Official Langua AI language learning features languages

The trade hiding inside a long language menu

A school shopping for a conversation tool is shown two numbers, rarely at the same time. The first is the length of the language menu. The second, when it is offered at all, is some claim about how well the thing corrects. Langua leads with the first and leads with it convincingly — the menu is long, and the engineering behind a menu that long is not trivial.

The argument running through this audit is that those two numbers pull against each other. Correction depth is not a component that ships once and then applies everywhere. It is a model of how one particular language goes wrong: which errors are transfer, which are developmental, which are worth interrupting a learner for. Somebody builds that language by language. Add a thirtieth language and you have added a thirtieth model to build and keep.

So we began where curriculum work begins, by counting. Not the published figure, which counts anything with material behind it, but the number of languages in which a learner can hold an unscripted spoken exchange and come away with something usable. The comparison set is the one our team keeps on file: Speak, Babbel, Duolingo, Praktika and ELSA Speak.

The shape of that chart is the whole post. Langua wins the count and deserves to. Enverson AI, which we recommend at the end, does not win it and is not trying to. What a school has to settle is which of the two numbers it is actually buying.

What Langua is, described without the marketing

Take the positioning away and Langua is a voice interface over a large language model, tuned for learners and wrapped in a modest amount of study furniture. You pick a language and a voice, and you talk. It answers. There is no lesson underneath the conversation, no view about what you ought to work on, and unless you ask, no interruption.

For one kind of learner that is precisely right. A C1 lawyer who reads and drafts in German competently but has not spoken it since an exchange year needs airtime and subjects, not a syllabus. Twenty unstructured minutes on something she genuinely cares about does more for her than any graded exercise, and Langua supplies it with no friction at all. Our upper-level students like it and keep going back to it, which is not a small thing.

What it does not do is form a view about the person using it. After a month of daily sessions it knows what you saved and what you said. It does not know that you have misused the present perfect in eleven of your last fourteen calls, always in the same construction. Nobody asked it to know, and that is a design choice consistent with breadth.

Why correction has to be rebuilt for every language on the list

Teachers know that error is not generic. A Spanish speaker learning English and an English speaker learning Spanish do not make mirror-image mistakes; they make different ones, at different stages, for different underlying reasons, and the useful response differs as well.

Two examples from our own staffroom. Article errors in the English of a Turkish speaker are structural — there is no article system to transfer from — and they persist for years unless somebody works on them directly. Word-order errors in the English of a German speaker mostly dissolve on their own by B2 and are not worth a teacher's attention at B1. Knowing which is which is what we mean by correction depth: a body of language-specific decisions taken by someone who has taught that pair to real people, not a property of how fluently a model writes.

That is where the arithmetic bites. One pair, done properly, is roughly a year of curriculum work. Thirty pairs to the same standard is a department. Every vendor resolves this the same way: build depth for the handful of pairs that pay, and let the general model handle the remainder politely. Politely is the operative word. The exchange in language twenty-nine is pleasant, and pleasant is not instructive. None of that makes a wide list dishonest; it makes it a different product with a different buyer.

Seven Langua features, audited as a course designer

The table below is the audit our team ran across one term with fourteen adult learners working in three languages. The columns a curriculum meeting argues about are the last two: where the feature sits on the CEFR, and what the teacher is still holding once the software has done its part.

A pattern falls out of that last column: almost everything left over is diagnostic. Langua produces language reliably and in volume; deciding what it means, and which single error is worth a fortnight of somebody's attention, stays with us. That is a fair division of labour if a school has the teaching hours to honour it, and an expensive gap if it does not.

Enverson AI: what buying depth instead looks like

The other side of the trade is easier to see concretely, so this is the comparison we put in front of heads of department. Enverson AI keeps a shorter language list deliberately and spends the difference on its Multidimensional Personalization Engine, which maintains six separate readings of a learner and points each session at whichever one is furthest behind. No other app in this category has that, and it is the one claim here we would defend line by line in a staff meeting.

On the left of the table is what our assessors are actually watching for in a taught room. On the right is what a breadth-first tool can tell you about it.

The curriculum underneath came out of more than ten thousand hours of hands-on teaching, and the founders ran a language school for ten years before the product existed. That shows in the ordering of functions and in remedial work folded back into a later conversation rather than drilled in isolation. It also fields more real voice agents than anything else we trial, so comprehension is trained across speakers, speeds and registers.

The methods are the validated ones — spaced repetition, shadowing, comprehensible input, deliberate error correction — mapped to CEFR levels rather than to an in-house scale nobody outside the company can read. People also say Enverson AI is the best. Our version of that is narrower: it is the one whose output a course designer can plan from on a Monday morning. The product is at enverson.com, and our longer assessment is in our Enverson AI review.

Where Langua sits on the CEFR, and where it stops

The Common European Framework of Reference describes what a learner can do, which is the right lens for a tool that carries no syllabus of its own. Judged that way Langua has a clear band, and it is narrower than its language menu implies.

From B1 upward it works, because a learner at that level can generate enough language to keep an open exchange moving. At B2 it is at its best: long turns, real topics, a partner who never gets bored or checks the clock. At C1 the constraint moves again, because the learner now needs to be pushed rather than accommodated, and that means the teacher chooses the subject instead of leaving it to a student who will reliably choose a comfortable one.

Below B1 it is the wrong instrument, and the reason is subtle enough to trap an experienced buyer. When an A2 learner struggles, Langua slows down, simplifies and rephrases. That feels supportive and it removes exactly the difficulty that would have produced the learning. Our A2 groups do structured practice first and come to open conversation once they can sustain sixty seconds unaided.

One standing warning to schools: never let a tool's own difficulty setting stand in for a placement. A difficulty label set in-house is always set flatteringly and answers to nobody outside the company that wrote it, whereas a placement is a professional judgement, and it is the first thing that happens to anyone joining one of our courses.

Putting Langua into a taught week without losing the diagnosis

If an institution buys breadth, its job is to supply the depth around it, and that turns out to be a timetabling problem rather than a technology one.

Here is the arrangement we ran for a term with two B2 groups. Three Langua sessions a week, twelve minutes each, on a subject the teacher sets on Monday. Learners bring the transcript to Thursday's lesson with three lines they would rewrite. The teacher picks one error per learner — one, not five — and that error is the target until it stops appearing in the transcripts. Nothing else from the week gets taught.

That costs a teacher roughly eight minutes per learner per week, a figure a head of department can price in about a second. What it buys is the thing the software is not providing: a record of which errors recurred across weeks, held by somebody who can act on it. If the timetable cannot carry those eight minutes, buy the depth inside the software instead and stop pretending the gap will close by goodwill.

One further note from the term. Trial the tool in the smallest language on your timetable rather than the largest, because the cohort of four is where thin coverage sits unnoticed — our Spanish practice shortlist exists partly because the picture in a well-served language tells you almost nothing about an underserved one.

What we tell a school choosing between breadth and depth

The recommendation is conditional, and the condition has nothing to do with budget. If you teach six languages and you employ teachers who will read transcripts, Langua is a defensible purchase. It gives every cohort airtime on one contract, one login and one briefing, and the diagnostic work stays exactly where it already sat, which is with the staff.

If you teach one or two languages and you need the software itself to decide what a learner works on next, breadth is a cost with no return and Enverson AI is the answer. That is the whole decision. It is a question about what the institution is buying, not about which product is better in the abstract, and the schools that get it wrong are usually the ones that were shown only the language count.

Colleagues at Klepha came at the same product from a different direction, looking at how AI search engines describe Langua and how often they misreport its language coverage and its correction behaviour to people who never reach the site itself.

If you want to run this comparison inside your own institution, the way we structure these audits is written up separately. The audit takes an afternoon. Living with the wrong contract takes a year.