Blind and visually impaired (BVI) computer science students face systematic barriers when learning data structures: current accessibility approaches typically translate diagrams into alternative text, focusing on visual appearance rather than preserving the underlying structure essential for conceptual understanding. More accessible alternatives often do not scale in complexity, cost to produce, or both. Motivated by a recent shift to tools for creating visual diagrams from code, we propose a solution that automatically creates accessible representations from structural information about diagrams. Based on a Wizard-of-Oz study, we derive design requirements for an automated system, Arboretum, that compiles text-based diagram specifications into three synchronized nonvisual formats-tabular, navigable, and tactile. Our evaluation with BVI users highlights the strength of tactile graphics for complex tasks such as binary search; the benefits of offering multiple, complementary nonvisual representations; and limitations of existing digital navigation patterns for structural reasoning. This work reframes access to data structures by preserving their structural properties. The solution is a practical system to advance accessible CS education.

We introduce ArtInsight, a novel AI-powered system to facilitate deeper engagement with child-created artwork in mixed visual-ability families. ArtInsight leverages large language models (LLMs) to craft a respectful and thorough initial description of a child's artwork, and provides: creative AI-generated descriptions for a vivid overview, audio recording to capture the child's own description of their artwork, and a set of AI-generated questions to facilitate discussion between blind or low-vision (BLV) family members and their children. Alongside ArtInsight, we also contribute a new rubric to score AI-generated descriptions of child-created artwork and an assessment of state-of-the-art LLMs. We evaluated ArtInsight with five groups of BLV family members and their children, and as a case study with one BLV child therapist. Our findings highlight a preference for ArtInsight's longer, artistically-tailored descriptions over those generated by existing BLV AI tools. Participants highlighted the creative description and audio recording components as most beneficial, with the former helping ``bring a picture to life'' and the latter centering the child's narrative to generate context-aware AI responses. Our findings reveal different ways that AI can be used to support art engagement, including before, during, and after interaction with the child artist, as well as expectations that BLV adults and their sighted children have about AI-powered tools.

Children struggle to understand hidden data processes (e.g., inferences) and related privacy implications (e.g., profiling). Children use visual cues to reason about technical processes in digital products, sometimes drawing inaccurate conclusions when interface cues are vague or absent. We conducted five consecutive participatory design sessions with children (ages 7–12), probing their perceptions of visual cues and data processes; and iteratively designed and reviewed new visual cues with them. We found that children conceptualized data collection concretely, lacked awareness of its pervasive nature, expressed limited understanding of data inferences, and recognized certain visual cues (e.g., loading, cloud) but unable to explain their meanings. We designed visual cues in “symbolic” and “concrete” styles using icons and metaphors, which helped children understand data flows. Our work contributes to developing comprehensible visual cues for children to support their data and privacy literacy. We discuss design and policy implications of our findings.

The emergence of generative artificial intelligence (GenAI) has ignited discussions surrounding its potential to enhance creative pursuits. However, distinctions between children’s and adult’s creative needs exist, which is important when considering the possibility of GenAI for children’s creative usage. Building upon work in Human-Computer Interaction (HCI), fostering children’s computational thinking skills, this study explores interactions between children (aged 7-13) and GenAI tools through methods of participatory design. We seek to answer two questions: (1) How do children in co-design workshops perceive GenAI tools and their usage for creative works? and (2) How do children navigate the creative process while using GenAI tools? How might these interactions support their confidence in their ability to create? Our findings contribute a model that describes the potential contexts underpinning child-GenAI creative interactions and explores implications of this model for theories of creativity, design, and use of GenAI as a constructionist tool for creative self-efficacy.