Abstract
This disclosure techniques for automatically adjusting the complexity of online content. It addresses the issue of "one-size-fits-all" content that doesn't cater to diverse reading levels, creating accessibility and comprehension barriers. Per techniques of this disclosure, publishers of static content can pre-generate multiple complexity variants of their articles using artificial intelligence, store the variants, and enable instant, client-side swapping between versions based on user selection of a target complexity level via a simple control. In dynamic content generation contexts, such as virtual assistants and AI-powered chatbots, the techniques enable users to trigger dynamic re-generation of responses at different complexity levels, seamlessly replacing the original text. Key advantages include an integrated user experience, high-quality output through voice/style-aware generation, and simplified end-user interaction. Applications range from conversational AI and web searches to news media, documentation, and productivity suites, broadening content accessibility for various audiences.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bustos, Juan, "Dynamic Adjustment of Text Complexity", Technical Disclosure Commons, (September 02, 2025)
https://www.tdcommons.org/dpubs_series/8541