Research into Language Difficulty

CEFR.AI calibrates language difficulty by combining text complexity with task demand.

CEFR.AI triangulation model Equilateral triangle with three evidence anchors: professional materials, CEFR.AI Test scores, and linguistic research. CEFR.AI Calibration Engine Professional Materials CEFR.AI Test Scores Linguistic Research
CEFR.AI Calibration Engine Professional Materials CEFR.AI Test Scores Linguistic Research

How We Calibrate

CEFR.AI Placement Scores

Real learner outcomes from CEFR.AI proprietary assessment.

Take the Test
Students reading in a classroom

Tools Powered by CEFR.AI

Official applications built on the CEFR.AI calibration framework.

Analyse

Analyze English texts with CEFR and GSE-calibrated scoring for fast, consistent level and difficulty insights.

Level Test

Measure learner proficiency with the CEFR.AI placement test, calibrated on the same research framework.

Coming Soon

An open API and app directory are coming soon so teams can build language products on CEFR.AI scoring.

Frameworks we Trust

CEFR (A1-C2) maps to GSE (10-90) so broad levels can be calibrated with finer precision.

Latest Research

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2026-03-17

Framework Note

Zone of Proximal Development in Language Learning

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,...

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2026-03-17

Method Note

Current Model: Score Engine v1

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...

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2024-12-26

Research Note

Why Flesch-Kincaid Falls Short for CEFR Classification

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...

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2023-07-24

Framework Note

The Power of Language Frameworks

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...

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