The Problem
Problem Statement
Job seekers often lose confidence after repeated interview rejections, focusing more on mistakes than on progress. Without structured feedback or tracking, they struggle to see improvement and maintain motivation.
Initial HMW Exploration
At the beginning, I explored three directions for how AI might support job seekers in their interview preparation:
Research & Insights
Interviews (3 participants)
I interviewed three job seekers via Zoom. Although their preparation styles varied, all lacked a structured way to evaluate their interviews. They showed strong interest in AI providing objective analysis (e.g., STAR response completeness) but little interest in AI-driven emotional support.
Survey (24 responses)
The survey results reinforced and quantified these findings:
42.9% do not track their job applications, and 66.7% do not record their interview experiences.
90% evaluate performance only through personal reflection.
66.7% reported struggling with the lack of feedback from interviewers.
Job seekers wanted AI to help with performance patterns (81%), practice questions (81%), and feedback on clarity/relevance (71.4%).
Only 28.6% were interested in AI for motivation or emotional encouragement.
Key Findings
Job seekers lack structured methods to track interviews and progress.
Most rely on personal reflection, not external or structured feedback.
There is strong demand for objective analysis, tailored practice questions, and actionable feedback.
Repeated rejection lowers confidence, making preparation discouraging.
Key Pain Points
Tracking interviews is time-consuming, so most people skip it or rely on simple tools.
There is little to no post-interview feedback.
Many are uncertain how to improve and stand out.
Repeated rejection lowers confidence, making preparation feel discouraging.
The Emotional Need
Beneath these functional gaps lies a deeper need: job seekers want to feel in control of their progress and to rebuild confidence after setbacks. A tool that provides visible evidence of improvement can help shift their focus from failure to growth.
User Journey Map
Refined Direction
Refined HMW
Why AI?
To understand where AI can truly add value without replacing human strengths, I created a Cognitive Offloading Matrix. This framework maps out:
What should remain human-led
What can be fully offloaded to AI
Where a human–AI partnership works best
Design Solutions
Game-Based AI-Powered Interview Tracker
A structured and motivating system where users log interviews, receive AI-generated STAR-based feedback, and visualize progress through skill trees and points. This concept focuses on building confidence by making improvement visible.
AI-Based VR Mock Interview System
An immersive, realistic mock interview experience using VR, where users can practice body language, tone, and timing in a safe environment. This concept reduces nervousness and builds confidence through simulation.
Smart AI-Powered Glasses for Post-Interview Analysis
A conceptual tool that records real interviews (with consent), analyzes verbal and nonverbal communication, and provides feedback. It supports deep reflection for users who want detailed review of their performance.
Design Iteration
Early Sketches
Through early sketches, I defined a straightforward flow where users move from choosing a topic to answering a question, receiving AI feedback, and tracking progress.
