@inproceedings{10.1145/3663547.3759713,
author = {Khanom, Nazmun Nahar and Gershkovich, Aaron and Alonzo, Oliver and Hassan, Saad},
title = {A Customizable AI-Powered Automatic Text Simplification Tool for Supporting In-Situ Text Comprehension},
year = {2025},
isbn = {9798400706769},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3663547.3759713},
doi = {10.1145/3663547.3759713},
abstract = {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.},
booktitle = {Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility},
pages = {1–5},
numpages = {5},
keywords = {Automatic Text Simplification, Reading Assistance, Chatbots, AI, AI Reading Tools, GPT, Deaf and Hard-of-hearing, DHH, Reading},
location = {
},
series = {ASSETS '25}
}