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1. Conventionalization of Iconic Handshape Preferences in FamilyHomesign Systems

   Quam, M.; Brentari, D.; Coppola, M. (June 2022, Languages)

   Sandler, W.; Aronoff, M.; Padden, C. (Ed.)

   Variation in the linguistic use of handshapes exists across sign languages, but it is unclear how these iconic handshape preferences arise and become conventionalized. In order to understand the factors that shape such handshape preferences in the earliest stages of language emergence, we examined communication within family homesign systems. Homesigners are deaf individuals who have not acquired a signed or spoken language and who innovate unique gesture systems to communicate with hearing friends and family (“communication partners”). We analyzed how characteristics of participants and stimulus items influence handshape preferences and conventionalization. Participants included 11 deaf homesigners, 24 hearing communication partners (CPs), and 8 hearing non-signing adults from Nicaragua. Participants were asked to label items using gestures or signs. The handshape type (Handling, Object, or combined Handling+Object) was then coded. The participants and groups showed variability in iconic handshape preferences. Adult homesigners’ families demonstrated more conventionalization than did child homesigners’ families. Adult homesigners also used a combined Handling + Object form more than other participants. Younger CPs and those with fewer years of experience using a homesign system showed greater conventionalization. Items that elicited a reliable handshape preference were more likely to elicit Handling rather than Object handshapes. These findings suggest that similarity in terms of handshape type varies even within families, including hearing gesturers in the same culture. Although adult homesigners’ families were more conventionalized than child homesigners’ families, full conventionalization of these handshape preferences do not seem to appear reliably within two to three decades of use in a family when only one deaf homesigner uses it as a primary system. 

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2. Customizing Generated Signs and Voices of AI Avatars: Deaf-Centric Mixed-Reality Design for Deaf-Hearing Communication

   https://doi.org/10.1145/3710953  

   Chen, Si; Cheng, Haocong; Su, Suzy; Patterson, Stephanie; Kushalnagar, Raja; Huang, Yun; Wang, Qi (May 2025, Proceedings of the ACM on Human-Computer Interaction)

   This study investigates innovative interaction designs for communication and collaborative learning between learners of mixed hearing and signing abilities, leveraging advancements in mixed reality technologies like Apple Vision Pro and generative AI for animated avatars. Adopting a participatory design approach, we engaged 15 d/Deaf and hard of hearing (DHH) students to brainstorm ideas for an AI avatar with interpreting ability (sign language to English and English to sign language) that would facilitate their face-to-face communication with hearing peers. Participants envisioned the AI avatars to address some issues with human interpreters, such as lack of availability, and provide affordable options to expensive personalized interpreting services. Our findings indicate a range of preferences for integrating the AI avatars with actual human figures of both DHH and hearing communication partners. The participants highlighted the importance of having control over customizing the AI avatar, such as AI-generated signs, voices, facial expressions, and their synchronization for enhanced emotional display in communication. Based on our findings, we propose a suite of design recommendations that balance respecting sign language norms with adherence to hearing social norms. Our study offers insights into improving the authenticity of generative AI in scenarios involving specific and sometimes unfamiliar social norms. 

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3. "We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools

   https://doi.org/10.1145/3706599.3719785  

   Huffman, Shuxu; Chen, Si; Mack, Kelly Avery; Su, Haotian; Wang, Qi; Kushalnagar, Raja (April 2025, ACM)

   Generative AI tools, particularly those utilizing large language models (LLMs), are increasingly used in everyday contexts. While these tools enhance productivity and accessibility, little is known about how Deaf and Hard of Hearing (DHH) individuals engage with them or the challenges they face when using them. This paper presents a mixed-method study exploring how the DHH community uses Text AI tools like ChatGPT to reduce communication barriers and enhance information access. We surveyed 80 DHH participants and conducted interviews with 9 participants. Our findings reveal important benefits, such as eased communication and bridging Deaf and hearing cultures, alongside challenges like lack of American Sign Language (ASL) support and Deaf cultural understanding. We highlight unique usage patterns, propose inclusive design recommendations, and outline future research directions to improve Text AI accessibility for the DHH community. 

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4. Discovering the Biases Children Bring to Language Learning

   https://doi.org/10.1111/cdep.12379  

   Goldin-Meadow, Susan (November 2020, Child Development Perspectives)

   Abstract The linguistic input children receive has a massive and immediate effect on their language acquisition. This fact makes it difficult to discover the biases that children bring to language learning simply because their input is likely to obscure those biases. In this article, I turn to children who lack linguistic input to aid in this discovery: deaf children whose hearing losses prevent their acquisition of spoken language and whose hearing parents have not yet exposed them to sign language. These children lack input from a conventional language model, yet create gestures, called homesigns, to communicate with hearing individuals. Homesigns have many, although not all, of the properties of human language. These properties offer the clearest window onto the linguistic structures that children seek as they either learn or, in the case of homesigners, construct language. 

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5. Homesign Research, Gesture Studies, and Sign Language Linguistics: The Bigger Picture of Homesign and Homesigners

   https://doi.org/10.1111/tops.12766  

   Coppola, Marie (July 2025, Topics in Cognitive Science)

   Abstract Studies of homesigns have shed light on the human capacity for language and on the challenging problem of language acquisition. The study of homesign has evolved from a perspective grounded in gesture studies and child development to include sign language linguistics and the role of homesigns in language emergence at the community level. One overarching finding is that homesigns more closely resemble sign languages used by linguistic communities than they resemble the gestures produced by hearing people along with spoken language. Homesigns may not exhibit all of the linguistic properties of community languages, but the properties they do exhibit are language properties, and for the people who use them, homesigns are their language. Further, the linguistic structures in homesigns are innovated by the deaf people who use them and are imperfectly learned by their hearing communication partners. I close with a call to action: We cannot celebrate discoveries about the mind made possible by studies of homesigns and emerging languages while ignoring the pervasiveness of language deprivation among deaf people, and the relative lack of deaf participation in science, even in studies of sign languages. While the scientific community learns much from studying homesigns and sign languages, we also have a responsibility to work toward ensuring that every deaf person has access to language, communication, and education. 

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