a language by passively turning pages in a textbook.
You actually progress when the language talks back to you.
Whenever you see images, hear real sentences, attempt to speak, and get feedback, every little thing finally clicks in your head.
Up to now, you needed a teacher by your side in any respect times to get that sort of feedback.
Today, generative AI can play that role in your phone or computer, like an AI language tutor you should use any time.
Once I began learning Mandarin ten years ago, I saw many foreigners struggling to be understood by locals in on a regular basis conversations due to poor pronunciation.
It convinced me that without good pronunciation, a wealthy vocabulary is useless.
I still remember sitting in my apartment in Shanghai, repeating the identical sentence many times, without anyone to correct me.
Years later, after I discovered generative AI, I remembered the engineer in China who was combating grammar books and tones.
I wanted to construct tools that might have helped me prior to now.
As a startup founder, I don’t have much free time, so I needed a solution to construct and test recent tools quickly.
That’s the reason I turned to n8n to construct assistants that might have made my Chinese practice much easier.
In this text, I’ll show how I exploit n8n and multimodal AI to construct a “study partners” for language learning that:
- Correct my pronunciation using Text-to-speech capabilities
- Create exercises to review vocabulary lists
- Generate images as an example words or contexts for flash-card style practice
Together, they show how AI and low-code platforms like n8n can support anyone learning a fancy language.
Even with each day usage, all of those together cost lower than 1 euro per thirty days.
AI For Pronunciation And Oral Comprehension
My name is Samir, a supply chain skilled who struggled with Mandarin during his six-year stay in China.
Let me introduce you to Yin, the AI-powered Language coach I developed last week.
This can be a web application I designed to support my Chinese learning journey after greater than five years without practising.
It includes three features:
- Pronunciation Exercises
- Multiple Alternative Questions (MCQ)
- Flash Cards
I’ll use each feature to reveal how I exploit multimodal AI to enhance my reading comprehension, listening, and pronunciation in Mandarin.
Why is pronunciation in Mandarin so Essential?
Let me share an actual story from China to spotlight the importance of using the proper tone in Mandarin.
At some point, I used to be invited to a job interview at the most important Chinese express company, valued at billions.
The complete conversation was in Chinese.
I had rigorously prepared my sentences, highlighting how I used data science to enhance warehouse operations.
At one point, I desired to say:
The verb “picking” means taking goods from shelves or racks in a warehouse.
In Chinese, my colleagues used the verb 拣货 (jiǎn huò) to explain this process.
But as a substitute of claiming jiǎn huò, I said jiàn huò.
Which is a very different word that you actually don’t wish to use in a job interview.
To maintain it polite here, let’s say is a rude word.
The manager burst out laughing.
I didn’t understand why until I debriefed with the headhunter later and repeated the sentence for her.
That moment taught me that pronunciation in Chinese isn’t nearly sounding natural.
You’ll be able to know 1000’s of words, but in case your tone is incorrect, people won’t understand you.
Because of this the primary feature of my app is an AI pronunciation coach.
Using Speech-to-Text Recognition to Practise
Using speech-to-text and reasoning, the app listens to what I say, compares it with the goal sentence, and provides feedback on which tones or syllables were off.
The main target here is on improving my pronunciation of logistics and provide chain terms (my field of experience).
For every word, we’ve:
- The word in Simplified Mandarin Characters:
- The sentence used to practise my pronunciation:
- The English translation:
For beginners, we are able to even add phonetics (Mandarin pinyin) using the toggle.
Methods to practice pronunciation?
I just should press the mic button at the underside to record my sentence.
The recording is robotically sent to the backend for evaluation that compares my pronunciation with the proper one.
A number of seconds later, I received my feedback.
The feedback is sort of detailed; it focuses on the words that you simply mispronounced.
It’s nearly like having a private teacher correcting me in real time, except this one never gets drained.
After all, this won’t replace an excellent teacher in a one-on-one lesson, but it may possibly enable you to practise after classes.
Once I began learning Mandarin, I used to spend evenings (after work) alone, repeating easy sentences to familiarise myself with the nuances of tones.
I didn’t have a feedback loop on the time; this tool would have been very helpful.
How does it work?
Text-to-speech and reasoning capabilities of GenAI
The backend is a straightforward n8n workflow connected to the frontend via a webhook.
The text-to-speech capabilities are used to transcribe the audio file sent by the front end into phonetics (pinyin).
The output of this Gemini audio transcription node includes the phonetics:
[
{
"content": {
"parts": [
{
"text": "zuò pǐn huò zǒnggòng fàng zài èrshí ge tuōpán shàng.n"
}
],
"role": "model"
},
"finishReason": "STOP",
"avgLogprobs": -0.16858814502584524
}
]This pinyin is then sent to the AI node Pronounciation Evaluation together with the goal pronunciation.
In this instance, I mispronounced the penultimate word.
That is precisely what the agent mentioned in his feedback.
This shows how we are able to use text-to-speech capabilities, combined with the reasoning of generative AI models, to enhance our pronunciation.
This might be adapted to any language.
What about image generation and speetch-to-text?
Generative AI for Content Generation
Should you observe the user interface of the appliance, you notice that every word has:
- An illustrative Image
- A sentence for the context
- Audio transcription available via the microphone icons
This content is generated using AI models to supply a wide range of teaching materials for the second feature: flashcards.
Text-to-Speech Solutions
An excellent solution to practise pronunciation is to listen and repeat.
Due to this fact, before recording my sentence, I can learn the way to pronounce the word using this primary speech-to-text feature.
For this, I exploit Google’s Text-to-Speech API because it is pretty convenient and free.
from gtts import gTTS
def generate_speech(text: str, lang: str):
filename = f"{uuid4().hex}.mp3"
filepath = f"./data/gtts/{filename}"
tts = gTTS(text=text, lang=lang)
tts.save(filepath)With a few lines of code, you possibly can generate the text-to-speech of any word using the correct language code.
This is strictly what I utilized in the tool to generate flashcards that I presented on Towards Data Science three years ago.
The concept on the time was to enhance my listening comprehension by adding audio to the flashcard answers.
What about long sentences?
The issue with Google Text-to-speech is the robotic voice.
Fortunately, we’ve eleven labs.
The workflow above is connected to the app via webhook.
The Eleven labs node that takes the output of the AI Agent Generate Example to generate the audio version of the sentence.
The user can now hearken to the sentence pronounced “like” a native speaker.
What’s remaining? Questions and illustrations …
Teaching material generation
As explained within the previous section, the sentences are also generated using AI.
The AI Agent node, powered by Gemini, takes the word to review as input and uses the system prompt below to generate a sentence.
You might be a Chinese language tutor for professionals.
Given a Chinese word, you MUST return a JSON object with EXACTLY these keys:
- "sentence": a brief Chinese sentence using the word in a business or
daily-life context
- "pinyin": the pinyin of the complete sentence
- "english": the English translation of the sentence
Return ONLY valid JSON. No explanations, no backticks, no extra text.
Example:
{
"sentence": "我去仓库检查货物。",
"pinyin": "Wǒ qù cāngkù jiǎnchá huòwù.",
"english": "I am going to the warehouse to examine the products."
}That ensures an almost infinite number of exercises.
And the cherry on the cake is the image generated with Gemini’s Nano Banana to assist us connect a word to its context.
After learning 1000’s of Chinese characters, I noticed that images help with memorising recent words.
That is precisely what I exploit within the flashcards feature.
The n8n backend provides to the front-end:
- The word in Chinese that you wish to learn with pinyin and English translation
- An example sentence and its translation generated by GPT
- An illustrative image generated by Gemini
The front end then manages the card-flipping mechanism.
If you wish to recreate this solution tailored to your needs, I even have shared an identical workflow on my GitHub.
Do you want multiple-choices questions? Gen AI may also help!
Generate Exercises from a vocabulary list
For the last feature, we generate multiple-choice inquiries to learn the identical vocabulary list.
We ask Gemini to generate questions from the vocabulary list, using multiple-choice options with just one correct answer.
[
{
"output": {
"question": "Which of the following is the correct Chinese translation for 'Variable Pricing'? Please answer with A, B, C, or D.",
"options": {
"A": "仓库",
"B": "可变定价",
"C": "卡车司机",
"D": "投标"
},
"correct": "B",
"right_feedback": "Great job! 可变定价 (kě biàn dìng jià) means Variable Pricing.",
"wrong_feedback": "Oops! The correct answer is B: 可变定价 (kě biàn dìng jià), which means Variable Pricing."
}
}
]The front-end uses this output to supply the questions with adapted feedback.
The backend of this feature is predicated on an n8n workflow that I also shared on my GitHub: AI-Powered Language Teacher using GPT.
Conclusion
I developed this app to experiment with how AI could enhance my learning capabilities.
After nearly five years without speaking Chinese, this multimodal AI assistant has proven to be an excellent help.
The complete backend is built on n8n for rapid prototyping and seamless integration.
You aren’t conversant in n8n and wish to learn?
I even have a whole tutorial, designed for beginners, on my YouTube channel that may guide you from instance creation to credential setup.
After this tutorial, you’ll have the option to make use of any of the workflows shared in my repository.
As I don’t have time to commit to in-person Chinese classes, I can have an assistant who will adapt to my schedule.
Can we do higher?
On the “roadmap” of this small side project, I even have:
- Adding complex grammar exercises that could possibly be done orally (combining reading comprehension, grammar and pronunciation)
- Implementing a writing module that might correct my calligraphy using image processing
Depending on my availability, I’ll aim to ship it by Q1 2026.
About Me
Let’s connect on LinkedIn and Twitter; I’m a Supply Chain Engineer using data analytics to enhance logistics operations and reduce costs.
For consulting or advice on analytics and sustainable supply chain transformation, please contact me via Logigreen Consulting.
