Educational flashcard generation from course materials

Introduction

Anyone who has sat through a semester of lectures knows the feeling: you have pages of notes, recorded videos, and slides, but no clear way to study them efficiently. The traditional approach means manually creating flashcards from your course materials, typing out questions and answers one by one. It is tedious, time consuming, and easy to abandon halfway through.

What if your course materials could automatically become a complete study deck? What if you could feed in lecture notes, summaries, or video transcripts and have a ready-to-use Anki deck waiting for you the next morning? This workflow combines three AI tools to do exactly that: it takes your raw course materials, extracts the key concepts, generates meaningful flashcard pairs, and packages them into a format you can study immediately.

This Alchemy guides you through wiring AnkiDecks-AI, Rember, and Resoomer-AI together using an orchestration tool. The workflow requires no manual handoff between tools, no copying and pasting, and no fiddling with file formats. One trigger starts the entire process. By the end, you will have a method for converting any course material into flashcards in minutes.

The Automated Workflow

The workflow operates in four distinct stages: material intake, summarisation, flashcard generation, and deck assembly. Each stage feeds into the next through API calls orchestrated by your chosen tool.

Choosing Your Orchestration Tool

For this beginner-level workflow, I recommend Zapier if you prefer a visual interface with minimal setup, or n8n if you want to self-host and avoid monthly subscription costs. Make (Integromat) offers a middle ground with good pre-built connectors. Claude Code can work if you are comfortable writing Python, but it requires more manual intervention unless deployed as a scheduled function.

For this guide, I will show n8n examples, as the logic translates easily to any platform.

Stage 1:

Material Intake

The workflow begins when you drop a file into a folder or send an email with your course material. You can trigger this via:

• A file upload to Google Drive or Dropbox

• An incoming email with an attachment

• A form submission with pasted text

For this example, let us assume material comes through a webhook as text. Here is what the trigger payload looks like:

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{
  "course_name": "Biology 101",
  "material_type": "lecture_notes",
  "content": "Photosynthesis is the process by which plants convert light energy into chemical energy stored in glucose. The light-dependent reactions occur in the thylakoid membrane where chlorophyll absorbs photons. The light-independent reactions (Calvin cycle) occur in the stroma and use ATP and NADPH from the light reactions to fix carbon dioxide...",
  "material_id": "mat_abc123"
}

Your orchestration tool receives this payload and passes the content field to the next stage.

Stage 2:

Summarisation with Resoomer-AI

Before generating flashcards, you need to condense verbose lecture notes into key concepts. Resoomer-AI specialises in this: it identifies the main ideas and removes redundant information.

The API endpoint for Resoomer-AI summarisation is:

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POST https://api.resoomer.ai/v1/summarize

Your orchestration tool sends this request:

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{
  "text": "Photosynthesis is the process by which plants convert light energy into chemical energy stored in glucose. The light-dependent reactions occur in the thylakoid membrane where chlorophyll absorbs photons. The light-independent reactions (Calvin cycle) occur in the stroma and use ATP and NADPH from the light reactions to fix carbon dioxide...",
  "language": "en",
  "percent": 30
}

Note the percent parameter: this asks for a summary that retains 30% of the original length. Adjust this based on your material density; denser material may need 40-50%, whilst verbose lectures work well at 20-25%.

Resoomer returns a summarised version:

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{
  "summary": "Photosynthesis converts light energy into chemical energy via two reaction stages. Light-dependent reactions in the thylakoid use chlorophyll to absorb photons and produce ATP and NADPH. Light-independent reactions (Calvin cycle) in the stroma use these molecules to fix carbon dioxide into glucose.",
  "reduction_percent": 29.8,
  "status": "success"
}

Store this summary in a variable for the next stage.

Stage 3:

Flashcard Generation with AnkiDecks-AI

Now you have condensed, focused content. AnkiDecks-AI takes this summary and generates question-answer pairs formatted for Anki.

The AnkiDecks-AI endpoint is:

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POST https://api.ankidecks-ai.com/v1/generate-cards

Send the summary as input:

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{
  "text": "Photosynthesis converts light energy into chemical energy via two reaction stages. Light-dependent reactions in the thylakoid use chlorophyll to absorb photons and produce ATP and NADPH. Light-independent reactions (Calvin cycle) in the stroma use these molecules to fix carbon dioxide into glucose.",
  "deck_name": "Biology 101 - Photosynthesis",
  "card_count": 15,
  "difficulty": "beginner",
  "include_images": false
}

The card_count parameter lets you control how many cards are generated. For a condensed summary, 15 cards is reasonable. AnkiDecks-AI returns an array of card objects:

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{
  "cards": [
    {
      "front": "What is photosynthesis?",
      "back": "The process by which plants convert light energy into chemical energy stored in glucose.",
      "tags": ["biology", "energy"]
    },
    {
      "front": "Where do light-dependent reactions occur?",
      "back": "In the thylakoid membrane of the chloroplast.",
      "tags": ["biology", "light-reactions"]
    },
    {
      "front": "What are the products of light-dependent reactions?",
      "back": "ATP and NADPH, which are used in the Calvin cycle.",
      "tags": ["biology", "light-reactions"]
    }
  ],
  "deck_id": "deck_xyz789",
  "status": "success"
}

Each card contains a front (question), back (answer), and tags for organisation.

Stage 4:

Deck Assembly with Rember

Rember is a study platform that can import Anki decks and manage them. However, for maximum compatibility and portability, the workflow should also export the deck as a standard .apkg file (Anki package format).

Send the generated cards to Rember via:

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POST https://api.rember.io/v1/decks/create

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{
  "name": "Biology 101 - Photosynthesis",
  "cards": [
    {
      "front": "What is photosynthesis?",
      "back": "The process by which plants convert light energy into chemical energy stored in glucose.",
      "tags": ["biology", "energy"]
    },
    {
      "front": "Where do light-dependent reactions occur?",
      "back": "In the thylakoid membrane of the chloroplast.",
      "tags": ["biology", "light-reactions"]
    }
  ],
  "public": false
}

Rember responds with:

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{
  "deck_id": "rember_deck_456",
  "url": "https://rember.io/study/rember_deck_456",
  "status": "created"
}

At this point, you have a study-ready deck in Rember. Optionally, to also create a downloadable Anki file, add a final step that converts the card array to .apkg format and uploads it to cloud storage.

Complete n8n Workflow Example

Here is how these four stages wire together in n8n. This is a simplified JSON export of the workflow:

Copy{
  "nodes": [
    {
      "name": "Webhook Trigger",
      "type": "webhook",
      "typeVersion": 1,
      "position": [250, 300],
      "webhookUrl": "https://your-n8n-instance.com/webhook/material-upload"
    },
    {
      "name": "Call Resoomer API",
      "type": "httpRequest",
      "typeVersion": 4.1,
      "position": [450, 300],
      "parameters": {
        "url": "https://api.resoomer.ai/v1/summarize",
        "method": "POST",
        "headers": {
          "Authorization": "Bearer YOUR_RESOOMER_API_KEY"
        },
        "body": {
          "text": "{{ $json.content }}",
          "language": "en",
          "percent": 30
        }
      }
    },
    {
      "name": "Call AnkiDecks API",
      "type": "httpRequest",
      "typeVersion": 4.1,
      "position": [650, 300],
      "parameters": {
        "url": "https://api.ankidecks-ai.com/v1/generate-cards",
        "method": "POST",
        "headers": {
          "Authorization": "Bearer YOUR_ANKIDECKS_API_KEY"
        },
        "body": {
          "text": "{{ $nodes['Call Resoomer API'].json.summary }}",
          "deck_name": "{{ $json.course_name }} - Generated",
          "card_count": 15,
          "difficulty": "beginner"
        }
      }
    },
    {
      "name": "Create Rember Deck",
      "type": "httpRequest",
      "typeVersion": 4.1,
      "position": [850, 300],
      "parameters": {
        "url": "https://api.rember.io/v1/decks/create",
        "method": "POST",
        "headers": {
          "Authorization": "Bearer YOUR_REMBER_API_KEY"
        },
        "body": {
          "name": "{{ $json.course_name }} - Flashcards",
          "cards": "{{ $nodes['Call AnkiDecks API'].json.cards }}"
        }
      }
    }
  ],
  "connections": {
    "Webhook Trigger": {
      "main": [["Call Resoomer API"]]
    },
    "Call Resoomer API": {
      "main": [["Call AnkiDecks API"]]
    },
    "Call AnkiDecks API": {
      "main": [["Create Rember Deck"]]
    }
  }
}

In n8n's visual editor, you would:

1. Create a Webhook node and set it to listen for incoming requests.

2. Add an HTTP Request node pointing to Resoomer, extracting the content field from the trigger.

3. Chain an AnkiDecks HTTP Request node that uses the summary from step 2.

4. Add a final HTTP Request node for Rember that accepts the cards from step 3.

5. Connect each node in sequence.

When you send material to the webhook URL, the entire chain executes automatically. Within 30 seconds to 2 minutes (depending on API response times), your deck is ready in Rember.

The Manual Alternative

If you prefer direct control without automation, you can run each tool independently and combine the outputs yourself. This approach takes longer but gives you the chance to review and edit at each stage.

Step 1: Paste your course material into Resoomer-AI's web interface and download the summary.

Step 2: Copy the summary and paste it into AnkiDecks-AI's generator. Review the generated cards and make manual edits if needed (questions could be clearer, answers might need context added).

Step 3: Export the edited cards as a CSV or JSON file.

Step 4: Import the file into Rember, or upload it directly to Anki.

This method adds 15-20 minutes of work per course section but is useful if your course material is inconsistent in quality or if you want to customise card wording.

Pro Tips

Rate Limiting and Quota Management

The AnkiDecks API typically enforces a limit of 100 cards per minute and 1000 cards per day on free plans. If you are processing large lecture series, consider batching requests: generate cards for one lecture per day rather than uploading your entire semester at once. Set a 2-second delay between API calls in your orchestration tool to avoid hitting rate limits.

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{
  "delay_seconds": 2,
  "batch_size": 50
}

Handling API Failures

Network issues or API downtime occasionally occur. Add error handling to your orchestration tool so that failed requests trigger a retry (usually 2-3 times) before notifying you via email. In n8n, use the "Catch" node to capture errors:

Copy{
  "errorHandling": "catch",
  "retryAttempts": 3,
  "delayBetweenRetries": 5000
}

Optimising Summarisation Depth

The summary percentage matters. Set it too low (under 20%), and you lose important context. Set it too high (over 50%), and the summary remains cluttered. For technical subjects like biology or chemistry, aim for 25-35%. For humanities material, 40-50% often works better. Test with a single lecture and adjust based on how complete the resulting flashcards feel.

Splitting Long Material

Course materials longer than 5000 words can cause timeouts or generate unfocused flashcard sets. Split lengthy lecture notes into sections (e.g., "Photosynthesis Overview", "Light Reactions", "Calvin Cycle") and run the workflow separately for each. This produces more cohesive decks that are easier to study.

Cost Optimisation

If you are processing many lectures, costs add up. Resoomer-AI and AnkiDecks-AI offer batch processing discounts at higher usage tiers. If you anticipate generating more than 200 flashcards per month, purchase their pro plans upfront rather than paying per-request rates. Rember is free up to 10 decks, so most students won't hit paid tiers.

Cost Breakdown

Total monthly cost for light use (under 100 cards per month): £0–£20 if self-hosting on n8n, or £20–£40 with Zapier.

Total monthly cost for heavy use (500+ cards per month): £30–£50 depending on tool tier selection.

For students processing one 2000-word lecture per week, expect to spend £10–£20 monthly if you choose the right tier combination, or nothing if you stay within free limits.