How AI turns basic vocab into immersive, multimodal learning

8 min read5 days ago

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Learning a new language is a marathon, not a sprint. Vocabulary is the engine of progress: without words, you can’t express yourself or understand others.

That’s why systematic vocabulary training is essential.

Flashcards, especially when paired with a spaced-repetition system (SRS), are among the most effective methods for building vocabulary.

Spaced repetition schedules reviews at expanding intervals to maximise long-term retention.

The simplest cards put a native-language cue on the front and the target-language translation on the back.

Helpful, yes, but it leaves a lot of learning value on the table.

A richer, structured flashcard includes:

• The word pair (base form) in both languages
• An example sentence in both languages
• An image that illustrates the sentence
• Target-language audio for pronunciation
• Brief grammar or usage notes

In this article, I’ll explain the benefits of each element and why you should add them to your cards.

And because building rich cards by hand is time-consuming, I’ll also show an AI-powered n8n workflow that generates them automatically,

You can import the results into Anki, the popular, free, and open-source flashcard app, with almost no manual effort.

Reasons for the Advanced Flashcard Design

The following image shows the final design of a Spanish language learning flashcard, which includes:

• Word pair we want to learn
• Related example sentences,
• Visual representations of the sentences in images
• Audio versions of the example sentences in the foreign language,
• Grammar notes

All these parts serve a purpose, which I would like to highlight in this section of the article before delving deeper into the workflow that I will later walk you through.

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Reason for Integration of Example Sentences

Isolated word pairs are easy to glance at, but just as easy to forget. When you use the target word in a short, natural sentence, your brain does more than remember its meaning.

You also pick up on grammar, common word combinations, and context. El gato negro duerme en el sofá teaches more than just that gato means cat in Spanish.

You also reinforce the order of adjectives, verb forms, and imagine a real-life situation.

This richer set of connections helps you remember the word later, whether by its sound, structure, or the scene.

Reason for Integration of Images

Images are more than just decoration; they help us remember.

According to dual coding theory, developed by psychologist Allan Paivio, our brains process words and pictures through two separate yet interconnected systems.

When we learn something using both words and images, we create more ways to remember it, which makes it easier to recall later.

This idea is connected to the Picture Superiority Effect, which means that people usually remember images better than words because pictures create both a visual memory and a related word in our minds.

Reason for Integration of Audios

When you see a word and hear it at the same time, you build a stronger mental sound form.

This helps you speak and understand quickly. Short audio clips, just a few seconds per sentence, make it easier to connect spelling with pronunciation, rhythm, and intonation.

One of my favourite routines is reading while listening and then repeating aloud what I hear. T

his practice has helped me become more natural with the language, although I still have a long way to go.

Reason for Integration of Grammar Notes

Grammar and usage micro-notes make each flashcard a quick lesson.

These notes should be short and easy to skim. The focus lies on single, tricky points in the sentences that should be kept and mined, or require more detailed explanations.

The goal is not to rewrite a grammar book, but to highlight the specific detail that might cause trouble later, like word order, agreement and endings, irregular stems, fixed prepositions and article pairs, register and nuance, or common collocations.

Take “El gato negro duerme en el sofá.” A concise note might remind you that Spanish adjectives usually follow the noun (“gato negro,” not “negro gato”).

Another could point out the stem change in “dormir → duerme” (o→ue, third person singular, present).

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Flashcard Creation Workflow

If I were to create flashcards containing all these beneficial learning features on my own, it would take me a considerable amount of time.

For this reason, I created the following n8n workflow for generating these advanced flashcards on autopilot.

I only need to enter the word pairs, consisting of a word in my native language and the corresponding word in the language I am studying, and the workflow will handle the rest.

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Let’s examine the separate parts of this workflow individually to understand what’s happening.

Word Pair Input

In the first part, the workflow waits for a POST request to be received over the webhook node.

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An HTTP POST request sends data in the request body to a server to process a resource, and the server returns a response with the result.

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In this case, the request body is structured as follows.

curl -X POST “http://localhost:5678/webhook-test/83aaddfe-887a-4988-8323-1cee01b0d48a” \  -H “Content-Type: application/json” \  --data-binary ‘[    {      “email”: “your.email@mail.com”,      “nativeLanguage”: “English”,      “targetLanguage”: “Spanish”,      “languageLevel”: “B1”,      “nativePhrase”: “to go shopping”,      “targetPhrase”: “ir de compras”    },    {      “email”: “your.email@mail.com”,      “nativeLanguage”: “English”,      “targetLanguage”: “Spanish”,      “languageLevel”: “B1”,      “nativePhrase”: “play basketball”,      “targetPhrase”: “jugar al baloncesto”    }  ]’

This usage of the webhook node allows for a flexible number of input vocabulary pairs and the later implementation of an advanced frontend application.

Ultimately, the various items in the request body are separated for processing in subsequent steps, allowing them to be handled individually.

Flashcard Generation

Afterwards, the flashcard text for each input item is generated.

The AI models generate a structured JSON file as an output, consisting of the file name, the word pair, an example sentence in a foreign language, an example sentence in the target language and grammar notes.

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All these generated items are further processed to enable multimodal learning with all senses.

Learning with all Senses

In the next step, all generated flashcard texts are enriched with images and audio and structured for the final output in this parallel workflow.

**Building an Anki Text File **For later import of the flashcards into Anki, the generated text must be structured in a predefined way, as shown in the following code block.

#separator:Semicolon#html:true#notetype:Basic#columns:Front;Back;Tags

“text front page flashcard 1”;”text back page flashcard 1“;"tags"“text front page flashcard 2”;”text back page flashcard 2“;”tags”

The n8n code blocks combine the output texts from the previous OpenAI generation node for all flashcard items into a unified text file for later import into Anki.

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An example of a perfectly formatted flashcard is provided here.

“<div style=’text-align:center’><img src=’image_manana_vamos_a_ir_de_compras.png’ style=’display:block;margin:0 auto 12px auto;max-width:200px;height:auto’></div><div>Tomorrow we are going <b>to go shopping</b> at the mall.</div>”;”<div>to go shopping → ir de compras</div><div style=’margin-top:8px’>Mañana vamos a <b>ir de compras</b> al centro comercial.</div><hr><div><strong>Grammar notes:</strong><ul><li><em>ir de compras</em> is a fixed expression meaning to go shopping for clothes or general items, not groceries.</li><li><em>vamos a</em> + infinitive expresses a near future plan. Here, <em>vamos a</em> introduces the action <em>ir</em>.</li><li><em>al</em> is the contraction of <em>a</em> + <em>el</em> before a masculine singular noun as in <em>al centro comercial</em>.</li><li><em>Mañana</em> placed at the start sets the time. It means tomorrow in this context.</li></ul></div> [sound:audio_manana_vamos_a_ir_de_compras.mp3]”;”“

It can be seen that images are referenced by <img src=’image_manana_vamos_a_ir_de_compras.png’> , and audios are referenced by [sound:audio_manana_vamos_a_ir_de_compras.mp3].

For more information on what Anki requires for importing multimodal flashcards, refer to the official Anki documentation.

**Audio Generation **In addition to the text, an audio is generated of the example sentence in the foreign language, using OpenAI’s TTS-1-HD model.

Afterwards, the separated audio files are renamed so that their names match the references used in the previously generated text file.

In the end, all audio files are compressed into a zip file so they can be sent via email.

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Image Generation Similarly, the images are generated. The main difference from the audio generation workflow part is that there is a previous image prompt generator model.

This model is instructed to generate an image generation prompt for the example sentence, instead of passing the example sentence directly to the image generation model.

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After the image generation, the images are also renamed according to the names used in the Anki text import file.

Finally, the images are also zipped for sending them via email.

Forward E-Mail Address

The final step in the parallel workflow passes the email address from the start, ensuring the email node knows where to send the generated flashcards.

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Sending final results

After generating all parts of the flashcards, the results are merged and sent via email to the provided address.

Moreover, a response Webhook is attached at the end to confirm that the workflow was executed successfully.

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The final email appears as depicted in the following image, which includes the text import file, the zip file for images, and the zip file of audio files.

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The provided images and audio files must be added to the Anki media folder so that the Anki application can find them.

The structured flashcards, which reference these images and audio files in the media folder, can be imported into Anki as described in the email.

This adds the entire batch of flashcards to the selected Anki deck and schedules the flashcards according to your Anki study settings.

Final Thoughts

If there’s one takeaway, it’s this: richer cards make lighter work.

Since switching to context-first, multimodal flashcards generated by this workflow, my study sessions feel shorter, my reviews stick better, and — crucially — I spend my energy learning, not formatting.

Reading a sentence, hearing it, seeing it, and noting one tiny grammar wrinkle turns each card into a mini-lesson I actually remember.

On a practical level, this setup has removed almost all friction. I drop in word pairs, and out comes a clean Anki import, complete with matching audio and images.

That consistency is what keeps me coming back daily; it turned vocabulary from a chore into a habit I look forward to.

Next, I’m planning a simple web UI, so adding words won’t require curl.

If you’d like to influence what that looks like or prefer a different direction, please write a comment and share your thoughts.

I am curious what your ideas are and how you study languages.