Published on: June 2026
SMART INTERSHIP AND SKILL GAP ANALYSIS
Sumithra devi. K
P. Rajapadiyan
Article Status
Available Documents
Abstract
The job market is really competitive these days. This is because the world of technology is changing fast. As a result there is a difference between the skills that students have and the skills that companies want when they are looking for new employees. This makes it hard for students to figure out what they are good at learn skills and find an internship , that is right for them. To make this system work we will use intelligence, machine learning and natural language processing to compare a students resume, education and skills with the requirements of available internships. We will use methods like Cosine Similarity to find the best match between a students profile and an internship. The AI-based Skill Assessment and Internship Recommender System will allow students to upload their resume and take a skills test online. It will also have features to manage student profiles and track recommended internships in one easy-to-use place. Overall this tool will help students find internships quickly and make better decisions about their future careers. The AI-based Skill Assessment and Internship Recommender System will help students by providing accurate recommendations and preparing them for the job market. This will help bridge the gap, between what students learn in school and what companies want from their employees.
How to Cite this Paper
K, S. D. (2026). Smart Intership and Skill Gap Analysis. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.130
K, Sumithra. "Smart Intership and Skill Gap Analysis." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.130.
K, Sumithra. "Smart Intership and Skill Gap Analysis." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.130.
References
- Han, J., Kamber, M., & Pei, J., Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2011.
- Goodfellow, I., Bengio, Y., & Courville, A., Deep Learning, MIT Press, 2016.
- Jurafsky, D., & Martin, J. H., Speech and Language Processing, Pearson Education, 2023.
- Aggarwal, C. C., Recommender Systems: The Textbook, Springer, 2016.
- Ricci, F., Rokach, L., & Shapira, B., Recommender Systems Handbook, Springer, 2022.
- Pedregosa, F., et al., "Scikit-learn: Machine Learning in Python", Journal of Machine Learning Research, 2011.
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", 2019.
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: Jun 11 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.

