P-01No real practice partner
Plenty of lessons, no one to actually talk to
Most learning apps lean on video lessons and drill questions. Learners understand a lot, but never build real conversation muscle.
Shipped / Production|AI Solution Brief·GSKJ-2026-CN-EDU-01
/ Case Study · 2026.05
AI 实时对话
Turn “rote drills for Chinese”
into a real-time, interactive
AI learning experience.
Built around how foreigners actually learn Mandarin, we combined LLM dialogue, AI Q&A, smart content generation and edge-inference hardware to rebuild the entire learning loop — listening, speaking, reading, writing and practice — so learners pick up Chinese the way they pick up a conversation.
< 1s
AI dialogue latency
Seconds
Content generation
5+
Learning modules
Software + hardware
Deployment model
中
LLM Response
“Where can I get baozi around here?”
Edge inference · 0.6s
/ 00 · Overview
How the AI learning loop works
An LLM, NLP, ASR and edge-compute stack purpose-built for live Mandarin practice — adaptive to each learner, and stable across hardware and network conditions.
Updated · 2026.05
Live DashboardDAU · 30D
Learner engagement, post-launch
After launchBefore launch
W1W3W6W9W12
< 1s
Dialogue latency
98.4%
Pronunciation accuracy
6 steps
End-to-end loop
< 1s
AI dialogue latency
Edge inference and streaming I/O keep replies conversational.
Seconds
Content generation
One-click vocabulary cards and review slides.
Multilingual
Contextual semantics
English, French, Spanish, Arabic, Japanese and more.
HW + SW
Edge + cloud AI
Keeps working under weak network or fully offline.
Tech Stack
LLMNLPASRTTSRAGKnowledge graphRecommenderEdge computeUser profiling
/ 01 · The Problem
01
For foreign learners, the hard part
isn't the textbook —
it's the lack of real conversation
Our client wanted a Mandarin learning system for overseas markets. In our research, almost every existing product hit the same set of structural problems.
P-02Not adaptive
One curriculum forced on everyone
Learners from different countries and language backgrounds approach Mandarin in very different ways. A single fixed curriculum tanks completion and retention.
P-03No semantic depth
Real Mandarin questions never get answered
Why is it 了 here? What's the difference between 把 and 被? Most apps can't read context, let alone explain the underlying logic the way a teacher would.
P-04Hardware-software gap
Software and hardware don't speak the same language
Our client needed AI baked into their device. Traditional SaaS is slow, network-dependent and laggy on voice — the device experience falls apart.
/ 02 · Live Sample
A foreign learner's
10-minute AI learning journey
We designed a full loop that chains AI dialogue, real-time correction, AI Q&A and content generation. Here's how the system handles an English speaker learning to order food in Mandarin.
● 6 roles
Flow · Learning loop5 steps · ≈10 min
- STEP 01/AI conversation
User:I'd like one chicken rice, please.
AI:AI parses the speech and opens a Mandarin scenario dialogue.
- STEP 02/Pronunciation correction
User:Mispronunciation (tone error)
AI:AI flags the tone and corrects pronunciation in real time.
- STEP 03/Semantic explanation
User:Why “一个” here?
AI:AI explains the measure-word logic in context, not just the rule.
- STEP 04/Smart recommendations
User:Stuck on the ordering scenario
AI:Suggests related topics — paying, asking for directions — to extend the practice.
- STEP 05/Slide auto-generation
User:Session ends
AI:AI generates a review deck so the learner can revisit the key points.
Scenario library · 12+
/ 03 · How We Think
For language learning, we refused to treat AI as
just another chatbot
A lot of AI edtech ships with “AI but no learning system.” Early in the project we mapped three approaches and rebuilt the role AI plays in the loop.
Path A
Recorded video courses
- Mature curriculum
- Solid for input phase
- No interaction
- Low engagement
- Lagging feedback
VERDICT
Useful as a knowledge layer, but doesn't solve the conversation problem.
Path B
Live human tutors
- High engagement
- Strong outcomes
- Expensive
- Hard to scale
- Schedule-bound
VERDICT
Great for high-value deep learning, but not viable for mass-market reach.
Our choice
Path C
AI-driven learning system
- Real-time AI dialogue
- Semantic understanding
- Adaptive learning path
- Auto-generated content
- Hardware-software co-design
- Heavy engineering work
- Needs a data feedback loop
VERDICT
AI becomes an always-on language partner; pair it with curriculum and learning data to form a long-running loop.
/ 04 · How It Works
An interactive, adaptive, continuously improving
AI learning pipeline
We didn't ship a chat window. We split the system into cooperating modules, each owning one part of the learning experience.
STEP 01
AI dialogue engine
Real language scenarios, simulated
Learners talk to the AI in Mandarin — ordering food, asking directions, business conversations, casual chat, classroom interaction. NLP and ASR parse intent and produce context-aware replies in real time.
NLPLLMASRTTS
OUTPUT
Live Mandarin conversation practice
STEP 02
AI Q&A engine
Explain the logic, not just the rule
Context-aware semantic reasoning. When the user asks “why doesn't 是 work here?” the AI explains the contextual difference, not just the rulebook.
Knowledge graphRAGSemantic reasoning
OUTPUT
On-demand language explanations
STEP 03
Personalization engine
AI adjusts the learning path
We adapt content from frequency, error patterns, pronunciation issues, interests and dialogue difficulty. When a learner plateaus on speaking, the system pushes more dialogue practice and trims reading.
User profilingLearning analyticsRecommender
OUTPUT
Personalized study plan
STEP 04
Slide auto-generation
Lessons, structured automatically
At the end of a session, the system aggregates vocabulary, grammar, dialogue and scenario notes, then renders a deck ready for the classroom or solo review.
Content extractionAuto layoutText generation
OUTPUT
Automated study materials
STEP 05
Edge compute + hardware
Device-grade experience, not just cloud APIs
We push parts of the AI workload onto the device to cut latency. Local speech processing, real-time responses, weak-network fallback and tighter data control.
Edge computeLocal cacheReal-time sync engine
OUTPUT
Smoother, more stable device UX
System architecture
Layered architecture — from app to device
App layer / AI layer / Data layer / Device layer — closing the full learning loop.
Edge computeCloud AI
/ 05 · The Result
Learners stopped
consuming Chinese — they started using it
After launch, engagement rose sharply and the learning loop became noticeably more immersive — most visibly in expression, spoken interaction and self-directed practice.
0.0x
Time on platform
0%
Weekly active learners
0%
Share of speaking activity
0%
Drop in error rate
Before vs. after launch
6 dimensions| Dimension | Before | After | |
|---|---|---|---|
| Learning style | Passive lesson playback | Live AI interaction | |
| Engagement | Low | Markedly higher | |
| Feedback loop | Delayed | Real-time | |
| Learning path | Fixed curriculum | AI-adaptive | |
| Spoken Mandarin | Slow improvement | Reinforced by high-frequency practice | |
| Study materials | Manual aggregation | Auto-generated by AI |
Key metric uplift
+ 4 dimEngagement28% → 86%
Speaking activity22% → 78%
Knowledge retention35% → 74%
Daily active users30% → 82%
Aggregated from 12 weeks of post-launch behavior (anonymized sample, trend comparison only).
Client Voice
“Our learners used to plateau in the “I can read it but can't say it” gap. Now the AI keeps the conversation going, corrects them and explains the grammar. The clearest signal is that session length and learning frequency went up across the board.”
— Client project lead
/ 06 · Technical Highlights
Three engineering pillars behind the system — the technical highlights
Edge compute
Local inference cuts response latency, keeps voice interaction conversational, and keeps working under weak network or fully offline conditions.
Local inferenceMillisecond responseOffline fallback
AI-driven learning
AI doesn't just answer questions — it participates in the conversation, recommends, corrects and generates content across the full loop.
DialogueRecommendationCorrectionContent
Hardware + software, end to end
Deep integration with the client's device: voice input, local processing, cloud sync and data protection keep the learning experience stable and continuous.
Voice I/OCloud syncData protection
/ 07 · Where It Fits
The same AI learning architecture
extends to more education scenarios
01
Overseas Mandarin education
AI Mandarin practice partner
02
K-12 English learning
AI spoken English coach
03
Corporate training
AI learning assistant for employees
04
Vocational education
AI skills training system
05
Kids learning hardware
AI companion learning
06
International schools
Multilingual AI learning platform
/ 08 · Design Principles
Core design principles
01
Interaction beats one-way teaching
You don't watch the language. You use it.
02
AI is a learning partner, not a tool
AI shows up across the whole loop, not just at the Q&A step.
03
Personalization beats uniform curricula
The system adapts content from real learner behavior.
04
Hardware-software co-design beats pure SaaS
Real learning needs device, AI and interaction working in concert.
