International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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SETU: A Fairness-Aware AI Framework for Optimizing Student-Internship Matching in Large-Scale National Schemes

Author(s) Meet Bhuva, Aditya Kumar Gautam, Dharambir Singh Sidhu, Dr. NB Prakash, Kumar Tanmay
Country India
Abstract Internship programs are pivotal for bridging the gap between academic knowledge and industry demands. In India, the Prime Minister Internship Scheme (PMIS) aims to provide one crore internships over five years, yet it faces significant challenges, including a mere 5% conversion rate from application to
participation, skill-opportunity mismatches, and systemic biases. This paper introduces SETU (Smart Employment and Training Unification), a novel AI-driven framework designed to overhaul the PMIS matching process. SETU leverages a multi-faceted approach, integrating Natural Language Processing (NLP) for
deep resume and job description analysis, advanced embedding models for semantic skill matching, and a predictive analytics module to estimate a candidate’s likelihood of joining. A core contribution of our work is the integration of a fairness-aware optimization layer, specifically designed to mitigate geographic and demographic biases, ensuring equitable access for students
from underrepresented backgrounds. We propose a scalable, cloud-based architecture that can handle millions of users while providing personalized, fair, and efficient internship recommendations. This system aims to significantly increase the conversion rate, enhance the overall impact of the PMIS, and ensure that the right student is matched with the right opportunity on a national scale.
Keywords Recommender Systems, Natural Language Pro- cessing, Fairness-Aware Machine Learning, Internship Matching, Predictive Analytics, Skill Extraction, PMIS.
Field Computer > Data / Information
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-03
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.72470
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E-ISSN 2582-2160
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