Convert academic papers into teaching materials and exam questions

An academic paper lands in your inbox. It contains genuinely useful material for next week's course, but transforming it into flashcards, lecture slides, and exam questions will consume most of your evening. You read the abstract, skim sections, manually extract key concepts, then spend another hour reformatting everything into your learning management system. By the time you finish, the paper's insights feel diluted by the friction of transcription. This workflow doesn't have to exist. With the right combination of AI tools wired together, you can upload a PDF, extract its core ideas, generate study materials in multiple formats, and have everything ready to integrate into your course, all without touching a keyboard between the initial upload and review. The following automation handles the entire pipeline: parsing the paper, identifying key concepts, generating flashcards for spaced repetition, creating exam questions at different difficulty levels, and organising everything into a structured format your course platform can ingest. Most of this happens without you moving between tabs or copy-pasting text.

The Automated Workflow

Tool selection and data flow

We'll use n8n for orchestration because it offers superior PDF handling compared to Zapier, integrates directly with multiple AI providers, and gives you granular control over data transformation.

The workflow moves through four distinct phases: ingestion, extraction, generation, and output. Here's what happens at each stage: 1. A PDF file (the academic paper) lands in a monitored folder or is uploaded via a form.

2. n8n extracts the full text and metadata using its built-in PDF node.

3. Chat With PDF by Copilot.us processes the document to answer specific questions about methodology, results, and key findings.

4. Claude Opus 4.6 (called via n8n's HTTP node) generates structured flashcards based on extracted concepts.

5. The same Claude call produces tiered exam questions (simple recall, application, critical analysis).

6. AnkiDecks AI receives the formatted flashcards and creates a shareable deck.

7. All outputs merge into a single JSON file ready to import into your learning management system.

Setting up the n8n workflow

Start by creating a new workflow in n8n. Add a "Webhook" trigger node that listens for PDF uploads. You can configure this to accept files via HTTP POST or connect it to a cloud storage trigger (Google Drive, Dropbox) if you prefer automatic ingestion.

CopyPOST /webhook/paper-upload
Content-Type: multipart/form-data { "file": [binary PDF data], "paper_title": "string", "course_code": "string"
}

Next, add an "Extract PDF Text" node. This native n8n node pulls the complete text and page metadata from the uploaded file. Then add an HTTP Request node to call Chat With PDF. You'll need an API key from Copilot.us. Configure the request as follows:

CopyPOST https://api.copilot.us/chat/pdf
Content-Type: application/json
Authorization: Bearer YOUR_COPILOT_API_KEY { "document_id": "{{ $node['Extract PDF Text'].data.documentId }}", "queries": [ "What is the primary research question?", "What methodology was used?", "What are the main findings?", "What are the limitations discussed?", "Who are the key authors and their affiliations?" ]
}

The responses from Chat With PDF become the source material for content generation. Now add a second HTTP Request node to call Claude Opus 4.6 via the Anthropic API. This node transforms the extracted information into flashcards and exam questions.

CopyPOST https://api.anthropic.com/v1/messages
Content-Type: application/json
x-api-key: YOUR_ANTHROPIC_API_KEY { "model": "claude-opus-4.6", "max_tokens": 4096, "messages": [ { "role": "user", "content": "Based on the following academic paper summary, generate exactly 12 flashcards in JSON format. Each flashcard must have a 'front' field (the question) and 'back' field (the answer). Additionally, generate 8 exam questions: 2 recall questions, 3 application questions, and 3 analysis questions. Format as valid JSON with two top-level keys: 'flashcards' (array) and 'exam_questions' (array). Paper summary:\n\n{{ $node['Chat With PDF'].data.summary }}" } ]
}

Claude will return structured JSON containing both the flashcards and exam questions. Parse this response with a "Set" node to extract and organise the data:

Copy{ "flashcards": {{ $node['Claude Generation'].data.content[0].text | parseJson }}.flashcards, "exam_questions": {{ $node['Claude Generation'].data.content[0].text | parseJson }}.exam_questions, "paper_title": {{ $node['Webhook'].data.paper_title }}, "course_code": {{ $node['Webhook'].data.course_code }}
}

Next, send the flashcards to AnkiDecks AI. This step formats them into an Anki-compatible deck.

CopyPOST https://api.ankidecksai.com/v1/decks/create
Content-Type: application/json
Authorization: Bearer YOUR_ANKIDECKSAI_API_KEY { "deck_name": "{{ $node['Set'].data.paper_title }} - {{ $node['Set'].data.course_code }}", "flashcards": {{ $node['Set'].data.flashcards | json }}
}

Finally, add a "Write to File" node that saves all outputs (flashcards, exam questions, AnkiDecks link, paper metadata) to a JSON file, which you can then import into your learning management system or store in cloud storage for later access.

Copy{ "paper_metadata": { "title": "{{ $node['Set'].data.paper_title }}", "course_code": "{{ $node['Set'].data.course_code }}", "processed_at": "{{ now }}" }, "anki_deck_link": "{{ $node['AnkiDecks'].data.deck_url }}", "flashcards": {{ $node['Set'].data.flashcards | json }}, "exam_questions": {{ $node['Set'].data.exam_questions | json }}
}

Once you've built this workflow, activate it and test with a real paper. The entire process, from PDF upload to structured output, should complete in under two minutes.

The Manual Alternative

If you prefer direct control or need to review content at each step, you can operate these tools sequentially without automation. Upload your paper to Explainpaper first, highlight confusing sections, and read its explanations to build your own understanding. Then open the PDF in Chat With PDF and ask questions about key concepts. Use the responses to manually write flashcards in Anki or AnkiDecks, and draft exam questions in a text editor. This method takes longer but gives you the chance to refine content as you go, which some educators prefer for high-stakes courses. For more on this, see Educational course content generation and assessment crea....

Pro Tips

Rate limiting and cost control

Claude Opus 4.6 has a rate limit of 50 requests per minute on most API tiers.

If you're processing multiple papers in batch, space out your n8n workflow triggers. Add a "Delay" node between the Chat With PDF call and the Claude call (2 seconds is usually sufficient) to avoid hitting rate limits unexpectedly.

Handling PDFs with poor OCR

Some academic papers, especially older ones or scans, have garbled text extraction. If n8n's PDF parser struggles, use Explainpaper as an intermediary: upload the problem paper there first, have it clean the text, then feed that cleaned version to your workflow. You can do this manually or by having n8n call Explainpaper's API if available.

Reducing API costs

Claude Opus 4.6 is more expensive than Claude Sonnet 4.6, but it's substantially better at structured output generation. For papers you know will have straightforward content, try Claude Sonnet 4.6 first to reduce costs. Switch to Opus only if Sonnet's flashcards or questions are too generic or miss important nuance.

Validating generated questions

Always review exam questions before using them in actual assessments. Claude sometimes generates questions that are either too easy (because it assumes students read the paper) or too obscure (because it fixates on minor details). Add a manual review step to your workflow where you approve questions before they're marked final.

Organising output files

If you process papers regularly, save each workflow output to a folder named by course code and term. Set up a secondary n8n workflow or Zapier automation that syncs these files to Google Drive or Dropbox, so your generated materials are always backed up and searchable.

Cost Breakdown