Building Dynamic AI-Powered Interview Systems with n8n: A Complete Guide

Conducting user research interviews is time-consuming, expensive, and difficult to scale. Traditional interview methods require scheduling coordinators, trained interviewers, transcription services, and hours of manual data compilation. What if you could automate the entire interview process while maintaining the conversational depth and follow-up questions that make qualitative research valuable?

This n8n workflow solves exactly that problem. It creates an intelligent conversational interview system that conducts dynamic user research interviews, asks contextual follow-up questions, stores responses securely, and automatically compiles all data into organized spreadsheets. The system uses AI to adapt questions based on user responses, creating a natural interview flow that feels personal rather than robotic.

Prerequisites: What You Need to Get Started

Before building this automated interview system, you will need access to several key services and APIs. These components work together to create a seamless interview experience.

Required APIs and Services

• OpenAI API: Powers the AI interviewer that generates contextual questions and manages conversation flow. You will need an active API key with access to GPT models.
• Redis Database: Provides temporary session storage that maintains conversation context throughout the interview. Redis handles the fast read and write operations needed for real-time interactions.
• Google Sheets API: Enables automatic storage of interview transcripts. This allows for easy data analysis and sharing with research teams.
• n8n Instance: You need a running n8n installation, either self-hosted or cloud-based, to build and execute the workflow.

Key n8n Components Used

The workflow leverages several specialized n8n nodes that handle different aspects of the interview process:

• Form Trigger Node: Creates the initial interview form that collects participant names and begins the session.
• Form Node: Displays dynamic questions to users and captures their responses throughout the interview.
• AI Agent Node: Contains the interviewer logic that analyzes responses and generates contextual questions.
• OpenAI Chat Model Node: Connects to GPT models for natural language processing and question generation.
• Memory Buffer Window Node: Maintains conversation history so the AI can reference previous answers when forming new questions.
• Redis Nodes: Handle session creation, updates, and retrieval for maintaining interview state.
• Set Nodes: Format and structure data at various points in the workflow.
• If Node: Determines when to continue the interview or end the session based on user input.
• Google Sheets Node: Saves the complete interview transcript with timestamps and metadata.
• Crypto Node: Generates unique session identifiers for each interview.

Step-by-Step Implementation Guide

Building this conversational interview system involves creating a continuous loop where AI asks questions, users respond, and the system intelligently decides what to ask next. Here is how to construct each component.

Step 1: Initialize the Interview Session

The workflow begins when a user visits the interview form. The Form Trigger Node presents a welcoming interface that explains the interview process. It collects the participant's name and displays important information about the temporary nature of sessions, which are automatically deleted after 24 hours.

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Once the user submits their name, the workflow generates a unique session identifier using the Crypto Node. This UUID becomes the key for storing all conversation data in Redis. The Create Session node initializes an empty list in Redis with a 24-hour expiration time, ensuring automatic cleanup.

The first interaction is logged immediately. A Generate Row node creates a timestamp and records the initial question and answer, marking it as the start of the interview. This data is pushed to the Redis session list, beginning the transcript that will eventually be saved to Google Sheets.

Step 2: Configure the AI Interviewer

The heart of this workflow is the AI Researcher agent, which acts as the intelligent interviewer. This node receives detailed instructions through its system message that shape its behavior throughout the conversation.

The AI is told to focus on a specific interview topic, which you can customize in the Set Interview Topic node. In this example, the topic is readiness for an AI automation job, but you can modify this to research any subject. The system prompt instructs the AI to ask open-ended questions, probe deeper when appropriate, and limit follow-up questions to avoid overwhelming participants.

The Memory Buffer Window node connects to the AI Researcher, providing conversation context. This memory component uses the session UUID to maintain separate conversation histories for concurrent interviews, preventing cross-contamination between different participants.

Step 3: Create the Interview Loop

After the AI generates its first question, the workflow enters a continuous loop. The Stop Interview node examines the parsed JSON response to check if the boolean flag is true. If false, the interview continues. If true, the interview ends.

Once the user submits their answer, the Generate Row node packages the question, answer, and timestamp into a structured record. This record is appended to the Redis session list via the Update Session node. The answer is then formatted by the Send Reply To Agent node and fed back into the AI Researcher.

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The AI analyzes the new answer in the context of previous exchanges, thanks to the Memory Buffer Window, and generates the next appropriate question. This loop continues indefinitely until the user requests to stop or the AI determines the interview is complete.

Step 4: Handle Interview Termination

When the Stop Interview node detects that the interview should end, the workflow branches to a different path. The Generate Row node creates a final record marking the stop_interview event with null values for question and answer fields. This record is added to the session data in Redis.

The Memory Manager node then clears all conversation history associated with the session UUID. This cleanup is important for data privacy and ensures that the next interview using the same n8n instance starts fresh without residual context from previous conversations.

The Redirect to Completion Screen form node displays a thank you message to the participant, providing closure to the interview experience. This completion screen serves as the trigger for the final data processing step.

Step 5: Save and Structure Interview Data

After showing the completion screen, the workflow retrieves the complete interview transcript from Redis. The Get Session node fetches all records associated with the session UUID, returning them as a list of JSON strings.

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Finally, the Save to Google Sheet node appends all interview records to a designated spreadsheet. The sheet includes columns for session ID, timestamp, participant name, record type, question, and answer. This structured format makes it easy to filter, analyze, and extract insights from multiple interviews. Research teams can access the spreadsheet immediately after each interview completes, enabling real-time analysis

Benefits and Real-World Use Cases

Scalability Without Quality Loss

Traditional interviews are limited by interviewer availability. This workflow can conduct dozens or hundreds of interviews simultaneously, each with personalized questions and follow-ups. Research that would normally take weeks can be completed in days, and the AI maintains consistent quality across all sessions.

24/7 Availability Across Time Zones

Participants can complete interviews whenever convenient, removing scheduling friction. This is especially valuable for global user research where coordinating across time zones is challenging. The asynchronous nature also gives participants time to think through their responses rather than feeling rushed.

Taking Your Interview System Further

This workflow provides a solid foundation that you can extend based on specific needs. Consider adding sentiment analysis to automatically categorize emotional tone in responses. Integrate with CRM systems to link interview data with customer profiles. Create multiple interview paths that branch based on early responses. Add file upload capabilities for participants to share screenshots or documents. Implement email notifications that alert research teams when high-priority responses are detected.

Building intelligent conversational interview systems is no longer reserved for organizations with dedicated engineering teams and large budgets. The result is a scalable, cost-effective solution that maintains the qualitative depth that makes interviews valuable while eliminating the logistical headaches that make them difficult. Whether you are conducting user research, employee feedback sessions, or market exploration, this automated interview system brings the power of AI to your qualitative research toolkit.