Draw2Learn: A Human-AI Collaborative Tool for Drawing-Based Science Learning

TL;DR

Draw2Learn is an AI-powered tool that enhances drawing-based science learning by providing structured tasks, visual scaffolds, and feedback, fostering learner autonomy and supporting educational outcomes.

Contribution

This work introduces a novel design framework for teammate-oriented AI in generative learning environments, focusing on drawing-based science education.

Findings

Positive user feedback on usability and usefulness

AI scaffolding enhances learner autonomy

Framework guides future AI integration in education

Abstract

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.