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Capstone Project
AI Powered Job Hunt
Web UI/UX
UX Researcher
UX Designer
Client
Timeline
Tools
Team
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16 weeks
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1 Graduate UX Designer
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My role
As the sole designer and researcher, I led end-to-end UX efforts, from framing the problem and conducting user research to designing, prototyping, and testing a fully functional concept.
Overview
While online job platforms like LinkedIn and Indeed offer vast opportunities, college students often struggle to manage the job search process efficiently. Between coursework, part-time jobs, and application fatigue, many fail to apply consistently or lose track of opportunities.
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This project set out to reimagine the job search experience for students by integrating AI assistance and structured task management within LinkedIn’s ecosystem. The focus was on simplifying application workflows, tailoring resumes intelligently, and helping users stay organized and motivated through every stage of their job hunt.
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The goal was to help students apply smarter, not harder, reducing stress, improving visibility into their progress, and enabling more confident, personalized applications powered by AI transparency and human control.
When Job Searching Becomes a Second Full-Time Job
For most college students, job hunting isn’t a simple task. It’s an ongoing cycle of searching, tailoring resumes, filling out applications, and tracking progress across multiple platforms. Between academic deadlines, part-time work, and extracurriculars, the process often becomes chaotic and mentally draining.​
​Students shared that applying for jobs felt like taking on a second job itself, time-consuming, repetitive, and disorganized. Many described the experience as “overwhelming” or “never-ending,” leading to inconsistent efforts and missed opportunities.​
Impacting quality of applications
Reduced chances of landing interviews
Fewer applications
​​​This challenge sparked the goal of designing a streamlined, AI-assisted experience that could reduce repetitive work and make job searching feel manageable, transparent, and even motivating.
Understanding Why the Process Feels Broken
Before jumping into design, I wanted to understand how students actually feel about job hunting, what slows them down, overwhelms them, or makes them lose motivation. To do this, I combined Reddit-based surveys with in-depth user interviews, reaching students from various disciplines actively applying for internships and entry-level roles.
Reddit Surveys: Gathering Broad Sentiments
To collect diverse insights quickly, I ran anonymous polls and open-ended surveys on Reddit communities like r/college, r/UXDesign, and r/jobs.
Goal
Findings
To understand which AI tools are currently in use, how often they are used, what features users find helpful or frustrating, and their overall attitudes toward automated job applications
Users of tools like JobCopilot, Wobo AI, and LoopCV value time-saving features.
57.1% users used these automation tools daily.
The main motivation to use an AI automation tool were time saving and being able to apply to jobs more quickly.
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Frustrations include Inaccurate autofill requiring manual corrections and lack of personalization in resumes/cover letters
Move around or click on the above images to view the reddit survey results
User Interviews: Uncovering Emotional Pain Points
To go deeper, I conducted semi-structured interviews with five students actively applying for internships and full-time roles.
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Goal
Findings
To understand how students currently manage the application process, how they feel about using AI for resume tailoring and job applications, and what specific pain points they face when balancing job searching with academic and personal responsibilities.
Students struggle to balance job applications with academics and part-time work.
Many feel overwhelmed by the repetitive nature of filling out applications and tailoring resumes.
Frustration were found with third-party job portals (e.g., Workday, Oracle Cloud) due to repeated logins and manual entry.
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Auto-apply tools are desired but students need them to maintain transparency and allow some personalization.
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Application tracking is inconsistent. Some use spreadsheets, others rely on memory or mails.
What Other Job Tools Missed
I focused on LinkedIn because it’s where most students already manage their professional identity and job search. It has the reach, trust, and infrastructure.
While exploring other automated job portals like JobCopilot, LoopCV, and Simplify, I noticed that they automate large parts of the application process but fall short in key areas that matter to students:
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They don’t generate role-specific resumes, reducing the relevance of applications.
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They lack integration with LinkedIn, disrupting a seamless job application experience.
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The auto-generated resume often feels generic and impersonal, missing individual tone and authenticity.
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Many require users to pay extra for subscriptions, creating a financial barrier for students seeking affordable support.
What's missing in Linkedin:
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No way to track resume versions or see which one was used where.
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No detailed status updates or progress tracking after applying.
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Limited personalization, with job suggestions based on keywords rather than career goals.
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No built-in deadline or timeline tracking to help plan applications.
Framing the Opportunity: Designing AI That Empowers, Not Replaces
Students don’t just need automation, they need intelligent support that keeps them in control.
My Solution
Access
Speed
Clarity
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Control
Automation Tools
Existing tools focused on speed or access but lacked clarity, personalization, and ownership.
This opened an opportunity to design an AI-powered job assistant that helps users organize applications, track timelines, and tailor resumes, all while staying within the familiar LinkedIn ecosystem.
The goal: not to apply faster, but to apply smarter, with structure, confidence, and relevance.
From Brainstorming to Scribbles to Smart Flows
Choosing the Kanban System
To identify the most effective way to track job applications, I conducted a review analysis of organizational tools like Trello, Notion, and EZTrackr. Through user feedback and feature comparisons, the Kanban board emerged as the most intuitive and motivating system for managing progress.
Its visual structure and drag-and-drop simplicity made complex application tracking feel approachable and organized. Unlike spreadsheets, Kanban offered students a way to visualize their journey from
Applied → Interviewing → Offer
while maintaining control and clarity.
User reviews consistently highlighted how seeing progress reduced mental fatigue and encouraged consistent follow-through. Integrating this structure into the job search experience allowed users to stay engaged, track tasks confidently, and maintain momentum.
Mapping the Journey
To pinpoint where students faced the most friction, I began by creating a user journey map of the entire job search experience, from discovering roles to tracking application outcomes. This visualization revealed emotional highs and lows, showing where motivation dropped due to repetitive tasks, disorganization, and lack of visibility.
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Through this mapping exercise, I identified three key touchpoints within the LinkedIn ecosystem where the experience could be meaningfully improved:
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Pre-Application
AI tailors resumes based on the selected role and editable LinkedIn experience, helping users apply strategically rather than generically.
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During Application
AutoApply submits applications where the user is a Top Applicant.
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Post-Application
A Kanban board logs job status, resume used, and highlights applications needing review.
Mapping the Journey
After defining the three key touchpoints, I began translating these concepts into early sketches and wireframes. My goal was to make the process feel structured, guided, and human-centered, without disrupting the familiar LinkedIn interface.
Three Features, One Seamless Experience
The final solution was designed as an integrated extension within LinkedIn, connecting every step of a student’s job search into one guided, intelligent experience. Each feature aligned with a key pain point uncovered during research, helping users feel more organized, confident, and in control without leaving the platform.
Play the video to view the final prototype flow:
AI Resume Tailoring
Step 1: Job Role Selection
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Progress bar reduces cognitive load and improves clarity.
Role selection helps AI generate more relevant, goal-aligned resumes.
Users can select the type of opportunity they’re seeking.
Step 2: Review Work Experience
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Prompts users to review and refine imported experience data
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Ensures accuracy before AI generates resumes
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Builds trust by keeping users in control of the personalization process
Multiple resume versions provide flexibility and user choice
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Clear labels help users easily compare and select the right version
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Increases likelihood of alignment with user voice and goals
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Editing options build trust in AI-generated content​
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Breaks resume content into editable, manageable sections
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Real-time preview enhances transparency and user confidence
AutoApply Tool for Top Applicant
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Auto-Apply Toggle in LinkedIn Jobs Section
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Toggle provides users with full control over activating Auto-Apply
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Familiar, intuitive design minimizes friction
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Empowers users to start automation on their own terms
“Applied” tag appears on listings where the system has auto-applied
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Applied state gives clear visual confirmation of submission
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Reassures users that the system is actively working for them
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Enhances trust by addressing automation’s common drawback: lack of visibility
Kanban-style Application Tracking
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My Jobs Section
Gives users an organized, visual overview of their job search within LinkedIn.
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Mirrors familiar tools like Trello and EzTracker for intuitive use.
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Solves the problem of scattered or inconsistent job tracking.
Application Details Card in My Jobs
Centralizes all job application details in one view
Tracks which resume version was used, reducing confusion reported in interviews
Helps users recall submissions and manage follow-ups
Needs Review Tab
Helps users identify where AI was unable to complete the application.
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Prioritization tools and reminders reduce drop-off and support time management by guiding attention to pending tasks
Needs Review Application Card
Displays deadlines and High Priority tags for urgent roles
​Suggests the best resume version based on job-role match
​​Uses red highlights to flag missing inputs​
Provides a direct link for quick completion
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What did the users say?
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Users liked the guided flow and 3 resume versions.
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Confirmation screen felt reassuring and transparent while using the Auto-apply tool.
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Users appreciated the Top Applicant-only condition
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Visual layout was intuitive and easy to follow in Kanban board.
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Users liked seeing resume versions used in Kanban board.
Closing Notes
This project reinforced the importance of designing AI systems that respect human control and emotional context.
I learned how combining structured organization (Kanban) with personalized intelligence (AI) can transform overwhelming workflows into clear, confidence-building experiences.
Balancing efficiency with empathy became the driving principle of this project, a lesson I continue to carry into every design challenge.