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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. 

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. 

Affective captions employ visual typographic modulations to convey a speaker’s emotions, improving speech accessibility for Deaf and Hard-of-Hearing (dhh) individuals. However, the most effective visual modulations for expressing emotions remain uncertain. Bridging this gap, we ran three studies with 39 dhh participants, exploring the design space of affective captions, which include parameters like text color, boldness, size, and so on. Study 1 assessed preferences for nine of these styles, each conveying either valence or arousal separately. Study 2 combined Study 1’s top-performing styles and measured preferences for captions depicting both valence and arousal simultaneously. Participants outlined readability, minimal distraction, intuitiveness, and emotional clarity as key factors behind their choices. In Study 3, these factors and an emotion-recognition task were used to compare how Study 2’s winning styles performed versus a non-styled baseline. Based on our findings, we present the two best-performing styles as design recommendations for applications employing affective captions. 

Searching unfamiliar American Sign Language (ASL) words in a dictionary is challenging for learners, as it involves recalling signs from memory and providing specific linguistic details. Fortunately, the emergence of sign-recognition technology will soon enable users to search by submitting a video of themselves performing the word. Although previous research has independently addressed algorithmic enhancements and design aspects of ASL dictionaries, there has been limited effort to integrate both. This paper presents the design of an end-to-end sign language dictionary system, incorporating design recommendations from recent human–computer interaction (HCI) research. Additionally, we share preliminary findings from an interview-based user study with four ASL learners.