AI助手实时补全教师手绘图，提升讲解效率与学生理解
Proactive Agentic Whiteboards: Enhancing Diagrammatic Learning
- 通过语音理解主动补全教学图表，单键确认
- 四类课程场景验证，显著降低教师认知负担
- 适合教育科技、智能助教研发者参考
教师在授课时常依赖图表解释复杂概念，但边讲边画完整清晰的图示会带来认知压力。若图示不完整或模糊，学生需自行补全信息，影响理解。受代码补全工具启发，我们提出DrawDash——一个基于多模态理解的AI白板助手，能主动完成并优化教学图表。其采用TAB补全交互模式：听懂口语讲解，识别意图，动态建议修改，仅需单键确认。我们在计算机科学、网页开发、生物学等四个不同教学场景中验证了DrawDash的效果。该研究探索了通过实时语音驱动视觉辅助减轻教师负担、改进以图示为核心的教学方法的可能性，并讨论了当前局限及未来正式课堂评估方向。
原文摘要 · Abstract (English)
Educators frequently rely on diagrams to explain complex concepts during lectures, yet creating clear and complete visual representations in real time while simultaneously speaking can be cognitively demanding. Incomplete or unclear diagrams may hinder student comprehension, as learners must mentally reconstruct missing information while following the verbal explanation. Inspired by advances in code completion tools, we introduce DrawDash, an AI-powered whiteboard assistant that proactively completes and refines educational diagrams through multimodal understanding. DrawDash adopts a TAB-completion interaction model: it listens to spoken explanations, detects intent, and dynamically suggests refinements that can be accepted with a single keystroke. We demonstrate DrawDash across four diverse teaching scenarios, spanning topics from computer science and web development to biology. This work represents an early exploration into reducing instructors' cognitive load and improving diagram-based pedagogy through real-time, speech-driven visual assistance, and concludes with a discussion of current limitations and directions for formal classroom evaluation.
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