AltCanvas: A Tile-Based Editor for Visual Content Creation with Generative AI for Blind or Visually Impaired People
Abstract
People with visual impairments often struggle to create content that relies heavily on visual elements, particularly when conveying spatial and structural information. Existing accessible drawing tools, which construct images line by line, are suitable for simple tasks like math but not for more expressive artwork. On the other hand, emerging generative AI-based text-to-image tools can produce expressive illustrations from descriptions in natural language, but they lack precise control over image composition and properties. To address this gap, our work integrates generative AI with a constructive approach that provides users with enhanced control and editing capabilities. Our system, AltCanvas, features a tile-based interface enabling users to construct visual scenes incrementally, with each tile representing an object within the scene. Users can add, edit, move, and arrange objects while receiving speech and audio feedback. Once completed, the scene can be rendered as a color illustration or as a vector for tactile graphic generation. Involving 14 blind or low-vision users in design and evaluation, we found that participants effectively used the AltCanvas’s workflow to create illustrations.
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In research involving people with disabilities, participants mostly collaborate on the design or evaluation of access-supporting generative AI systems.
“Similarly, in research involving PwD, participants mostly collaborate on the design or evaluation of accesssupporting generative AI systems [2, 3, 22, 60, 61, 73, 98, 109].”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [73]
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Huh, M., Peng, Y.-H., & Pavel, A. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3586183.3606735 · Cited by 67 · see citing works
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Cripping AI: Reimagining AI Through Lived Disability Experiences
Tang, X., Lin, T.-A., Li, J., & Wu, S. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3806744 · Cited by 2 · see citing works
Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation
Thieme, A., Faia Marques, R., Grayson, M., Balachandar, S., Tyler Cassidy, C., Choksi, M. Z., Longden, C., Huda, R. S., Ileve Kalovwe, N., Mallon, C., et al. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790768 · Cited by 1 · see citing works
- Authors
- Lee, S., Kohga, M., Landau, S., O'Modhrain, S., & Subramonyam, H.
- Affiliations
- Stanford Engineering, Stanford University, United States
- Stanford University (US)
- Touch Graphics (United States) (US)
- University of Michigan, United States
- Stanford University, United States
- Venue
- The 26th International ACM SIGACCESS Conference on Computers and Accessibility
- Publisher
- ACM
- Published
- 2024-10-27
- Pages
- 1-22
- OpenAlex
- W4403576922
- Cited by
- 25
- References
- 3
- Citations by year
- 2026: 11 · 2025: 14
- FWCI
- 13.66
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- National Science Foundation (2229873)
- Topics
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- visual impairment, blind users, low vision, generative AI, text-to-image generation, tactile graphics, speech feedback, audio feedback, visual content creation, illustration design, assistive technology
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