A new phrase in a real context
Not a detached vocabulary item, but language connected to a moment the learner actually lived.
Research-driven adaptive AI learning technology
Innovative
Based on research
No other app like it*
A new intelligence layer for human-led language learning
Every lesson learns how to teach you better
LingoSide listens to your live lessons, understands what you know, where you struggle and how you express yourself — then turns every conversation into personalized guidance, memory and practice. Your teacher leads the lesson. LingoSide makes every lesson compound.
Already live in the sidecar: live transcription, instant translation, AI reply suggestions — each with its translation — and live semantic RAG, so suggestions are never pulled out of thin air: when your teacher calls back to something from earlier in the lesson, the copilot gets the reference. No other app does this beside a live human lesson.*
* In our market research we found no other product that combines these beside a live human lesson — see the market review below.
In the app today
- Live transcription with instant translation beneath
- AI reply suggestions — always a few, each with its translation
- Live semantic RAG — help grounded in your whole lesson
- Polish · English · Russian — any pair, either direction
- Every lesson transcript saved to your archive
Planned next layer
Durable lesson memory, post-lesson learning and continuity across sessions.
The lesson ends. The signals should not.
A real conversation reveals what no generic course can know.
Every lesson produces valuable evidence about how one person understands, speaks and recalls language. Most of it disappears when the call ends.
A correction from the teacher
The difference between what the learner intended and what they were able to say.
A recurring point of friction
A construction, sound or response pattern that repeatedly slows the conversation down.
Knowledge that stays passive
Words the learner recognizes immediately but cannot yet retrieve when speaking.
Personal meaning
Interests, stories and situations that make future practice relevant instead of generic.
The pattern across many lessons
The most valuable signal — and the one a single transcript cannot reveal on its own.
From conversation to long-term learning
One continuous loop, not five disconnected tools.
The intended innovation is continuity: each stage is designed to use evidence from the same real lessons, while the live experience stays quiet enough for the human conversation to remain central.
See what exists today-
01
Before the lesson
Long-term directionBring the right earlier material back into reach.
Prepare the learner to retrieve useful phrases, unresolved difficulties and relevant context before the next conversation starts.
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02
During the lesson
Working prototypeSupport the moment without taking over the conversation.
Keep source text, translation and contextual reply suggestions in a glanceable sidecar. Finalized lesson context can be archived, and earlier moments from the same session can inform reply suggestions.
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03
After the lesson
Archive now · review plannedLay the foundation for structured learning evidence.
The prototype can preserve finalized session records. The next layer is intended to preserve selected phrases, teacher-confirmed corrections and evidence-linked moments for recap and practice.
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04
Between lessons
Planned next layerPractice through retrieval, not passive re-reading.
Revisit selected material at useful intervals and in the context in which it first mattered, rather than generating another generic list of exercises.
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05
The next lesson
Long-term directionLet the next conversation build on the last one.
Use earlier learning evidence to support better decisions for both learner and teacher — without turning the lesson into a dashboard or an automated course.
Personal learning memory
Beyond a transcript archive: toward a learning-evidence model over time.
Today, when configured, the prototype can retrieve relevant earlier fragments from the ongoing lesson to inform reply suggestions, and it can preserve a session archive. Durable cross-lesson memory is the next product layer.
That memory is intended to stay evidence-backed and inspectable — not pretend to know everything about a learner.
Planned next layer
What the learner model is designed to represent
Language knowledge
Known and unknown constructions, active and passive vocabulary, useful chunks.
Patterns and friction
Recurring errors, hesitation points and material that has not yet become retrievable.
Personal context
Topics, interests and situations that can make future practice meaningful.
Change over time
What has become easier, what keeps returning and what is worth retrieving next.
Evidence before inference
Every useful memory item should remain connected to the lesson moment that supports it.
Useful, not exhaustive
The goal is not to retain every word. It is to preserve the signals that can improve future learning.
Inspectable and controllable
Durable memory must be designed with clear review, deletion and retention controls before broad use.
Research-informed by design
Many disciplines. One practical question: what helps this learner retain and reuse language?
LingoSide draws from dozens of disciplines and research areas. They are not a technology checklist; together, they define how the system should pay attention, capture and structure evidence, and decide what to bring back.
01
How memory lasts
Research on forgetting, retrieval practice, spacing, consolidation and metacognition will guide how future review decides when a lesson moment should return and what effort creates durable recall.
- Memory and forgetting
- Spaced retrieval
- Metacognition
02
How languages are acquired
Second-language acquisition, psycholinguistics and language pedagogy shape how input, output, corrective feedback, vocabulary and meaningful interaction work together.
- Second-language acquisition
- Corrective feedback
- Language pedagogy
03
How speech is understood and produced
Speech recognition, listening, phonetics, phonemes, pronunciation and prosody inform what could be observed responsibly in future speech analysis — and where the system should stay silent.
- Speech recognition
- Phonetics and prosody
04
How learning adapts from evidence
Knowledge tracing, adaptive learning, error classification and learning analytics offer ways to update practice from observed performance rather than a fixed course sequence.
- Learner evidence
- Knowledge tracing
05
How useful context is extracted
Conversation analysis, NLP, semantic search, retrieval and generative models help structure what happened — while human-computer interaction keeps that analysis from competing with the lesson.
- Semantic search and RAG
- Conversation analysis
Research is a design constraint, not a badge. It helps decide what belongs live, what belongs after the lesson, what needs validation and what should not be built yet.
Stress-tested, not cherry-picked
Built on research we first tried to break.
No cherry-picking. We gathered the science of how people really learn languages into one brief — then tried hard to prove ourselves wrong. Only the claims that survived get to shape LingoSide.
23
research areas reviewed, from memory science to speech decoding
261
verifiable claims extracted, each tied to a published source
171
unique sources, with meta-analyses weighted first
133
feature ideas mapped from the evidence into a phased plan
Every key claim had to pass three simple tests: Is the research solid? Has it been repeated by other scientists? Does it apply to a real online lesson? Here is what happened to the 30 claims our product depends on:
18
Held up
Passed all three tests. We build on these.
9
Promising
One test raised doubts. We use them carefully, not blindly.
3
Busted
Popular beliefs that failed the tests. They stay out of the product.
Captions turbo-charge listening
With captions in the language you’re learning, people simply understand much more of what they hear.
In studies: g ≈ 0.99 — one of the largest effects in language research.
→ That’s why live captions are the heart of the sidecar.
Recalling beats re-reading
Pulling a phrase from memory strengthens it far more than reading it one more time — and the gap widens over weeks.
Meta-analysis of 188 experiments: g = +0.51 vs re-reading.
→ Review will quiz you gently instead of showing lists.
Little & often beats cramming
Short practice spread over days wins against one long session — especially weeks later, when it actually matters.
Meta-analysis: 48 experiments, 3,411 learners, medium-to-large effect.
→ Practice lands between lessons, never inside them.
Your own language is a ramp
Quick hints in your native language speed up beginners — and matter less and less as you advance.
g = 0.33; the advantage shrinks with proficiency.
→ Translation appears on demand and quietly steps back as you grow.
The stronger the evidence, the bolder the feature. Solid findings become defaults, promising ones become options — and busted myths stay out, no matter how popular they are.
Under the hood
A copilot that remembers your lesson — while it’s still happening.
Most AI tools forget a conversation the moment it scrolls away. The LingoSide prototype is engineered around a different idea: the lesson itself becomes a live, searchable memory the AI can draw on — in real time, and later.
Lesson audio
Live captions
Who said what
Translation
Lesson memory
Grounded help
The core innovation
Working prototypeIn-session semantic memory (RAG)
RAG — retrieval-augmented generation — is a fancy name for a simple idea: before the AI helps you, it first looks up what actually happened in your lesson. And “semantic” means it searches by meaning, not exact wording — you never have to remember the precise word. Say “trip”, and it finds the moment you talked about cancelling a journey. Here is how it works:
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1
Listen & capture
Every finished sentence from the conversation becomes a small snippet of lesson memory.
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2
Understand & index
Each snippet gets a numeric “meaning fingerprint” (an embedding) and joins a live index of the lesson — organised by meaning, not just words.
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3
Recall & help
When you ask for help, the sidecar retrieves the earlier moments closest in meaning and hands them to the AI — so suggestions build on your lesson, not on generic phrases.
Every AI reply suggestion is built on this memory — grounded in what was really said, not invented.
Help stays on-topic in minute 55 just like in minute 5.
The AI reads a small, relevant slice — so it stays fast and affordable.
Planned next layer
Today this memory lives for one lesson; every session is already archived. The next layer extends the same retrieval across all your lessons — so “you met this phrase in lesson 12” becomes something the copilot can actually say.
A summary that never loses the thread
Working prototypeAn hour of talking is too much for any AI to re-read every time. So the sidecar keeps folding older lines into a running summary while holding the freshest lines word-for-word — the thread of the conversation survives, and responses stay quick.
Captions that settle, then become useful
Working prototypeA draft line appears the moment you speak and may still flicker; once the system is confident, the line freezes. Only frozen lines get translated, archived and made clickable — and each one is labelled with who said it, teacher or learner.
Quiet by engineering, not by promise
Working prototypeWhen the system is unsure, it stays silent instead of guessing. Speech, translation and AI providers sit behind neutral adapters, so no single vendor is load-bearing — and if a cloud service runs dry mid-lesson, that layer switches itself off gracefully instead of nagging you with errors.
For teachers, tutors and schools
More continuity for the teacher. More attention for the learner.
LingoSide is designed to sit beside human-led teaching, not replace it. The learner remains in a real relationship with a teacher or conversation partner; the system carries forward the evidence that is difficult to track across isolated calls. That cross-lesson continuity is a planned product layer.
Discuss a future teacher or school pilotThe responsibility stays clear
Human-led by design
The planned system is intended to handle
- continuity across lesson evidence
- structuring selected lesson material
- surfacing patterns for review
- preparing context for future practice
The teacher remains responsible for
- judgment and interpretation
- the learner relationship
- instruction and feedback
- the direction of the lesson
The product is being designed to carry continuity and analysis. The teacher keeps judgment, relationship and instruction.
A different operating model
Built around the lessons you already have.
LingoSide does not need to turn into another course, replace the human lesson or stop at a searchable transcript. It is designed to connect those lesson moments into a learning process.
| Model | Standard language app | Online lesson | Transcription tool | LingoSide |
|---|---|---|---|---|
| Primary input | Pre-built course content | A real human conversation | Audio or a call recording | Designed around real lesson audio and finalized language |
| Live role | A separate learning activity | The teacher leads and adapts | Captures what was said | Working prototype: quiet support beside the human-led lesson |
| What happens after | Continue the course path | Depends on manual notes and preparation | Search or reread the record | Archive today; planned learning layer turns selected material into future practice |
| Source of adaptation | Answers inside the app | Teacher observation and judgment | No learning model by default | Planned: observed lesson history, with the teacher still in control |
| Continuity | Inside its own curriculum | Held by the teacher and learner | A record, not a learning model | Designed to connect successive real lessons |
The LingoSide column describes the product model. Cross-lesson memory and adaptive review are in development, not presented here as generally available features.
2025–2026 market review
Meeting tools transcribe. AI tutors replace. The live lesson is left alone.
We reviewed the current landscape of live-caption tools, AI language tutors and marketplace add-ons, and verified each product claim against vendor documentation. A clear pattern emerged — and with it, a clear gap.
Meeting caption tools
Built for meetings, not learning
Translated captions in video-call platforms and AI meeting notetakers stream a raw wall of parallel text or notes. There is no learning design, no dosing — and when the call ends, nothing comes back.
No pedagogy, no review, often behind business paywalls.
Marketplace AI add-ons
Around the lesson, never inside it
Tutoring platforms are adding AI summaries and practice exercises — but deliberately before and after the lesson. The live hour itself, where the richest learning evidence appears, stays untouched.
No live support, no captions, no spaced-repetition engine.
AI conversation tutors
Practice with the AI, not your teacher
Speaking apps offer roleplay with an AI partner instead of supporting your real tutor. Useful for extra practice — but none of them sits beside a live human lesson while it happens.
Replaces the conversation LingoSide is built to protect.
The gap LingoSide is built for
Quiet during the lesson. Working after it.
- Glanceable captions beside your live human lesson, translation dosed and on demand
- The lesson itself becomes your review material
- Practice scheduled on the memory science above
In our review, no product combined live in-lesson support with an evidence-based review loop.
Observations from our 2025–2026 competitive research, checked against vendor documentation at the time of review. Product capabilities and pricing change quickly.
An honest product horizon
A working live foundation. A larger learning system in progress.
The long-term vision is ambitious: a system that can observe learning evidence over months and help the learner and teacher make better decisions. The current product is an early, focused part of that system.
01
Working prototype
Live lesson foundation
- Windows-first compact sidecar
- Live source transcription
- Translation beneath the source
- Contextual reply and help suggestions
- Session archive and current-lesson context
02
Planned next layer
Learning continuity
- Durable memory across lessons
- Evidence-backed recap and saved material
- Retrieval practice and spaced review
- Patterns across recurring lesson moments
- Clear memory and retention controls
03
Long-term direction
Adaptive learning intelligence
- A learner evidence model that develops over months
- Context prepared before the next lesson
- Practice adapted to history and retrieval
- Useful patterns surfaced to the teacher
- Continuity across successive lessons
Clear scope, no theatre
Questions worth answering early.
LingoSide is at an early product stage. The live foundation is real; the durable learning system is the direction we are planning and developing for future validation.
Is LingoSide available today?
A Windows-first working prototype exists for live lessons, with source transcription, translation, contextual reply suggestions and session archiving. The waitlist provides updates about future testing and early access; signing up does not guarantee access.
Does it already remember previous lessons?
Not yet. The current system can use semantic context from the ongoing lesson and save a session record. Durable cross-lesson memory, learner evidence and adaptive review are being developed.
Is this a transcription or translation product?
Those capabilities are the live foundation, not the final product category. The larger goal is to connect real lesson evidence to review, retrieval and future lessons.
What is actually under the hood?
Streaming speech recognition with two-phase captions (a draft that settles into a final line), speaker attribution, instant translation — and an in-session semantic memory (RAG) that lets AI help build on what was really said in the current lesson. Providers sit behind neutral adapters, so no single vendor is load-bearing. The technology section walks through it.
Does LingoSide replace the teacher?
No. The teacher or conversation partner leads the interaction. LingoSide is designed to handle continuity and analysis while leaving judgment, relationship and instruction with the human teacher.
Who is early access for?
Primarily people learning with a teacher, tutor or conversation partner. We also welcome teachers and language schools interested in discussing future pilot use cases.
Which platforms and languages are in scope?
The prototype is Windows-first. Its current session configuration includes English, Polish and Russian. Android is a later direction, and language coverage will expand only as the live experience is validated.
How should lesson data and privacy be handled?
Live lesson audio may be sent to configured cloud speech-recognition services. Transcript excerpts may also be sent to configured translation or model providers, and finalized session data can be archived. Clear consent, retention, inspection and deletion controls are requirements for the durable memory layer — not claims we make before those controls are ready.
Early access
Help shape a system where lessons build on one another.
Join the waitlist for future LingoSide testing and early-access updates. We are looking for learners who already take live lessons and care about what happens between one conversation and the next.
Learners
Teachers
Pilot partners
We’ll use this address for LingoSide testing and early-access updates. Joining does not guarantee an invitation or launch date.
For teachers and partners
Interested in a future pilot?
If you teach online, run a language school or want to explore a learning-continuity use case, tell us about your lessons and what currently gets lost between them.
This form is for pilot conversations and partnerships. Learners can use the waitlist above.