Publication Date

Spring 2026

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Teng Moh

Second Advisor

Melody Moh

Third Advisor

Thomas Austin

Keywords

Large Language Model, Applicant Tracking System, Machine Learn- ing, Text Generation, Prompt Engineering

Abstract

The job market is a brutal battleground for those looking for a job. Resumes are used as a baseline to determine if a candidate is the right fit for the job. A good quality resume can determine if you get an interview or not. In this paper, we introduce ApplicantAI, an artificial intelligence (AI) tool to help candidates produce resumes quickly, for the job they are applying to. The goal of ApplicantAI is to help job seekers improve their resumes, so their application can pass the Applicant Tracking Systems (ATS), increasing their chances of landing an interview. Using techniques like prompt engineering and text generation, ApplicantAI completely automates the creation of quality resumes, reducing the amount of time needed to update a single resume. With the assistance of ApplicantAI, updating a resume can be reduced from twenty minutes to about five minutes. Applying to multiple jobs can now be done with ease using quality resumes. The tool streamlines the application processing by improving resume quality and reducing the amount of time one spends on improving their resume for the job of interest.

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Available for download on Saturday, May 22, 2027

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