CrackInterview
CrackInterview.AI is an AI-powered mock interview platform designed to help users practice and improve their performance in tech job interviews. I led the UX/UI design, focusing on creating a streamlined user flow and an intuitive, engaging experience.
Team size::
2
Duration:
12 weeks
My role
As the lead designer on the project, I was responsible for overseeing the entire design process—from research to final delivery. I led the interaction and UX strategy while collaborating with a design intern, who supported visual design tasks such as UI polish, posters, and marketing assets.
User Research: Collaborated with the founder to conduct user interviews, uncovering key user needs and pain points to inform product direction.
Competitor Analysis: Analyzed competing platforms to identify strengths, weaknesses, and design opportunities that guided our positioning and feature prioritization.
UX & UI Design: Mapped out user flows, created low-fidelity wireframes, and developed high-fidelity UI designs with a focus on clarity and engagement.
Usability Testing: Organized and facilitated testing sessions to validate design assumptions and gather actionable feedback from real users.
Product Testing: Actively participated in the product testing during showcase events, gathering user feedback to assess the effectiveness and impact of the product.
Iteration: Refined and improved the product design based on testing results, stakeholder feedback, and changing user needs.
Conclusion
Throughout this project, my focus was on deeply understanding user needs and turning those insights into a seamless, intuitive experience. By conducting in-depth research, mapping clear user flows, and continuously testing and refining the design, I was able to simplify the interview preparation process for users. The final product empowers users with clarity, confidence, and a smoother path toward their career goals.
Project Detail
Main Problem
While libaspace.com already provided career resources for Chinese-speaking users, there was a growing need to go deeper into the North American job market and offer more personalized, AI-driven support. To address this, we created CrackInterview.AI as a dedicated platform focused on AI-powered mock interviews.
However, we discovered that AI alone wasn’t enough—users still needed targeted guidance and real connections. Therefore, we extended the experience by integrating real mentors who provide feedback based on AI interview results, along with personalized referrals to help users break into their desired roles.
AI-Powered Mock Interviews
Generates realistic interview questions based on the user’s resume and target job description (JD), followed by detailed feedback and actionable guidance to help users improve.
Cheat Sheet
A curated collection of real interview questions from top tech companies, allowing users to study and prepare more effectively with relevant, up-to-date content.
Expert Consultation & Referrals
After completing mock interviews, users can connect with experienced mentors who provide personalized coaching and, when appropriate, referral opportunities to relevant job openings.
Persona
User Research
To better understand the challenges faced by early-career job seekers, especially international students targeting tech roles in North America, we conducted a series of user interviews and surveys. Our participants included recent graduates, master’s students, and junior professionals in fields such as software engineering, data science, and product management.
Key Insights
Lack of interview feedback: Many users never hear back after applying and don’t know why.
Resume-job mismatch: Users often feel unsure if their resume aligns with job descriptions.
Low interview opportunities: Especially common among international students facing visa-related rejection.
Overwhelming job platforms: LinkedIn and Indeed are cluttered, repetitive, and not personalized enough.
Limited guidance: Many want real, targeted advice from experienced professionals rather than general AI tools.
Referral gap: Accessing referrals is difficult without insider connections.
Solution
Main Features
Mock Interview
Our AI-powered mock interview simulates real job interviews based users‘ resume and the selected job description. The entire session is video recorded, helping users reflect on their performance and track progress over time.
Due to the higher cost of running AI digital interviewers, the platform offers two mock interview formats based on the user’s subscription plan. Users can freely choose interview modes based on their preferences and subscription level:
AI Digital Interviewer Mode: Includes a lifelike AI avatar conducting the interview for a more immersive experience.
Standard Mode: A more lightweight version without the digital avatar—ideal for users who prefer self-guided practice while observing their own performance.
Conversation design
The conversation design for the AI mock interview system focuses on creating a natural, engaging, and psychologically realistic dialogue flow between the user and the AI interviewer.
Role-Based Tone & Personality
The AI interviewer adopts different tones and language styles aligning with various company cultures or job roles. This enhances realism and user immersion.
Natural Language Flow
Dialogue patterns are optimized for clarity, rhythm, and conversational pacing—making interactions feel fluid and human-like, while still maintaining structure and assessment logic.
Adaptive Prompts
The system responds dynamically to user input, offering clarifications, follow-up questions, or redirection as needed to simulate the unpredictability of real interviews.
Feedback-Oriented Transitions
Each interview section ends with subtle transitions that guide the user into the next stage naturally, reducing friction and keeping users engaged throughout the session.
AI Digital Interviewer
An AI-generated interviewer simulates a lifelike virtual recruiter, enhancing immersion and realism. Questions are asked via voice and displayed with subtitles at the bottom, allowing users to practice in a human-like scenario.
Standard Mode
A simpler text-based interview interface where users can enable their webcam to observe their body language in real-time. Conversation history is shown on the right panel, helping users track the full flow of the mock interview and self-reflect more easily.
Code Mode
Designed for technical roles, this mode presents real-time coding questions and provides a built-in code editor. Users can type, run, and explain their logic while answering—mimicking real coding interviews.
Mock Interview result
The Result Page provides users with a comprehensive, structured summary of their AI mock interview performance. Designed to be insightful yet easy to navigate, this page helps users understand their strengths, identify weaknesses, and take actionable next steps.
Job Pages
The Job List page intelligently connects users with curated, high-match job opportunities based on their resume and job preferences. Each listing includes a clear match percentage, allowing users to focus on roles that best fit their background.
Future Improvements
To further enhance the platform’s value for both job seekers and employers, the following improvements are planned:
Partner Job Integration + Report Sharing
Collaborate with companies to access up-to-date job openings. Qualified candidates can choose to share their interview reports directly with employers—boosting trust and improving screening success rates.
Talent Pool for Employers
Build a centralized talent pool where employers can browse candidates who have completed mock interviews, with performance data and resume highlights readily available.
AI Resume Matching + Auto-Apply
Develop a system that automatically matches resumes to verified job openings and submits applications on behalf of the user—streamlining the process and reducing missed opportunities.