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We surveyed 70 mobile app creators (34 professionals and 36 students learning mobile app creation) to understand their perceptions toward creativity and accessibility. We found mobile app creators who treated design constraints as creative constraints naturally included accessibility, but those with the freedom of unrestricted aesthetic design often disregarded accessibility. Our research suggests that we can change negative perceptions toward accessible design by making it an integrated part of the creative process. 

A current trend in mobile user interface design is to provide alternative color modes (ACMs), such as light mode, dark mode, and high contrast mode, to improve people’s interaction experiences according to their vision access needs and/or the environment. For example, high contrast mode can improve UI visibility for people with low vision and for people using their smartphones in bright sunlight. However, little is known about the experiences of people using ACMs. To address this, we interviewed 29 people with and without vision impairments to discuss the benefits and challenges of ACMs. We found that while ACMs are beneficial, the current implementation results in accessibility and usability issues, particularly for people with vision impairments (e.g., negative health consequences and affected work performance). Using our findings, we outline steps the HCI community should consider to address current limitations and improve future ACMs. 

Cultural background influences aesthetic web design preferences, and aesthetic design impacts accessible design. However, limited research has focused on this intersection of cultural background and accessible web design. With the majority of HCI and design resources originating from the Global North, we investigated the conflicts experienced due to the cultural background of digital designers from the Global South and current web accessibility guidelines. We conducted a design activity and interview study with 10 designers from five countries in the Global South to identify how current web accessibility guidelines conflict with our participants’ cultural design preferences. We found there are specific cultural challenges encountered in accessible web design, both at the design level (e.g., typography and color scheme) and within broader societal contexts (e.g., designer-client interactions). Our paper also offers suggestions from our participants to make the accessible design process more culturally inclusive by improving the web accessibility resources to become culturally customized and engaging more cultural perspectives in accessibility research and education. 

The popularity of accessibility research has grown recently, improving digital inclusion for people with disabilities. However, researchers, including those who have disabilities, have attempted to include people with disabilities in all aspects of design, and they have identified a myriad of practical accessibility barriers posed by tools and methods leveraged by human-computer interaction (HCI) researchers during prototyping. To build a more inclusive technological landscape, we must question the effectiveness of existing prototyping tools and methods, repurpose/retrofit existing resources, and build new tools and methods to support the participation of both researchers and people with disabilities within the prototyping design process of novel technologies. This full-day workshop at CHI 2025 will provide a platform for HCI researchers, designers, and practitioners to discuss barriers and opportunities for creating accessible prototyping and promote hands-on ideation and fabrication exercises aimed at futuring accessible prototyping. 

People learning American Sign Language (ASL) and practicing their comprehension skills will often encounter complex ASL videos that may contain unfamiliar signs. Existing dictionary tools require users to isolate a single unknown sign before initiating a search by selecting linguistic properties or performing the sign in front of a webcam. This process presents challenges in extracting and reproducing unfamiliar signs, disrupting the video-watching experience, and requiring learners to rely on external dictionaries. We explore a technology that allows users to select and view dictionary results for one or more unfamiliar signs while watching a video. We interviewed 14 ASL learners to understand their challenges in understanding ASL videos, strategies for dealing with unfamiliar vocabulary, and expectations for anin situdictionary system. We then conducted an in-depth analysis with eight learners to examine their interactions with a Wizard-of-Oz prototype during a video comprehension task. Finally, we conducted a comparative study with six additional ASL learners to evaluate the speed, accuracy, and workload benefits of an embedded dictionary-search feature within a video player. Our tool outperformed a baseline in the form of an existing online dictionary across all three metrics. The integration of a search tool and span selection offered advantages for video comprehension. Our findings have implications for designers, computer vision researchers, and sign language educators. 

Analyzing dance moves and routines is a foundational step in learning dance. Videos are often utilized at this step, and advancements in machine learning, particularly in human-movement recognition, could further assist dance learners. We developed and evaluated a Wizard-of-Oz prototype of a video comprehension tool that offers automatic in-situ dance move identification functionality. Our system design was informed by an interview study involving 12 dancers to understand the challenges they face when trying to comprehend complex dance videos and taking notes. Subsequently, we conducted a within-subject study with 8 Cuban salsa dancers to identify the benefits of our system compared to an existing traditional feature-based search system. We found that the quality of notes taken by participants improved when using our tool, and they reported a lower workload. Based on participants’ interactions with our system, we offer recommendations on how an AI-powered span-search feature can enhance dance video comprehension tools.