StoryDrawer: A Child–AI Collaborative Drawing System to Support Children's Creative Visual Storytelling
Abstract
Visual storytelling is a new approach to creative expression based on verbal and figural creativity. The keys to visual storytelling are narrating and drawing over a period of time, which can be beneficial but also demanding on creativity for children. Informed by need-finding investigations, we developed StoryDrawer, a co-creative system that supports visual storytelling for children aged 6–10 years through collaborative drawing between children and artificial intelligence (AI). The system includes a context-based voice agent and two AI-driven collaborative strategies: the real-time transformation of children's telling into drawings, and the generation of abstract sketches with semantic similarity to existing story content. We conducted a 2 × 2 study with 64 children to evaluate the efficacy of StoryDrawer by varying the two strategies in four conditions. The results suggest that StoryDrawer provoked participants’ creative and elaborate ideas and contributed to their creative outcomes during an engaging visual storytelling experience.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some reviewed papers consider or attempt to mitigate the constraints AI places on creativity.
“We found that some of the reviewed papers consider or attempt to mitigate this [37,62].”
From Child-AI Co-Creation: A Review of the Current Research Landscape and a Proposal for Six Design Considerations (2025) · cites this paper as [62]
Intended constraints are deliberately designed by developers, such as AI-generated suggestions or facilitation strategies that may steer creativity in a specific direction.
“Intended constraints are those deliberately designed by developers, such as AI-generated suggestions or facilitation strategies that, while helpful, may end up steering creativity in a specific direction [62].”
From Child-AI Co-Creation: A Review of the Current Research Landscape and a Proposal for Six Design Considerations (2025) · cites this paper as [62]
Drawing is intuitive and straightforward for children compared to other digital literacy.
“we chose drawing as the medium for assets creation, recognizing its intuitive and straightforward nature for children in comparison to other digital literacy [14, 81].”
From ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12 (2024) · cites this paper as [81]
The citation lists haven’t been fetched yet
References 76 · Cited by 114
- Authors
- Zhang, C., Yao, C., Wu, J., Lin, W., Liu, L., Yan, G., & Ying, F.
- Affiliations
- Zhejiang University (CN)
- Hubei University of Technology (CN)
- Venue
- CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2022-04-29
- Pages
- 1-15
- OpenAlex
- W4225012808
- Cited by
- 114
- References
- 76
- Citations by year
- 2026: 31 · 2025: 41 · 2024: 27 · 2023: 14 · 2022: 1
- FWCI
- 23.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (2021FZZX002-15)
- Major Project of Zhejiang Social Science Foundation (21XXJC01ZD)
- National Natural Science Foundation of China (52075478)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Health Sciences › Health Professions › Speech and Hearing › Digital Storytelling and Education
- Keywords
- visual narrative, children's creativity, collaborative drawing, human-AI co-creation, voice assistants, semantic similarity, children aged 6-10
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
