Automated Quiz Grader Using n8n Workflow

This n8n workflow automates the process of grading quizzes by integrating webhooks, natural language processing, vector similarity search, and predictive AI models. It is designed to receive quiz data via a webhook, process and analyze the responses, and log the results automatically, providing a scalable and efficient grading system. The workflow begins with a webhook trigger that captures quiz submissions, splits the input text into manageable chunks, and generates embeddings using Cohere’s API. These embeddings are stored and queried from Pinecone, a vector database, to find relevant responses or information. The workflow employs a language model from OpenAI to evaluate and process the data in conjunction with a Retrieval-Augmented Generation (RAG) agent, which synthesizes the quiz responses, grades them, and decides the outcome. Results are appended to a Google Sheet for record-keeping, and error notifications are sent via Slack. This setup is ideal for educational platforms, online assessments, or any automated evaluation system that requires fast and consistent grading.