Unlocking Futures: A Natural Language Driven Career Prediction System for Computer Science and Software Engineering Students
Project Overview
The document explores the application of generative AI, specifically a Natural Language Processing (NLP) driven career prediction system, in the context of education for Computer Science (CS) and Software Engineering (SWE) students. It emphasizes the critical need for personalized career guidance that aligns with students' unique skills and interests, facilitating informed decision-making regarding their future careers. By employing machine learning (ML) algorithms to analyze student data, the system aims to accurately predict appropriate career paths, thereby enhancing the effectiveness of academic advisers and career counselors. The findings indicate that such AI-driven tools can significantly improve the quality of career guidance, ensuring that students receive tailored recommendations that reflect their individual capabilities and aspirations. Ultimately, the integration of generative AI in educational settings can lead to more informed career choices, better alignment of student skills with industry demands, and improved outcomes for graduates entering the workforce.
Key Applications
AI-assisted career prediction model using Natural Language Processing
Context: Educational context for undergraduate Computer Science and Software Engineering students
Implementation: Data was collected through Google Forms from various universities and processed using machine learning techniques, including multiple classification algorithms.
Outcomes: The system provides accurate career suggestions based on students' skills, interests, and related activities, helping them navigate career options more effectively.
Challenges: Challenges include ensuring the accuracy of predictions, the quality of data collected, and addressing the complexity of career decision-making.
Implementation Barriers
Data Quality
The quality of data collected through surveys may vary, which can affect the accuracy of predictions.
Proposed Solutions: Implement rigorous data cleaning and preprocessing techniques to ensure data quality and relevance.
Model Complexity
The complexity of machine learning models may lead to issues such as overfitting, affecting their generalization to new data.
Proposed Solutions: Utilize regularization techniques and cross-validation to balance model complexity and performance.
Project Team
Sakir Hossain Faruque
Researcher
Sharun Akter Khushbu
Researcher
Sharmin Akter
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Sakir Hossain Faruque, Sharun Akter Khushbu, Sharmin Akter
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai