Published on: May 2026
AI-BASED CAREER GUIDANCE SYSTEM FOR ENGINEERING STUDENTS
THILAKAVATHI P VARSHANA G NISHANTHINI R
KANAGADURGA N
Article Status
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Abstract
Student input is captured through an assessment tool that includes questions regarding skills, knowledge, personal preferences, behaviors, and many more. Collected student data is analyzed using decision tree, random forest, and support vector machine algorithms to predict suitable career areas. After prediction, the system generates recommendations such as software development, data science, cloud computing, and cybersecurity, among others.
The system is designed using Python programming language and machine learning tools. Furthermore, it employs web technologies such as HTML and CSS to build its user interface. Experimental results show that the proposed AI system can help students make decisions in choosing appropriate career areas based on their strengths and preferences.
Keywords:
Artificial Intelligence; Career Guidance; Machine Learning; Recommendation System; Student Analysis
How to Cite this Paper
P, T., G, V. & R, N. (2026). AI-Based Career Guidance System for Engineering Students. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.644
P, THILAKAVATHI, et al.. "AI-Based Career Guidance System for Engineering Students." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.644.
P, THILAKAVATHI,VARSHANA G, and NISHANTHINI R. "AI-Based Career Guidance System for Engineering Students." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.644.
References
[1] Student’s Career Interest Prediction using Machine Learning, P. Shahane, P. Rinke, T. Datar, and S. Badjate, “Student’s Career Interest Prediction using Machine Learning,” International Research Journal of Engineering and Technology (IRJET), vol. 9, no. 11, Nov. 2022.[2] Career Path Prediction System Using Supervised Learning Based on Users’ Profile, H. Kolhe, R. Chaturvedi, S. Chandore, G. Sakarkar, and G. Sharma, “Career Path Prediction System Using Supervised Learning Based on Users’ Profile,” Lecture Notes in Electrical Engineering, Springer, 2023. DOI: 10.1007/978-981-19-7346-8_50.
[3] Career Prediction Website using Machine Learning, V. Jabade, J. Jadhav, M. Ingole, S. Joshi, and A. Kadgi, “Career Prediction Website using Machine Learning,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 11, no. 12, Dec. 2023.
[4] Career Guidance System Using Decision Tree, Random Forest, and Naïve Bayes Algorithm, C. U. Betrand, O. B. Aliche, and C. G. Onukwugha, “Career Guidance System Using Decision Tree, Random Forest, and Naïve Bayes Algorithm,” International Journal of Science, Technology and Society, 2024.
[5] Pathpilot: AI-Based Placement and Career Role Recommender, “Pathpilot: AI-Based Placement and Career Role Recommender,” International Journal of Engineering Research & Technology (IJERT), 2025.
[6] Career prediction system using machine learning, A. Dwivedi, F. Khan, M. R. Singh, N. Fatima, and A. K. Singh, “Career prediction system using machine learning,” Advances in Electronics, Computer, Physical and Chemical Sciences, 2025.
[7] Development of a Smart Career Advisory System Using Machine Learning Algorithms and Real-Time Chat Applications, K. D. Prasad et al., “Development of a Smart Career Advisory System Using Machine Learning Algorithms and Real-Time Chat Applications,” IJRASET, vol. 14, no. 3, 2026.
[8] An Intelligent Career Guidance System using Machine Learning, S. Vignesh, C. Shivani Priyanka, H. Shree Manju, and K. Mythili, “An Intelligent Career Guidance System using Machine Learning,” 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 987–990, IEEE, 2021.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: May 21 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.
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