A computational text-analysis tool developed by Arthur Graesser, Danielle McNamara, and colleagues at the University of Memphis (now hosted at Arizona State University). Coh-Metrix produces over 200 measures of cohesion, language, and readability, grounded in multilevel theories of comprehension that distinguish surface form, textbase, and situation model. Where classical readability formulas like Flesch-Kincaid Grade Level capture only sentence length and word length, Coh-Metrix adds discourse-level cohesion, lexical concreteness, and rhetorical structure.
The Text Easability Assessor (Coh-Metrix-TEA) funnels the 200-plus measures into five factors that vary systematically across genre and grade level (Graesser, McNamara & Kulikowich 2011):
A text scoring high on all five is comprehensible to a wider readership; a text low on cohesion but high on syntactic simplicity may still confuse readers because the sentences-to-situation-model bridge is left implicit.
Coh-Metrix-TEA is the closest thing to an off-the-shelf passage profiler usable in item-bank curation. Three uses recur in the literature: stratifying passages across CEFR-aligned bands using more than just sentence/word length; flagging passages with anomalous cohesion that may produce construct-irrelevant difficulty; and comparing AI-generated passages to a human reference set to detect stylistic drift before bank entry.
The tool is freely available for research; the production-grade variant is licensed.