CareerX: AI-Powered Career Path Recommender System for College Students

CareerX is an AI-powered career path recommender system designed to help college students make informed career decisions based on their skills, interests, and academic background. This research paper presents the design, architecture, and implementation of the CareerX mobile application, which integrates Natural Language Processing (NLP), machine learning algorithms, and real-time job data to deliver personalized career recommendations. Key features include a resume analyzer, learning roadmap generator, and job finder module. The system was tested with users from Dronacharya College of Engineering, achieving a 90% satisfaction rate and showing significant improvements in resume quality and job matching. This preprint documents the methodology, evaluation results, and future scope of the project.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-20
DOI
https://doi.org/10.5281/zenodo.15250948
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

CareerX: AI-Powered Career Path Recommender System for College Students

Zenodo (CERN European Organization for Nuclear Research)
Recommender Systems and Techniques
article

CareerX: AI-Powered Career Path Recommender System for College Students

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Abstract

CareerX is an AI-powered career path recommender system designed to help college students make informed career decisions based on their skills, interests, and academic background. This research paper presents the design, architecture, and implementation of the CareerX mobile application, which integrates Natural Language Processing (NLP), machine learning algorithms, and real-time job data to deliver personalized career recommendations. Key features include a resume analyzer, learning roadmap generator, and job finder module. The system was tested with users from Dronacharya College of Engineering, achieving a 90% satisfaction rate and showing significant improvements in resume quality and job matching. This preprint documents the methodology, evaluation results, and future scope of the project.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 37%
Recommender Systems and Techniques
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