Proactive Agentic Whiteboards: Enhancing Diagrammatic Learning

TL;DR

This paper introduces DrawDash, an AI-powered whiteboard assistant that proactively enhances educational diagrams in real time by understanding spoken explanations, aiming to reduce instructors' cognitive load and improve diagrammatic teaching.

Contribution

We present DrawDash, a novel multimodal system that dynamically refines educational diagrams during lectures through speech recognition and proactive suggestions.

Findings

Effective real-time diagram refinement demonstrated across diverse subjects

Reduces instructors' cognitive load during diagram creation

Potential to enhance diagrammatic learning in classrooms

Abstract

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.