This workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content s…
This workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content stored in Google Docs, using AI-powered analysis and vector database retrieval This workflow saves educators hours of manual work while ensuring high-quality, curriculum-aligned assessments. Let me know if you'd like help adapting it for specific subjects! Use Cases Educators**: Rapidly generate quizzes, midterms, or flashcards. E-learning platforms**: Automate question banks for courses. Corporate training**: Create assessments for employee onboarding. Technical Requirements: APIs**: Google Gemini, OpenAI, Qdrant, Google Workspace. n8n Nodes**: LangChain, Google Sheets/Docs, HTTP requests, code blocks. This workflow combines AI efficiency with human-curated quality, making it a powerful tool for modern education and training. Advantages of This Workflow ✅ Fully Automated Exam Generation: From document to fully formatted quiz content with no manual intervention. ✅ Supports Comprehension and Critical Thinking: Questions are designed to go beyond factual recall, including inference and application. ✅ Uses AI and RAG for Accuracy: Ensures that answers are grounded in the document content, reducing hallucination. ✅ Seamless Google Integration: Pulls content from Google Docs and writes outputs to Google Sheets. ✅ Scalable for Any Subject: Works with any article or content domain as input. ✅ Modular and Customizable: Can be easily adapted to generate different question types or to use other LLMs or storage systems. How It Works Document Ingestion: The workflow starts by fetching an educational document (e.g., textbook chapter, lecture notes) from Google Docs. Converts the document to Markdown for structured processing. AI Processing: Splits text into chunks and generates vector embeddings (via OpenAI) for semantic analysis. Stores embeddings in Qdrant (vector database) for retrieval. Question Generation: Open-ended questions: Google Gemini AI creates 10 critical-thinking questions. Multiple-choice questions: Generates 10 MCQs (1 correct + 3 plausible distractors) using RAG to validate answers against the vector DB. Answer Validation: For open questions: Retrieves context-aware answers from the vector store. For MCQs: Ensures distractors are incorrect but believable via AI cross-checking. Output: Saves questions/answers to Google Sheets in two tabs: Open questions: Question + AI-generated answer. Closed questions: MCQ + options + correct answer. Set Up Steps Prerequisites: APIs/Accounts: Google Workspace (Docs + Sheets). OpenAI (for embeddings). Google Gemini (for question generation). Qdrant (vector DB – self-hosted or cloud). n8n Nodes: Ensure LangChain, Google Sheets/Docs, and HTTP request nodes are installed. Configure Connections: Link credentials for: Google Docs/Sheets (OAuth2). OpenAI (API key). Google Gemini (API key). Qdrant (URL + API key). Customize Input: Replace the default Google Doc ID in the "Get Doc" node with your source document. Adjust chunk size/overlap (Token Splitter node) for optimal text processing. Tweak Question Generation: Modify prompts in: "Open questions" node: Adjust criteria (e.g., difficulty, question types). "Closed questions" node: Edit MCQ formatting rules. Output Settings: Update the Google Sheet ID in "Write open" and "Write closed" nodes. Map columns in Google Sheets to match question/answer formats. Run & Automate: Trigger manually ("Test workflow") or schedule periodic runs (e.g., for updated content). Need help customizing? Contact me for consulting and support or add me on Linkedin. An n8n automation workflow template by Davide Boizza.
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