Two readers paste the same article. One started Spanish last week; the other reads B2 novels on the tram. Blend both texts at 30% and — until now — the word selection logic treated them identically: useful, mid-frequency vocabulary, evenly spread. Good defaults, but level-blind. The beginner met words years ahead of them; the advanced reader met casa and comer, words they'd known since week two.
One question instead of a placement test#
We considered the standard options. A CEFR picker (A1–C2) assumes you know what "B1" means — most people don't. A placement test is friction nobody asked for. So we ask the question the way a person would:
| Your answer | Roughly means | What the blend targets |
|---|---|---|
| Just starting | A1 | The core few hundred words |
| I know the basics | A2 | Everyday vocabulary beyond survival words |
| I can hold conversations | B1 | Mid-frequency words, opinions, narration |
| I'm comfortable reading | B2+ | The long tail — precise, abstract, idiomatic |
You'll see it once during onboarding, and again only if you switch to learning a different language — because your level is per language: comfortable in Spanish says nothing about your Japanese.
If you already know what A2 actually means or where B2 begins, the mapping will feel familiar — but the app never makes you translate yourself into the framework.
The i+1 idea: why "slightly too hard" is exactly right#
The theory here is one of the oldest and most-cited in second-language acquisition: Stephen Krashen's comprehensible input hypothesis — learners acquire language from input pitched at i+1, one notch beyond their current competence i. Too far below and there's nothing to acquire; too far above and there's nothing to anchor to. Blending is arguably the purest delivery mechanism for i+1 ever built: the surrounding sentence in your language is the anchor, and the blended word is the +1.
Level-aware selection turns that from philosophy into practice. Every word in our vocabulary graph carries a picture of how common it is in real usage, and your level tells the engine roughly how far into that vocabulary you already reach. Selection then favors the band just beyond that point: far-too-easy words lose their appeal (for a B2 reader, casa stops being interesting), far-too-hard words wait their turn (an A1 learner won't retain academic abstractions), and the words you're most likely to acquire next come first.
This composes cleanly with mastery-aware blending: mastery handles the words the app has measured you know; your level handles the thousands it hasn't — the vocabulary an intermediate learner walks in with but never saved.
Your answer is a starting point, not a label#
Self-assessment is imperfect — people undersell and oversell themselves. So the level question only steers while the app has nothing better. As your saved words and spaced-repetition history accumulate, LingoBlend measures what your demonstrated vocabulary actually says about your level, and that measurement takes over from the declared one — in both directions, and continuously, because six months from now you won't be the level you are today and nobody remembers to update a setting.
And if the blend ever feels mistuned, the direct control is right there: your level sits in Personal Details, editable per language, any time.