AI-Powered Auto-Generate Exam Questions and Answers from Google Docs with Gemini

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

1. 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.

2. AI Processing:

   • Splits text into chunks and generates vector embeddings (via OpenAI) for semantic analysis.
   • Stores embeddings in Qdrant (vector database) for retrieval.

3. 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.

4. 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.

5. 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

1. 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.

2. Configure Connections:

   • Link credentials for:
     • Google Docs/Sheets (OAuth2).
     • OpenAI (API key).
     • Google Gemini (API key).
     • Qdrant (URL + API key).

3. 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.

4. Tweak Question Generation:

   • Modify prompts in:
     • "Open questions" node: Adjust criteria (e.g., difficulty, question types).
     • "Closed questions" node: Edit MCQ formatting rules.

5. Output Settings:

   • Update the Google Sheet ID in "Write open" and "Write closed" nodes.
   • Map columns in Google Sheets to match question/answer formats.

6. 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.