Comprendil

Comprendil is a proposed theoretical framework in cognitive science and educational technology for characterizing and measuring reading comprehension. The term emerged in scholarly discussions during the late 2010s and draws on the verb comprendre (to understand) and the diminutive suffix -il to signify a compact unit of comprehension.

The framework distinguishes three interlinked facets: literal comprehension (recall of explicit details), inferential comprehension (drawing conclusions from text and context), and metacognitive comprehension (awareness and regulation of one’s understanding and strategy use).

Assessment under Comprendil centers on a multi-level task set that yields a Comprendil score, intended to reflect depth of processing and integration of prior knowledge. Tasks include literal questions, inference prompts, and self-explanation or strategy reports, scored on a standardized rubric.

Applications span classroom assessment, adaptive tutoring, and the evaluation of readability in digital content. In research contexts, the framework informs the development of AI reading assistants designed to detect gaps in understanding and to tailor content accordingly.

Reception has been mixed. Proponents argue that Comprendil provides a structured way to operationalize comprehension beyond surface recall, while critics cite reliability concerns, cultural bias, and the need for robust empirical validation across genres and languages.