# Proactive Agentic Whiteboards: Enhancing Diagrammatic Learning

arXiv: https://arxiv.org/abs/2512.01234 · HTML: https://mlsanity.com/paper/2512.01234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 2 of 2)

- [A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning](https://mlsanity.com/paper/1909.06983/) (2019)
- [Examining Autocompletion as a Basic Concept for Interaction with Generative AI](https://mlsanity.com/paper/2201.06892/) (2022)

## Built on by (most cited first, 1 of 1)

- [VoCa: Designing Speech-Canvas Interaction for Voice-Based Conversational Agents](https://mlsanity.com/paper/2610.04706/) (2026)

## Abstract (arXiv)

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.