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

2026-06-14 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

An AI-driven Student Assessment and Career Prediction System has been proposed to help undergraduate Computer Science and Software Engineering students identify suitable career paths. This integrated framework combines a Career Guidance Expert (CGE) system with a Web-Based Student Assessment (WBSA) platform. The CGE system utilizes a Multilayer Perceptron (MLP) model, trained on real-world academic and extracurricular data collected via snowball sampling, achieving a 94.71% validation accuracy in predicting personalized career paths. The WBSA platform, developed using Node.js, Next.js, and PostgreSQL, facilitates student-faculty interaction through assessments, tasks, mentorship, and secure chat. The entire system operates on a secure cloud-based infrastructure, providing reliable performance for career selection in the IT sector.

Key takeaway

For university administrators and career counselors aiming to improve student retention and career alignment in computing disciplines, implementing an integrated AI-driven system like this offers a robust solution. You can provide personalized career recommendations, facilitate real-time assessments, and strengthen student-faculty interaction. This approach helps students identify suitable jobs, research domains, or higher study opportunities, directly addressing common struggles in career path identification.

Key insights

An integrated AI system can accurately guide students toward suitable careers by aligning abilities and interests.

Principles

• Personalized career recommendations benefit from AI integration
• Snowball sampling can effectively gather academic/extracurricular data

Method

Integrate a Multilayer Perceptron (MLP)-based Career Guidance Expert (CGE) with a Web-Based Student Assessment (WBSA) platform for real-time assessment, guidance, and interaction, supported by cloud infrastructure.

In practice

• Employ MLP models for predicting personalized career paths
• Develop scalable web platforms using Node.js, Next.js, and PostgreSQL

Topics

• Student Career Guidance
• AI in Education
• Multilayer Perceptron
• Web-Based Assessment
• Computer Science Education
• Node.js

Best for: AI Student, AI Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.