Automated Readability Scoring

The use of computational methods to automatically evaluate the reading difficulty level of a text. Traditional readability formulas like Flesch-Kincaid and Dale-Chall use surface features such as average sentence length, word length, and vocabulary frequency to assign grade-level scores. However, these formulas were designed for general readers and do not reliably predict difficulty for people with intellectual disabilities, who face distinct challenges including slower semantic encoding, difficulty building cohesive discourse representations, and limited working memory. More recent approaches use machine learning and natural language processing techniques to incorporate deeper linguistic features such as syntactic complexity, entity density, and discourse structure for more accurate readability prediction across diverse populations.