externalizing mental models, but providing timely feedback at scale remains challenging. We present Draw2Learn, a system that explores how AI can act as a supportive teammate during drawing-based learning. The design translates learning principles into concrete interaction patterns: AI generates structured drawing quests, provides optional visual scaffolds, monitors progress, and delivers multidimensional feedback. We collected formative user feedback during system development and open-ended comments. Feedback showed positive ratings for usability, usefulness, and user experience, with themes highlighting AI scaffolding value and learner autonomy. This work contributes a design framework for teammate-oriented AI in generative learning and identifies key considerations for future research.
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