future pathway ai-powered career prediction for students based on skills and academic performance
Gunasri J,I Prameela,Geethanjali V
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 19 April,2025 Pages:2013-2017
Abstract
In today's rapidly evolving job market, accurately understanding and predicting a student’s career trajectory is of paramount importance [1]. This study aims to forecast the future career paths of students by leveraging a combination of their academic performance—specifically college CGPA—and their assessed skills, which are increasingly vital in the modern, competitive employment landscape. By integrating historical job placement data, the proposed system evaluates whether a student's profile aligns with the types of companies where graduates are most likely to secure employment [4]. Furthermore, it goes beyond mere placement prediction by identifying the specific roles and responsibilities that suit individual students based on their performance and competencies. A key objective of this work is to bridge the gap between student preparedness and industry expectations. To that end, the system offers personalized guidance on the future skills that students should develop to meet evolving industrial demands. This personalized information is delivered in a secure and confidential manner, ensuring privacy and trust
Kewords
Keywords: Career Prediction, Machine Learning in Career Guidance, Predictive Analytics, Personalized Career Planning.
Reference
[1] R. Lavi, Y. J. Dori, N. Wengrowicz, and D. Dori, “Model-based systems thinking: Assessing engineering student teams,” IEEE Trans. Educ., vol. 63, no. 1, pp. 39– 47, Feb. 2020. [2] S. Avargil, Z. Kohen, and Y. J. Dori, “Trends and perceptions of choosing chemistry as a major and a career,” Chem. Educ. Res. Pract., vol. 21, pp. 668–684, Feb. 2020. [3] G. Shwartz, O. Shav-Artza, and Y. J. Dori, “Choosing chemistry at different education and career stages: Chemists, chemical engineers, and teachers,” J. Sci. Educ. Technol., pp. 1–14, Mar. 2021. [4] B. Bettin, M. Jarvie-Eggart, K. S. Steelman and C. Wallace, "Preparing First-Year Engineering Students to Think About Code: A Guided Inquiry Approach," in IEEE Transactions on Education, vol. 65, no. 3, pp. 309-319, Aug. 2022, doi: 10.1109/TE.2021.3140051. [5] M. Qamhieh, H. Sammaneh and M. N. Demaidi, "PCRS: the abbreviation Personalized CareerPath Recommender System for Engineering Students," in IEEE Access, vol. 8, pp. 214039- 214049, with the essential 2020, doi: 10.1109/ACCESS.2020.3040338. [6] B. Bettin, M. Jarvie-Eggart, K. S. Steelman and C. Wallace, "Preparing First-Year Engineering Students to Think About Code: A Guided Inquiry Approach," in IEEE Transactions on Education, vol. 65, no. 3, pp. 309-319, Aug. 2022, doi: 10.1109/TE.2021.3140051. [7] K. Steelman et al., “Work in progress: The perception of computer programming within engineering education: An investigation of student attitudes, beliefs, and behaviors,” in Proc. ASEE Annu. Conf. Expo., 2020, pp. 1–8. Accessed: Dec. 16, 2021. [8] N. Shrestha, C. Botta, T. Barik, and C. Parnin, “Here we go again: Why is it difficult for developers to learn another programming language?” in Proc. ACM/IEEE 42nd Int. Conf. Softw. Eng., 2020, pp. 691–701. [9] L. Harvey and J. R. Anderson, “Transfer of declarative knowledge in complex informationprocessing domains,” Human Comput. Interact., vol. 11, no. 1, pp. 69–96, 1996. Accessed: Dec. 16, 2021. [10] D. Belfadel, M. Zabinski, and I. Macwan, “Introduction to MATLAB programming in fundamentals of engineering course,” in Proc. ASEE [Annu. Conf. Expo., 2021, p. 11
