@inproceedings{10.1145/3663547.3759746,
author = {Kim, Chaelin and McLaren, Cameron and Krishnan, Madhangi and Modayur, Nikhil and Hassan, Saad},
title = {Signing for Care: A Demo and Initial Evaluation of an American Sign Language Learning Tool for Emergency Medical Service Providers},
year = {2025},
isbn = {9798400706769},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3663547.3759746},
doi = {10.1145/3663547.3759746},
abstract = {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.},
booktitle = {Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility},
pages = {1–6},
numpages = {6},
keywords = {American Sign Language, Sign Language, ASL, Sign Language Learning, Emergency Medical Services, Emergency Medical Responders, EMR, EMS, EMT, Vocabulary Learning, Dictionary, Communication in Emergency Settings, First Aid, Healthcare},
location = {
},
series = {ASSETS '25}
}