Jake Jin1, Yu Sun2 and Ang Li2, 1USA, 2California State Polytechnic University, USA
Our research tackles the pressing issue of making news articles accessible and understandable to diverse audiences, particularly those with low literacy levels or cognitive disabilities such as dyslexia or autism [1]. We introduce an innovative AI-driven news application that employs advanced text simplification techniques alongside dynamic user feedback loops to significantly enhance readability and comprehension. At the heart of our solution is the integration of cutting-edge natural language processing (NLP) and machine learning technologies, including BERT text simplification models for parsing and restructuring complex sentences, coupled with sentiment analysis to gauge the emotional tone of content [2][3]. Addressing challenges such as maintaining accuracy in text simplification and fine-tuning the user feedback mechanism were pivotal in our development process [4]. Through rigorous experimentation, including controlled tests and user trials, we observed marked improvements in the accessibility of news content, with enhanced readability scores and positive user feedback. Our application stands out by offering a scalable, user-centered approach to news consumption, adapting to individual preferences and reading abilities. This ensures a more inclusive, informed public discourse, making our app an indispensable resource for brididing the information divide and empowering all users to stay informed, regardless of their literacy level or cognitive capabilities.
App, Artificial intelligence, Reading, News, Simplification
