Interest in learning American Sign Language (ASL) is growing across higher education institutions in North America, as reflected in rising enrollments. Yet this growth is constrained by limited program availability and few opportunities to practice outside the classroom. AI-based technologies show promise for supporting ASL learning, but educators – who bring essential pedagogical, linguistic, and cultural expertise – have been largely absent from conversations on the design of these tools, with prior work focusing primarily on learners. To address this, we conducted formative interviews with eleven Deaf and one hearing ASL instructor, followed by two focus groups with six Deaf educators, to examine how AI tools could support ASL education. Findings revealed priorities for technology design and considerations for integration into existing pedagogical practices, with attention to curricular, linguistic, and access factors. We offer insights for designing and researching technologies aimed at (1) providing adaptive, structured feedback on signing performance and (2) supporting immersive conversational practice with virtual signing partners.

Acute Coronary Syndromes (ACS), including ST- and non-ST-segment elevation myocardial infarction (STEMI, NSTEMI), remain a leading cause of global mortality. Traditional Cardiovascular Risk Scores (CVRS) provide important insights but mainly rely on clinical data, often neglecting environmental factors (e.g.air pollution, climate) that significantly influence cardiovascular health. Integrating complex time-series environmental and clinical datasets also presents substantial challenges. We propose TabulaTime, a multimodal deep learning framework integrating clinical risk factors with environmental data to enhance ACS risk prediction. TabulaTime delivers three innovations: multimodal integration of time-series environmental and clinical data; PatchRWKV for extracting complex temporal patterns with linear computational complexity; and enhanced interpretability through attention mechanisms. TabulaTime improves prediction accuracy by 20.5% over CatBoost, with environmental data contributing a 10.1% gain. PatchRWKV outperforms state-of-the-art models (MLP-, CNN-, RNN- and Transformer-based models). Feature analysis highlights key clinical and environmental predictors. This approach advances personalised prevention and strengthens public health against cardiovascular risks. • A novel multimodal deep learning framework, TabulaTime, integrates clinical and environmental time-series data to improve Acute Coronary Syndrome (ACS) risk prediction. • We introduce PatchRWKV, an efficient time-series feature extractor with linear complexity that outperforms state-of-the-art models in capturing temporal patterns. • The integration of environmental data improves ACS prediction accuracy by 10.1%, with feature analysis identifying key clinical and pollution-related predictors.

Reading is a vital skill for social, educational, and professional development, yet various disabilities can impact a person's ability to read and develop literacy skills.HCI and accessibility researchers have explored a wide range of technologies to support reading for people with disabilities.To understand trends in this space, we analyzed 101 publications from Association for Computing Machinery (ACM) venues , coding for target user communities, research methods, technologies, types of support, and contributions.Most research focused on people with dyslexia, followed by people who are Blind or Low Vision, Deaf or Hard of Hearing, or who have intellectual and cognitive disabilities.The majority of studies involved artifact development and short-term lab-based evaluations, with common technologies including visual augmentations, text modifications, and simplification-primarily aimed at improving readability, comprehension, and reading speed.However, participatory approaches and longitudinal evaluations were rarely employed, and the body of work has disproportionately focused on web-based digital reading.Following the initial coding, we conducted community-specific analyses of individual publications to identify patterns and limitations.Based on these analyses, we offer a set of open research questions and community-specific directions to guide future work.

People with disabilities represent linguistically diverse communities.For example, among Deaf and Hard of Hearing (DHH) people, many of whom use sign language as their primary language, there is significant variation in written language literacy, highlighting that some might benefit from reading comprehension support tools.Prior research has demonstrated the benefits of lexical and syntactic approaches to Automatic Text Simplification for DHH readers and explored design considerations.Building on this work, we present a fully automatic, GPT-based text comprehension tool that provides in-situ reading support.The tool, released with this demo paper, is easily customizable and adaptable to support a range of disability communities and literacy levels.We present usage scenarios to spark conversations around broader applicability, personalization needs, and future studies comparing in-situ reading support to chatbot-style GPT interfaces.

Deaf and Hard of Hearing (DHH) people often face significant barriers in medical settings, leading to miscommunication and reduced access to care.While American Sign Language (ASL) interpretation is essential for effective communication with DHH signers, it is frequently unavailable in emergency contexts.Emergency Medical Responders (EMRs)-frontline responders trained to deliver basic emergency care-often struggle to obtain accurate medical histories, particularly from DHH people with limited English literacy.To address this, we designed an AI-based ASL learning tool tailored for EMRs, featuring medical vocabulary modules and AI-powered vocabulary testing support.We present a preliminary evaluation of the tool with five EMRs and publicly release a working prototype with this paper.Insights from the study inform new features and vocabulary expansion.

Recruiting participants from disability communities for accessibility research presents unique challenges that require careful consideration of ethical practices, intersectional representation, methodological rigor, and community sustainability.As accessibility research continues to grow and evolve, researchers face tensions between meaningfully including participants with disabilities and addressing emerging concerns around recruited participants not adequately representing the diversity of the community, overburdening certain participants, participant verification, and fair compensation practices.This workshop will bring together members of the ASSETS community to examine current recruiting practices and document insights into ethical, rigorous, and inclusive participant recruitment in disability research.Through facilitated discussions, we will explore three main themes: (1) methods and models, (2) eligibility criteria and participant verification, and (3) ethical and sustainability considerations.The workshop aims to share current practices, identify key challenges, and develop preliminary guidelines to support accessibility researchers in more sustainable participant recruitment.

Figure 1: Multimodal afective captions, combining visual cues and vibrations felt via a wrist-worn device, enrich the viewing experience for d/Deaf or Hard-of-Hearing individuals by portraying speaker emotions, improving engagement.

Progress in machine understanding of sign languages has been slow and hampered by limited data. In this paper, we present FSboard, an American Sign Language finger-spelling dataset situated in a mobile text entry use case, collected from 147 paid and consenting Deaf signers using Pixel 4A selfie cameras in a variety of environments. Fin-gerspelling recognition is an incomplete solution that comprises only a small part of sign language translation, but it could provide some immediate benefit to Deaf/Hard of Hearing signers while more broadly capable technology develops. At >3 million characters in length and >250 hours in duration, FSboard is the largest fingerspelling recognition dataset to date by a factor of >10x. As a simple baseline, we finetune 30 Hz MediaPipe Holistic landmark inputs into ByT5-Small and achieve 11.1% Character Error Rate (CER) on a test set with unique phrases and signers. This quality degrades gracefully when decreasing frame rate and excluding face/body landmarks—plausible optimizations to help with on-device performance—but falls short of human performance measured at 2.2% CER.1

Social stigma negatively impacts the well-being of neurodivergent individuals. Specifically for autistic people, the social isolation and pressure to conform to normative ways of being can take a tremendous toll; such as a thwarted sense of belonging to the point of higher rates of suicide. Yet, few technologies are directly targeting the problem of stigma. Socio-technical systems have tremendous potential to shift public perception of traditionally marginalized populations. However, these systems are not consistently designed to reflect the values and needs of neurodivergent individuals. This work explores the use of design sprints to envision CT– a spectrum of technology that could reduce social stigma by increasing the public’s awareness, accommodations, acceptance, advocacy, and appreciation. This work reports on two design sprints across 25 HCI community members with varying lived experiences with neurodiversity and knowledge of design practice. The resulting design concepts were discussed in the groups and then analyzed to reflect on how they might combat stigma. Results reveal designs that support the freedom to be oneself via (1) safe spaces (2) public understanding, and (3) authentic expression of strengths, challenges, and needs.

Closed-captioning is an essential part of viewing audio-visual content for many people, including those who are D/deaf and Hard-of-Hearing. Traditional closed-captioning systems generally consist of a single track of timed text that offers limited options for personalization. Research into extending the capabilities of captioning, such as affective, poetic, and customizable captions has shown a desire among a subset of users for these features, but only in specific contexts. However, due to the difficulty in creating custom stimuli videos utilizing the custom captioning system, comparisons between systems and longitudinal studies have not been pursued. This demo paper introduces Rich Captions, a structured system that allows for a single closed-caption file to be tagged with additional information that can then be flexibly leveraged to render different customizable, creative, and poetic captions from the same file. Additionally, we introduce the Rich Caption Editor 1, a free, open-source software system designed to author, edit, and render rich captions. The system design was informed by a formative design workshop with closed-captioning researchers and advocates. The current design allows researchers to generate reproducible stimuli for closed-captioning studies. Once the design space and user preferences are better understood, the rich captioning framework could be refined to serve a general audience.

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.

PopSign is a smartphone-based bubble-shooter game that helps hearing parentsof deaf infants learn sign language. To help parents practice their ability to sign,PopSign is integrating sign language recognition as part of its gameplay. Fortraining the recognizer, we introduce the PopSign ASL v1.0 dataset that collectsexamples of 250 isolated American Sign Language (ASL) signs using Pixel 4Asmartphone selfie cameras in a variety of environments. It is the largest publiclyavailable, isolated sign dataset by number of examples and is the first dataset tofocus on one-handed, smartphone signs. We collected over 210,000 examplesat 1944x2592 resolution made by 47 consenting Deaf adult signers for whomAmerican Sign Language is their primary language. We manually reviewed 217,866of these examples, of which 175,023 (approximately 700 per sign) were the signintended for the educational game. 39,304 examples were recognizable as a signbut were not the desired variant or were a different sign. We provide a training setof 31 signers, a validation set of eight signers, and a test set of eight signers. Abaseline LSTM model for the 250-sign vocabulary achieves 82.1% accuracy (81.9%class-weighted F1 score) on the validation set and 84.2% (83.9% class-weightedF1 score) on the test set. Gameplay suggests that accuracy will be sufficient forcreating educational games involving sign language recognition.

Soon, smartphones may be capable of allowing American Sign Language (ASL) signing and/or fingerspelling for text entry. To explore the usefulness of this approach, we compared emulated fingerspelling recognition with a virtual keyboard for 12 Deaf participants. With practice, fingerspelling is faster (42.5 wpm), potentially has fewer errors (4.02% corrected error rate) and higher throughput (14.2 bits/second), and is as desired as virtual keyboard texting (31.9 wpm; 6.46% corrected error rate; 10.9 bits/second throughput). Our second study recruits another 12 Deaf users at the 2022 National Association for the Deaf conference to compare the walk-up usability of fingerspelling alone, signing, and virtual keyboard text entry for interacting with an emulated mobile assistant. Both signing and virtual keyboard text entry were preferred over fingerspelling.

As they develop comprehension skills, American Sign Language (ASL) learners often view challenging ASL videos, which may contain unfamiliar signs. Current dictionary tools require students to isolate a single sign they do not understand and input a search query, by selecting linguistic properties or by performing the sign into a webcam. Students may struggle with extracting and re-creating an unfamiliar sign, and they must leave the video-watching task to use an external dictionary tool. We investigate a technology that enables users, in the moment, i.e., while they are viewing a video, to select a span of one or more signs that they do not understand, to view dictionary results. We interviewed 14 American Sign Language (ASL) learners about their challenges in understanding ASL video and workarounds for unfamiliar vocabulary. We then conducted a comparative study and an in-depth analysis with 15 ASL learners to investigate the benefits of using video sub-spans for searching, and their interactions with a Wizard-of-Oz prototype during a video-comprehension task. Our findings revealed benefits of our tool in terms of quality of video translation produced and perceived workload to produce translations. Our in-depth analysis also revealed benefits of an integrated search tool and use of span-selection to constrain video play. These findings inform future designers of such systems, computer vision researchers working on the underlying sign matching technologies, and sign language educators.

Advancements in AI will soon enable tools for providing automatic feedback to American Sign Language (ASL) learners on some aspects of their signing, but there is a need to understand their preferences for submitting videos and receiving feedback. Ten participants in our study were asked to record a few sentences in ASL using software we designed, and we provided manually curated feedback on one sentence in a manner that simulates the output of a future automatic feedback system. Participants responded to interview questions and a questionnaire eliciting their impressions of the prototype. Our initial findings provide guidance to future designers of automatic feedback systems for ASL learners.

Television captions blocking visual information causes dissatisfaction among Deaf and Hard of Hearing (DHH) viewers, yet existing caption evaluation metrics do not consider occlusion. To create such a metric, DHH participants in a recent study imagined how bad it would be if captions blocked various on-screen text or visual content. To gather more ecologically valid data for creating an improved metric, we asked 24 DHH participants to give subjective judgments of caption quality after actually watching videos, and a regression analysis revealed which on-screen contents’ occlusion related to users’ judgments. For several video genres, a metric based on our new dataset out-performed the prior state-of-the-art metric for predicting the severity of captions occluding content during videos, which had been based on that prior study. We contribute empirical findings for improving DHH viewers’ experience, guiding the placement of captions to minimize occlusions, and automated evaluation of captioning quality in television broadcasts.

Searching for the meaning of an unfamiliar sign-language word in a dictionary is difficult for learners, but emerging sign-recognition technology will soon enable users to search by submitting a video of themselves performing the word they recall. However, sign-recognition technology is imperfect, and users may need to search through a long list of possible results when seeking a desired result. To speed this search, we present a hybrid-search approach, in which users begin with a video-based query and then filter the search results by linguistic properties, e.g., handshape. We interviewed 32 ASL learners about their preferences for the content and appearance of the search-results page and filtering criteria. A between-subjects experiment with 20 ASL learners revealed that our hybrid search system outperformed a video-based search system along multiple satisfaction and performance metrics. Our findings provide guidance for designers of video-based sign-language dictionary search systems, with implications for other search scenarios.

Despite some prior research and commercial systems, if someone sees an unfamiliar American Sign Language (ASL) word and wishes to look up its meaning in a dictionary, this remains a difficult task. There is no standard label a user can type to search for a sign, and formulating a query based on linguistic properties is challenging for students learning ASL. Advances in sign-language recognition technology will soon enable the design of a search system for ASL word look-up in dictionaries, by allowing users to generate a query by submitting a video of themselves performing the word they believe they encountered somewhere. Users would then view a results list of video clips or animations, to seek the desired word. In this research, we are investigating the usability of such a proposed system, a webcam-based ASL dictionary system, using a Wizard-of-Oz prototype and enhanced the design so that it can support sign language word look-up even when the performance of the underlying sign-recognition technology is low. We have also investigated the requirements of students learning ASL in regard to how results should be displayed and how a system could enable them to filter the results of the initial query, to aid in their search for a desired word. We compared users’ satisfaction when using a system with or without post-query filtering capabilities. We discuss our upcoming study to investigate users’ experience with a working prototype based on actual sign-recognition technology that is being designed. Finally, we discuss extensions of this work to the context of users searching datasets of videos of other human movements, e.g. dance moves, or when searching for words in other languages.