FutureBuddy: Career Guidance System

Abstract

Career selection remains one of the most critical and confusing stages in a student’s academic journey. Conventional counseling systems are limited by scalability and personalization, often failing to match individual abilities with suitable professions. FutureBuddy is a web-based Career Guidance System designed to overcome these limitations through structured, data-driven decision support. The system evaluates students across four domains—Personality, Interests, Skills, and Values—and applies a deterministic heuristic scoring engine to identify the most appropriate career paths. In addition to recommendations, FutureBuddy generates complete roadmaps detailing skills, certifications, courses, and milestones required to achieve each career. Built using the MERN stack (MongoDB, Express, React, Node.js) and integrated with Firebase Authentication, the system provides a personalized dashboard, progress tracking, and shareable career summaries. Initial testing shows that FutureBuddy improves decision clarity, increases engagement, and simplifies the overall career-planning process.

Introduction

Students often struggle with career selection due to limited structured guidance and reliance on subjective counseling methods. FutureBuddy addresses this challenge with a technology-driven, rule-based system that matches students to careers based on their personality, interests, skills, and values.

The platform features four structured assessments whose results are processed through a deterministic heuristic model to generate weighted suitability scores for multiple career paths. Top recommendations are presented with personalized roadmaps detailing required skills, certifications, learning resources, internships, and professional milestones.

Built on a modular architecture, FutureBuddy combines a React.js/Tailwind CSS frontend with a Node.js/Express.js backend and MongoDB for data storage, while Firebase Authentication ensures secure user access. Dashboards provide both students and institutions with insights into progress, skill gaps, and emerging career trends.

A controlled evaluation with 50 undergraduate students showed high satisfaction: 90% found recommendations relevant, 85% valued the roadmap, and 80% gained confidence in career planning. FutureBuddy offers transparent, actionable, and scalable career guidance, though future improvements could include adaptive learning and integration with dynamic labor-market data.

Conclusion

FutureBuddy demonstrates the effectiveness of structured, rule-based digital systems in delivering personalized career guidance. By combining modern web technologies with logical assessment methodologies, the system transforms subjective decision-making into a guided, data-backed process. Future enhancements include integration of AI/ML techniques to personalize recommendations further, synchronization with live job and certification databases, and support for multilingual interfaces. This approach represents a step forward toward democratizing career guidance and helping students transition from uncertainty to informed career planning.

References

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Copyright

Copyright © 2025 Bina Rawatkar, Rohit Umredkar, Vishal Nemade, Vaishnavi Tandekar, Shrishakti Sharma, Priyanka Choudhari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.