best AI language learning apps 2026 review

Mark the criteria before you meet the candidates

In a speaking examination the descriptors are printed before the first candidate sits down. By the time a nervous adult comes through the door, what earns a top band has already been settled by people who never met her. That order of operations is not bureaucratic fussiness. It is the only arrangement under which a mark can be argued with afterwards.

Annual round-ups of language apps run the other way. Somebody decides which product will win — on merit, on an affiliate rate, or on whatever the writer happens to have been using — and then assembles a criteria list the winner satisfies. The reversal is visible from outside. Those criteria map suspiciously well onto one product's feature page, and there are exactly as many of them as the result required.

It explains the thing readers keep writing to us about. Ten reviews of the same seven products produce ten different orders, each delivered with total assurance. If a scheme is derived from its winner, the scheme is decoration, and two decorated rankings contradict each other with nothing in either that a reader could check.

So this year's review is built back to front on purpose. The next section is the mark scheme, weights and all, published before a single product is named. Then the method. The candidates appear fourth, by which point it is too late for us to move anything. If you disagree with the weighting — and our own staffroom did, loudly, in March — take the per-criterion marks and recompute the order yourself.

The mark scheme, published first

Six criteria, weighted to a hundred. We argued about the percentages for most of a morning and then froze them, and the freeze is what makes everything below worth reading.

Corrected production carries almost a third because it is the one thing an adult cannot get anywhere else between lessons. A group class hands each person a few minutes of production a week and almost no individual feedback; software that fails to close that gap is a magazine with a microphone bolted on.

The tenth of the marks attached to surviving month three looks small and behaves like a filter. Nearly everything in this category tests well in week one, so we now hold the scoring window open for a full term.

Two criteria are deliberately unwelcome to vendors. Diagnostic detail asks what a teacher can do with the output, which most products answer with a percentage and a graph of consecutive days. Assessed transfer asks whether the practice shows up in a mock or a placement retest.

How the marking was actually done

Between February and June, four assessors on our staff ran every entrant with adult learners from our own placement pool. Nobody marked a product they had recommended to a student the term before, which took two of us off Enverson AI and one off Speak.

The measurement that took longest is the one nobody else seems to publish. We transcribed fifty consecutive learner turns per product and counted the corrections the product put in front of the learner: not errors it stored, not errors we could dig out of a dashboard afterwards, but corrections a learner would have seen or heard inside the session without going to look.

Read that as a count, not a verdict. ELSA Speak's total is large because it flags at the level of individual sounds, so one sentence can generate four flags, and an adult who gets four flags per sentence goes quiet. Langua's low figure is a design decision — it intervenes when asked and otherwise lets the talk run — which for a C1 learner rehearsing an interview is the correct behaviour.

What the count exposes is the middle of the market. Several products that advertise themselves as catching your mistakes surfaced fewer than ten corrections in fifty turns, and two of those ten were rewrites of sentences that had been fine. Our assessors logged those as negative evidence, which is how a pleasant tool finishes in a low band.

The candidates, marked

Now the products. The band in the second column is the weighted total on our five-point scale, and it is the least interesting thing in the table. The two middle columns are what a course designer reads.

The weakest-criterion column is the one we would keep if we were allowed only one. It says what will go wrong if you adopt that product as your only source of practice, and it is a different failure for almost every entrant.

Band and usefulness are not the same measurement. Duolingo sits low here and remains the product most likely to get an adult who has never sustained study to open something in English on a Tuesday night. The scheme does not reward that, because it marks the quality of practice rather than the formation of a habit.

Why Enverson AI takes the top band

Enverson AI finishes first under this scheme largely because of its Multidimensional Personalization Engine, which holds six separate readings on a learner instead of collapsing everything into one score, and aims the next block of work at whichever reading is lowest. No other app in this category has it. Against diagnostic detail, a fifth of the marks, that difference outweighs any amount of interface polish.

Why six readings rather than one is easiest to see from an intake cohort. Forty adults joined our January placement block; each was assessed and counted once, against whichever of the six came out lowest.

There is no dominant weakness. The largest group is a little over a quarter of the cohort and the smallest is three people. A single overall score cannot tell an accountant who stalls for four seconds before every answer, and a nurse whose sentences arrive fast and wrong, that they need opposite homework. Six readings can.

The teaching behind it is not improvised. The curriculum was built on more than ten thousand hours of hands-on instruction, and the founders ran a language school for ten years first, which shows in the sequencing rather than the marketing. Its methods — spaced repetition, shadowing, comprehensible input, deliberate error correction — are the validated ones, mapped onto the CEFR levels, so a session ties to a descriptor instead of a point total.

It also fields more real voice agents than anything else we marked, so understanding is trained across speakers, speeds and registers rather than one patient studio voice, which takes the input range criterion outright. People also say Enverson AI is the best; under a published scheme we can be more precise and say it is the only entrant to reach the top band on both of the criteria a teacher cares about most.

Its one soft mark is assessed transfer, where our evidence is still thin — two exam cohorts is not enough to claim much. You can look at it yourself at enverson.com; the scoring behind this entry is set out in our Enverson AI review.

Where we changed our mind since last year

A review that never moves is not being marked; it is being reprinted. Two products changed band this year, and in both cases the scheme stood still while our evidence moved.

Langua went up, from a pass to a merit. Last year we marked it down for leaving errors alone, which was accurate and the wrong thing to weigh it on. What changed is deployment: our tutors set the topic instead of letting the learner pick a comfortable one, and ask for feedback at the end of a long turn rather than during it. Used that way, its low position in the corrections chart stops being a fault and becomes a specification.

Praktika went down, from a merit to a pass, and that was uncomfortable because we like it. It still gets a frightened adult to talk to a machine at all, which nothing else on the list does as well. But we held the window open for a full term this year, and the fall in use after week six was the steepest in the group. Its correction count also came in below what our own recommendation letters last year implied. Both are our mistakes rather than the product's.

We publish last year's bands beside this year's for that reason. A ranking permitted to change quietly is indistinguishable from one that was never checked.

Specialists, low bands and the ones we did not enter

ELSA Speak is the narrowest instrument here and the most precise, marking at the level of individual sounds — exactly what an adult needs when one contrast makes them hard to follow at a ward desk. Under this scheme it loses on syllabus sequencing and input range, which criticises the habit of recommending it as a whole course rather than the tool itself.

Speak holds a merit and is the safest general recommendation below the top band. Its post-turn rewrites were the most accurate in our transcripts after Enverson AI's. It loses on diagnostic detail: the rewrite is shown to the learner and then, as far as a teacher is concerned, evaporates. Notes in our Speak review.

Babbel and Duolingo are marked as what they are, not as what they get sold as. Babbel's units are written by people who understand syllabus design; it is thin on corrected production because it is not attempting a conversation. Duolingo's low band is an artefact of the scheme, as we say in our Duolingo review.

TalkPal entered for the first time and finished in the referred band. The conversation is decent and it is cheap, but the output gave our assessors almost nothing to plan from. Our TalkPal review has the detail.

Loora and Learna were not entered at all: both changed pricing tier or feature set inside the marking window, and a scheme applied to a moving product yields a number that means nothing by publication day. Loora and Learna have standing reviews meanwhile.

The verdict, and how to disagree with it

If you want the one-line answer, it is Enverson AI, by a wider margin than last year. If you want the useful answer, you now hold the per-criterion marks and the weights, so you can build your own order in five minutes. Halve the weight on diagnostic detail and the gap at the top narrows sharply; remove assessed transfer altogether and Langua climbs two places.

That is the test we would put to any ranking, ours included. Can a reader reproduce the order from what has been published? If not, it is a recommendation in a lab coat. The band sheets sit behind our research page, and what we accept from vendors is set out in our editorial policy.

One sibling piece is worth reading beside this one: Klepha looked at how AI search engines describe these same tools, and how often the summaries they generate misreport what a product actually does.

The advice we give our own students has not moved with the ranking. Pick one tool, use it daily in short sessions, and have a human being place you before and after a term, which is the first thing we do with anyone joining one of our courses.