An Integrated System for Real-Time Student Assessment and Career Guidance Using Neural Networks in Computing Disciplines

The article presents an AI-driven Student Assessment and Career Prediction System that integrates a Career Guidance Expert (CGE) with a Web-Based Student Assessment (WBSA) platform, aimed at helping undergraduate students in Computer Science and Software Engineering identify suitable career paths. The CGE utilizes a Multilayer Perceptron (MLP) model, achieving a validation accuracy of 94.71% based on real-world data, while the WBSA platform is built using Node.js, Next.js, and PostgreSQL for scalable and secure web interactions. This system is significant for practitioners as it combines AI with real-time assessments and personalized career guidance, potentially improving student outcomes in the IT sector.