@inproceedings{10.1145/3744257.3744258,
author = {Hassan, Saad and Bohacek, Matyas and Kim, Chaelin and Crochet, Denise},
title = {Towards an AI-Driven Video-Based American Sign Language Dictionary: Exploring Design and Usage Experience with Learners},
year = {2025},
isbn = {9798400718823},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3744257.3744258},
doi = {10.1145/3744257.3744258},
abstract = {Searching for unfamiliar American Sign Language (ASL) signs is challenging for learners because, unlike spoken languages, they cannot type a text-based query to look up an unfamiliar sign. Advances in isolated sign recognition have enabled the creation of video-based dictionaries, allowing users to submit a video and receive a list of the closest matching signs. Previous HCI research using Wizard-of-Oz prototypes has explored interface designs for ASL dictionaries. Building on these studies, we incorporate their design recommendations and leverage state-of-the-art sign-recognition technology to develop an automated video-based dictionary. We also present findings from an observational study with twelve novice ASL learners who used this dictionary during video-comprehension and question-answering tasks. Our results address human-AI interaction challenges not covered in previous WoZ research, including recording and resubmitting signs, unpredictable outputs, system latency, and privacy concerns. These insights offer guidance for designing and deploying video-based ASL dictionary systems.},
booktitle = {Proceedings of the 22nd International Web for All Conference},
pages = {80–94},
numpages = {15},
keywords = {American Sign Language, ASL, Dictionary, Video-based Dictionary, Human-AI Interaction},
location = {
},
series = {W4A '25}
}