Proactive Agentic Whiteboards: Enhancing Diagrammatic Learning
This addresses the challenge for educators in real-time teaching by reducing cognitive demands during diagram creation, though it is an early exploration with incremental improvements.
The paper tackles the problem of educators struggling to create clear diagrams while teaching by introducing DrawDash, an AI-powered whiteboard assistant that proactively completes and refines diagrams based on spoken explanations, reducing cognitive load and improving pedagogy.
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.