AI-BASED INTERNSHIP RECOMMENDATION SYSTEM FOR PM INTERNSHIP SCHEME

The manual and often inefficient process of matching students to internships under the Prime Minister's (PM) Internship Scheme results in significant skill-opportunity misalignment, limited outreach, and reduced intern engagement.This paper proposes an AI-Based Internship Recommendation System (AI-IRS) designed to transform this allocation process.The AI-IRS leverages a Hybrid Recommendation Model, combining Content-Based Filtering, Collaborative Filtering, and a Skill-Gap Analysis Module powered by Natural Language Processing (NLP) and Machine Learning (ML).This system analyzes student profiles, including skills, academic history, and preferences, against the requirements of available internships, providing personalized and optimal matches.The primary goals are to enhance matching accuracy, promote equitable access for students from diverse backgrounds, and improve overall operational efficiency for the PM Internship Scheme, thereby supporting national skill development and employability initiatives.Initial system architecture and algorithmic principles demonstrate the potential for a transparent, scalable, and highly effective allocation platform.