An Integrated System for Real-Time Student Assessment and Career Guidance Using Neural Networks in Computing Disciplines
arXiv:2606.15831v1 Announce Type: new Abstract: 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, this study proposes 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. Within the integrated framework, CGE enhances personalized career recommendations using AI while also assisting students a
The proliferation of accessible AI tools and increased focus on personalized education makes deep integration of AI into guidance systems feasible and timely.
This development signifies a growing trend in leveraging AI to enhance educational outcomes and address critical skill-job mismatches, potentially improving workforce development.
The paradigm shifts from traditional, often generic, career counseling to dynamic, data-driven, and personalized guidance directly integrated with student performance assessment.
- · Students in Computing Disciplines
- · Educational Technology Providers
- · Universities and Colleges
- · Employers seeking talent
- · Traditional Career Counseling Services
AI-driven platforms provide tailored career recommendations, reducing student uncertainty and improving academic relevance.
Improved student placement rates could lead to a more efficient talent pipeline for tech industries, boosting regional economies.
Mass adoption of such systems might necessitate standardized 'AI tutor' or 'AI guidance' certifications for educational institutions and developers.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI