InterviewIQ AI

An AI-powered interview coach that transforms resumes into personalized mock interviews with real-time feedback and career guidance.

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InterviewIQ AI – Your Personal AI Interview Coach

Inspiration

Preparing for interviews is one of the biggest challenges for students and job seekers. While there are plenty of online resources, most provide generic questions without considering a candidate's resume, experience, or target role. Professional interview coaching is also expensive and not accessible to everyone.

As a student preparing for software engineering placements, I wanted to build a solution that makes personalized interview preparation available to everyone. InterviewIQ AI was created to bridge that gap by providing resume-aware mock interviews, AI-generated feedback, and a personalized improvement roadmap.

What it does

InterviewIQ AI is a full-stack web application that simulates a realistic interview experience.

The application allows users to:

  • Upload a PDF or DOCX resume
  • Select a target job role
  • Participate in an AI-powered mock interview
  • Answer adaptive follow-up questions based on previous responses

  • Receive a detailed performance report including:

    • Overall interview score
    • Communication assessment
    • Technical knowledge evaluation
    • Problem-solving ability
    • Strengths and weaknesses
    • Personalized learning roadmap

Rather than asking generic interview questions, the AI uses resume context to create a more relevant and personalized interview.

How I built it

Frontend

  • React
  • Vite
  • Tailwind CSS
  • Framer Motion

Backend

  • Node.js
  • Express.js

AI

  • OpenAI API
  • Prompt Engineering
  • Resume-aware interview generation

Resume Processing

  • pdf-parse
  • Mammoth

The frontend provides a responsive user experience while the backend handles resume parsing, interview session management, AI communication, and evaluation generation.

Challenges I ran into

One of the biggest challenges was making the interview feel natural instead of generating unrelated questions.

To solve this, I designed the interview flow so that each response becomes part of the conversation context. This allows the AI to generate meaningful follow-up questions rather than treating every answer independently.

Another challenge was supporting both PDF and DOCX resume uploads. Different file formats required different parsing approaches while still producing clean text that could be used effectively by the AI model.

Building a polished full-stack application within a short hackathon timeline also required prioritizing the core experience over additional features.

What I learned

This project taught me that building AI applications involves much more than calling an LLM API.

I learned how to:

  • Design effective prompts
  • Manage conversational context
  • Build RESTful backend services
  • Handle file uploads and parsing
  • Improve user experience through responsive design and animations
  • Build reliable fallback mechanisms when AI services are unavailable

Most importantly, I learned how AI can be combined with traditional software engineering to solve real-world problems.

Future Improvements

I plan to extend InterviewIQ AI with:

  • Voice-based interviews
  • Video interview simulation
  • Live coding interview mode
  • Company-specific interview preparation
  • Progress tracking across multiple sessions
  • Multi-language support
  • ATS resume optimization
  • Personalized analytics dashboard

Impact

InterviewIQ AI demonstrates how AI can make career preparation more accessible and personalized.

By transforming a candidate's resume into an adaptive interview experience and providing actionable feedback, the platform helps students and job seekers build confidence before real interviews. The long-term vision is to make high-quality interview preparation available to anyone, regardless of background or access to professional coaching.

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