Draw2Learn: A Human-AI Collaborative Tool for Drawing-Based Science Learning
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912868124852224 |
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| author | Hang, Yuqi |
| author_facet | Hang, Yuqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01494 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Draw2Learn: A Human-AI Collaborative Tool for Drawing-Based Science Learning Hang, Yuqi Human-Computer Interaction Artificial Intelligence 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. |
| title | Draw2Learn: A Human-AI Collaborative Tool for Drawing-Based Science Learning |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2602.01494 |