Unlocking Futures: A Natural Language Driven Career Prediction System for Computer Science and Software Engineering Students

TL;DR

This paper presents an AI-driven system that uses NLP and machine learning to predict suitable career paths for CS and Software Engineering students, enhancing personalized career guidance.

Contribution

It introduces a novel NLP-based dataset and compares multiple ML and DL algorithms for accurate career prediction tailored to students' skills and interests.

Findings

Multiple ML and DL models achieved high prediction accuracy.

The system provides personalized career suggestions for students.

The approach improves career guidance effectiveness for CS and SWE students.

Abstract

A career is a crucial aspect for any person to fulfill their desires through hard work. During their studies, students cannot find the best career suggestions unless they receive meaningful guidance tailored to their skills. Therefore, we developed an AI-assisted model for early prediction to provide better career suggestions. Although the task is difficult, proper guidance can make it easier. Effective career guidance requires understanding a student's academic skills, interests, and skill-related activities. In this research, we collected essential information from Computer Science (CS) and Software Engineering (SWE) students to train a machine learning (ML) model that predicts career paths based on students' career-related information. To adequately train the models, we applied Natural Language Processing (NLP) techniques and completed dataset pre-processing. For comparative analysis, we utilized multiple classification ML algorithms and deep learning (DL) algorithms. This study contributes valuable insights to educational advising by providing specific career suggestions based on the unique features of CS and SWE students. Additionally, the research helps individual CS and SWE students find suitable jobs that match their skills, interests, and skill-related activities.