Career Navigator: Your AI Career Counselor

Career planning for college students is significantly influenced by career choices, which can be a daunting decision in today's society. Many students are uncertain about their future paths despite having various talents and skills. Our innovative approach to student career advising integrates machine learning (ML) for job prediction with a user-friendly chatbot interface, aiming to provide a comprehensive solution to this issue. The ML-based career prediction model analyzes several factors, including students' talents, educational backgrounds, and interests. We compared three ML algorithms—KNN, Random Forest, and Decision Tree—finding KNN to be the most accurate with a 97% success rate. The application, developed using Flutter and Google API, features a chatbot that answers career-related queries, allows appointment bookings, and includes a FAQ page. The website, designed with Flask, HTML, Jinja2, and Scikit-learn, offers a login system and a career prediction tool that assesses users' proficiency in various fields through a series of questions. It then suggests appropriate job fields, necessary skills, job locations, salary information, and relevant courses, enhancing the overall career counseling experience. This dual strategy ensures students receive dynamic and personalized guidance, helping them make informed decisions about their professional futures.

Authors

Publication Details

Published
2024-11-29
DOI
https://doi.org/10.1109/idicaiei61867.2024.10842724
Citations
3
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.36
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article

Career Navigator: Your AI Career Counselor

Asha Tyagi, Ashwini Barbadekar, Aryan Thaploo, Vedant Bhagwan
3 citations
Artificial Intelligence in Healthcare and Education
0.36
article

Career Navigator: Your AI Career Counselor

Asha Tyagi, Ashwini Barbadekar, Aryan Thaploo, Vedant Bhagwan
article en
3 citations

Abstract

Career planning for college students is significantly influenced by career choices, which can be a daunting decision in today's society. Many students are uncertain about their future paths despite having various talents and skills. Our innovative approach to student career advising integrates machine learning (ML) for job prediction with a user-friendly chatbot interface, aiming to provide a comprehensive solution to this issue. The ML-based career prediction model analyzes several factors, including students' talents, educational backgrounds, and interests. We compared three ML algorithms—KNN, Random Forest, and Decision Tree—finding KNN to be the most accurate with a 97% success rate. The application, developed using Flutter and Google API, features a chatbot that answers career-related queries, allows appointment bookings, and includes a FAQ page. The website, designed with Flask, HTML, Jinja2, and Scikit-learn, offers a login system and a career prediction tool that assesses users' proficiency in various fields through a series of questions. It then suggests appropriate job fields, necessary skills, job locations, salary information, and relevant courses, enhancing the overall career counseling experience. This dual strategy ensures students receive dynamic and personalized guidance, helping them make informed decisions about their professional futures.

Openalex Percentile: Top 34%
Artificial Intelligence in Healthcare and Education
0.36
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Career Navigator: Your AI Career Counselor — Asha Tyagi, Ashwini Barbadekar, et al. · (2024) | TGRS Research Map | TGRS