Aspire Guide: Career Guidance System Powered by Meta-Modeling

DOI

10.2991/978-94-6463-858-5_45How to use a DOI?

Keywords

Career Guidance; AI Chatbot; Machine Learning; NLP; Career Prediction

Abstract

After finishing their senior secondary studies, a majority of students face confusion about what to choose as a career path and take an ample amount of time to decide on it. Traditional counseling methods fail to provide personalized, data-driven recommendations. So many uncertainties and so much more to ponder over, choosing the right field of study is a challenge. Students like these need a personal career prediction based on personal choices and the homework done by our site can help them navigate through the complex decision-making process. This paper presents Aspire Guide, an AI-powered career recommendation system that leverages meta-modeling by combining Random Forest, Support Vector Machines (SVM), Decision Trees, and XGBoost to enhance career prediction accuracy. The system also integrates a conversational AI chatbot, utilizing Natural Language Processing (NLP) to interact with students and offer real-time career advice.

Through extensive evaluation, Aspire Guide achieved 94% accuracy, outperforming single-model approaches. By combining these incredible innovations from the fields of machine learning and natural language understanding (NLU), the foundation creates a customized, multi-faceted career advisory environment that provides students with the tools they need to make informed decisions regarding their future career path.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Amol Bhilare
AU  - Varad Sangole
AU  - Sarthak Sakore
AU  - Anurag Raut
AU  - Sania Khan
PY  - 2025
DA  - 2025/11/04
TI  - Aspire Guide: Career Guidance System Powered by Meta-Modeling
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 520
EP  - 531
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_45
DO  - 10.2991/978-94-6463-858-5_45
ID  - Bhilare2025
ER  -