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June 19, 2026Open Access

Intelligent-Based Career Guidance Information Management

Key Points

This study aims to create a computerized career guidance information management system to improve traditional counseling methods.
Developed a web-based platform using Python (Flask, TensorFlow) for backend and HTML, CSS, JavaScript for frontend.
Implemented a recommendation engine leveraging machine learning algorithms for tailored career suggestions.
Included features such as user registration and dynamic recommendation refinement based on questionnaire responses.
The system provides personalized career recommendations based on users' skills and interests.
Adaptive recommendations improve over time, offering more accurate career paths as more data is collected.
Highlights the efficiency and accessibility of AI-powered career counseling solutions.

Abstract

This study develops a computerized career guidance information management system to overcome the limitations of traditional career counseling methods, which often lack accessibility, real-time updates, and personalization. The system uses AI and ML to provide tailored career recommendations based on users’ skills, interests, and questionnaire responses. The web-based platform, implemented using Python (Flask, TensorFlow) for the backend and HTML, CSS, and JavaScript for the frontend, features user registration, skill selection, and dynamic recommendation refinement through questionnaires. The recommendation engine employs machine learning algorithms to deliver accurate, adaptive career suggestions. This research bridges the gap between conventional counseling and modern technologies, offering a scalable, data-driven career guidance solution. This study highlights the potential of AI-powered systems to provide personalized, accessible, and efficient career advice in alignment with digital education advancements

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