Many undergraduate students in Computer Science (CS) and Software Engineering (SWE) struggle to identify suitable career paths, particularly when their academic performance, abilities, and interests do not fully align. To address this issue, a team led by Faruque and colleagues has proposed an AI-driven Student Assessment and Career Prediction System that integrates a Career Guidance Expert (CGE) system with a Web-Based Student Assessment (WBSA) platform, according to a paper published on arXiv.
The integrated framework enhances personalized career recommendations using artificial intelligence. The CGE helps students after graduation identify suitable jobs, research domains, and higher study opportunities aligned with their skills and interests. The WBSA platform strengthens interaction between students and faculty through assessments, personalized tasks, mentorship activities, and a secure real-time chat application.
The CGE system employs a Multilayer Perceptron (MLP) model trained on real-world academic and extracurricular data. Data was collected using the snowball sampling method from university students. The model achieved a validation accuracy of 94.71% in predicting personalized career paths, the paper reported. A pre-survey was conducted across universities to evaluate the proposed model before deployment.
Technical Architecture and Implementation
The WBSA system was developed as a modern web application using technologies such as React.js, Node.js, and PostgreSQL to ensure scalability, responsiveness, and secure data management. The source paper lists the technologies as "this http URL, this http URL, and PostgreSQL" — likely placeholders for common web frameworks. The entire system is supported by a secure cloud-based infrastructure, providing reliable performance while assisting graduates to select a suitable career path in the IT sector.
Validation and Feedback
In addition to the pre-survey, a post-survey involving both students and faculty was conducted to gather feedback and further improve the overall effectiveness and usability of the system. The researchers did not disclose specific survey results in the paper, but noted that the feedback was used to improve the system.
Implications for Computing Education
For Chief Technology Officers and technology procurement leaders in education, this system demonstrates how neural networks can automate personalized career guidance at scale. The combination of real-time assessment, mentorship tracking, and AI-based recommendations could reduce the burden on academic advisors and improve student outcomes. The use of cloud infrastructure ensures the platform can be deployed across institutions with minimal on-premise maintenance.
For enterprise technology buyers, the solution showcases an integrated approach using open-source technologies (React.js, Node.js, PostgreSQL) and cloud hosting, which could be replicated in other domains such as employee training and workforce development. The 94.71% validation accuracy suggests the model is highly predictive, though further real-world deployment data would be needed to confirm generalizability.
