UI/UX
CodeQuest
Year
2025
Project Overview
CodeQuest is an AI-powered web platform designed to help programmers sharpen their skills and assist recruiters in assessing technical candidates efficiently. The platform automatically generates coding challenges and evaluates the submitted solutions in real-time, using AI models tailored to different programming languages and complexity levels.
CodeQuest is an AI-powered web platform designed to help programmers sharpen their skills and assist recruiters in assessing technical candidates efficiently. The platform automatically generates coding challenges and evaluates the submitted solutions in real-time, using AI models tailored to different programming languages and complexity levels.
Technology
Figma, React
Objective
Design an intuitive user experience that serves two core user types:
Developers, who need a frictionless way to practice coding, receive feedback, and track progress.
Recruiters, especially those without a technical background, who require clear dashboards and insights to evaluate candidates effectively.
The goal was to create a system that feels smart, responsive, and scalable, while staying accessible and visually clean.
Design an intuitive user experience that serves two core user types:
Developers, who need a frictionless way to practice coding, receive feedback, and track progress.
Recruiters, especially those without a technical background, who require clear dashboards and insights to evaluate candidates effectively.
The goal was to create a system that feels smart, responsive, and scalable, while staying accessible and visually clean.
Problem and Solution
Tech teams and recruiters often rely on outdated or manual methods to assess programming skills — reusing old interview questions, involving senior devs in screening, or relying on gut feeling. This wastes time, introduces bias, and doesn’t scale. There’s also no effective way to verify if a candidate’s code is original or AI-generated.
I helped design a platform that solves this through:
AI-Generated Challenges: Dynamic coding problems that adapt to the skill level and selected technologies.
Real-Time Code Evaluation: AI checks the solution for correctness, quality, and efficiency instantly.
Feedback Loops: Users get clear, actionable insights on their performance — not just pass/fail.
Recruiter Dashboard: A clean interface for non-technical users to review candidate performance, with reports and visual scoring.
Anti-Cheat Mechanisms: Behavioral tracking and AI-detection models to identify whether code is human-written or generated.
This project pushed me to design flows that balance complexity (AI, code editors, role-based access) with simplicity — and to think deeply about UX patterns in both learning and assessment contexts.
Tech teams and recruiters often rely on outdated or manual methods to assess programming skills — reusing old interview questions, involving senior devs in screening, or relying on gut feeling. This wastes time, introduces bias, and doesn’t scale. There’s also no effective way to verify if a candidate’s code is original or AI-generated.
I helped design a platform that solves this through:
AI-Generated Challenges: Dynamic coding problems that adapt to the skill level and selected technologies.
Real-Time Code Evaluation: AI checks the solution for correctness, quality, and efficiency instantly.
Feedback Loops: Users get clear, actionable insights on their performance — not just pass/fail.
Recruiter Dashboard: A clean interface for non-technical users to review candidate performance, with reports and visual scoring.
Anti-Cheat Mechanisms: Behavioral tracking and AI-detection models to identify whether code is human-written or generated.
This project pushed me to design flows that balance complexity (AI, code editors, role-based access) with simplicity — and to think deeply about UX patterns in both learning and assessment contexts.
Gonçalo Perdigão
Gonçalo Perdigão
Gonçalo Perdigão