Zone of Proximal Development in Language Learning
Published: 2026-03-17 | Authors: CEFR.AI | Framework Note
The Zone of Proximal Development (ZPD) is one of the most useful ideas in language education, but it is often described too vaguely to guide real decisions. In practical terms, ZPD is the space between what a learner can do independently and what they can do with support. For CEFR.AI,...
Current Model: Score Engine v1
Published: 2026-03-17 | Authors: CEFR.AI | Method Note
This note documents the current production scoring model as implemented in the score engine API (`meta.version = legacy-gse-v1`). The goal is methodological transparency: what v1 does well, what it does not do, and what evidence currently supports it. v1 estimates text difficulty from text-only inputs. It combines: - Flesch Reading...
Why Flesch-Kincaid Falls Short for CEFR Classification
Published: 2024-12-26 | Authors: CEFR.AI | Research Note
Can native-speaker readability metrics really predict CEFR levels? Many online ESL/EFL text analysis tools have defaulted to using the Flesch-Kincaid readability index, simply because no specialized algorithms exist for language learners. Using 59 graded texts, we systematically test whether this widely-adopted solution actually works. While our investigation does reveal a...
The Power of Language Frameworks
Published: 2023-07-24 | Authors: CEFR.AI | Framework Note
Language frameworks are not optional in language learning; they are the minimum structure required for reliable decisions. If we want to match learners with texts and tasks that are challenging but manageable, we need shared standards for what “difficulty” means. This note is a simple introduction to language frameworks: why...