AI Mock Interviwer

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About the Project

Inspiration

I built the AI-Powered Mock Interviewer to help students and professionals practice interviews in a realistic, structured, and feedback-driven environment. Many candidates struggle to articulate their answers and understand where they need improvement. This project aims to simulate a real interview experience and provide actionable feedback.

What it does

The AI Mock Interviewer allows users to:

Select an interview type: behavioral, technical, or system design.
Answer dynamically generated questions from the AI interviewer.
Receive an automated scoring and evaluation based on a structured rubric.
Generate a comprehensive feedback report highlighting strengths, weaknesses, and suggestions for improvement.

How I built it

Used Kiro to translate high-level interview specs into working code.
Created a modular Next.js frontend with pages for starting interviews, viewing history, and reading feedback.
Employed Kiro hooks to automate report generation and modularize code for easy updates.
Structured the interview flow to handle dynamic question generation and real-time response evaluation.

Challenges I ran into

Ensuring the AI asked relevant and dynamic questions across multiple interview types.
Structuring specifications so Kiro could reliably generate working, modular code.
Handling edge cases where users gave unexpected answers, requiring robust evaluation logic.

Accomplishments that I'm proud of

Successfully implemented a full interview flow with feedback generation.
Built a system that adapts to new interview types easily due to modular design.
Demonstrated the effective use of spec-to-code AI workflows to accelerate development.

What I learned

How to leverage Kiro’s spec-to-code and hooks for rapid development.
The importance of modular design and structured evaluation for AI-driven systems.
Practical experience in AI-assisted frontend and backend integration.

What's next for AI Mock Interviewer

Expand scoring rubrics to include behavioral nuances and soft skills evaluation.
Enable real-time coaching and hints during the interview.
Integrate more advanced AI evaluation models to improve feedback accuracy.
Add coding question simulation, allowing users to practice technical coding problems interactively.