You downloaded the app. You went through the placement flow, answered some questions, got assigned a level. And then it started you on content you mastered years ago.
If you write client-facing reports in English daily, present to leadership in English, and run meetings in English — sitting through "business vocabulary for intermediate learners" isn't just a waste of time. It's a signal that the app has no model of your actual problem. It knows you're not a complete beginner. It doesn't know what being advanced and still experiencing a gap actually looks like.
So you gave it a week, maybe two, and then you stopped. Because the content wasn't wrong in an insulting way — it was wrong in a more fundamental way. It was designed for someone else.
The apps aren't bad. They're built for a different problem.
Duolingo is extraordinarily good at what it's designed for: getting someone from zero to conversational in a new language, through gamified repetition that keeps engagement high. For a learner going from A1 to B2, it's a genuinely effective tool. For someone who has been working in English for a decade, it has nothing to offer — not because the content is beneath them, but because the entire pedagogical model is calibrated for a different kind of gap.
Speak is designed to reduce conversation anxiety — to get learners speaking out loud, building the confidence that comes from actually producing the language rather than just studying it. For someone who is genuinely hesitant to speak English at all, that's the right intervention. For someone who runs client calls in English every week, the problem isn't conversation anxiety. It's something more specific.
ChatGPT is the most sophisticated of the three, and the one most likely to feel promising at first. You can have complex conversations, get corrections, ask about usage. It's useful. But it has no infrastructure for the specific problem that advanced professionals face. Every session starts from zero. There's no memory of which words you've attempted, which ones have started to feel more natural, which ones keep collapsing under the pressure of a real meeting. The conversation is as good as you make it, which means the work of designing practice for your specific gap falls entirely on you — and if you knew exactly how to do that, you'd have done it already.
The structural mismatch with all of them is the same: they're built to guide someone through a defined body of knowledge, from low to high. The advanced professional doesn't need that. The knowledge is already there.
The actual problem
If you're past B2 and still experiencing a gap in professional English, the gap almost certainly isn't general fluency. It's activation.
You say "reduce" when you mean "mitigate." "Complicated" when you mean "nuanced." "Warning" when you mean "caveat." In a meeting, on a client call, in a live negotiation, the precise word doesn't arrive in time and you reach for the safe alternative. Not because you don't know the better word — you do, you've seen it dozens of times — but because knowing a word and being able to deploy it under pressure are different things. The gap between passive vocabulary and active vocabulary isn't one that general conversation practice closes.
This is the specific problem that general AI language tools have no infrastructure for. Three reasons.
No word-level progress tracking. Activation isn't binary. A word moves through phases: first encounter, early attempts, partial confidence, reliable deployment, automatic use under pressure. Where you are with a specific word determines what kind of practice you need. A word you've never produced needs different drilling than one you've used three times but still hesitate on, which needs different practice than one that's nearly automatic but collapses in high-stakes moments. Generic tools have no memory of where you are with any specific word. Every session starts from the same place, which means there's no system moving a word through the phases it needs to pass through to become automatic.
No phase-aware exercise selection. Even if an AI tool remembers that you've worked on "mitigate" before, it can't calibrate the exercise to where you actually are with that word. Early-phase practice should focus on accurate use in simple contexts. Mid-phase practice should introduce variability — different scenarios, different registers. Late-phase practice should simulate the actual pressure conditions where the word needs to be available automatically. Generic conversation practice doesn't make those distinctions. It gives you roughly the same interaction regardless of where you are with a word, which means you plateau.
Feedback lacks register nuance. AI conversation tools can tell you if something is grammatically correct. What they can't reliably tell you is whether "mitigate" sounds natural in this specific professional context, whether "caveat" fits the register of this particular conversation, whether your phrasing reads as senior or as slightly junior. That calibration requires feedback specifically trained on professional language — on the difference between correct and native-sounding, between technically accurate and contextually appropriate. General conversation AI isn't built for that distinction.
Why a fixed curriculum path doesn't work at this level
There's a more fundamental mismatch between how these apps are structured and what advanced professionals actually need.
Apps are built around content progression: a defined set of things to learn, ordered from simpler to more complex, delivered in sequence. That model works well when someone doesn't know what they need to learn — when the learning path itself is valuable guidance.
At the advanced professional level, the words worth practicing are different for every person. A consultant needs "contingency," "guardrails," and "flag." A tech product manager needs "traction," "scope," and "fast-track." A finance professional needs "headroom," "earmark," and "ring-fence." None of them need a general professional vocabulary course — they need targeted activation of a self-identified, person-specific vocabulary gap. The words that keep collapsing under pressure are the words worth practicing. A fixed curriculum path has no mechanism for that.
This is why the standard advice doesn't work for advanced learners — and why it continues not to work even when the delivery mechanism is sophisticated AI. The problem isn't content delivery. It's targeted activation with progress tracking, phase-aware practice, and register-calibrated feedback.
That's a different product category from what the apps are building — and it's the category that vocabulary test scores and general proficiency metrics systematically fail to measure.
Lyra Practice is built for exactly this gap. There's a free tier if you want to see how it works: lyrapractice.com.