International Journal of Science, Engineering and TechnologyAuthors: Khilari Atharv Sopan, Professor Bangar A.P., Professor Bhosale S.B., Professor Karad.V.A
Abstract: Large public internship programmes must match very large numbers of candidates to a limited number of company-posted slots while respecting preferences, eligibility rules and priority for under-represented groups. The Prime Minister's Internship Scheme (PMIS) of India, launched as a pilot in October 2024, faces exactly this allocation problem. This paper presents a focused narrative review of verified sources relevant to an AI smart allocation engine for PMIS. It examines four connected themes: the design of the scheme and its reported pilot outcomes, matching theory and allocation mechanisms, recommendation methods used in e-recruitment, and fairness and reservation constraints in Indian centralised allocation. The literature shows that official guidance describes a portal that shortlists candidates using preferences, company requirements and priority groups, that companies made 82,077 offers in Round I of the pilot against more than 1.27 lakh posted opportunities, that only about 33% of offers were accepted over the first two rounds, and that deferred acceptance based allocation with reservations has been used at scale in Indian engineering admissions. However, the exact PMIS shortlisting method is not public, reasons for non-acceptance are not evidenced, and no source evaluates an AI allocation engine for internships. The review contributes a comparative analysis of representative sources, a proposed conceptual seven-layer allocation engine architecture, and a prioritised list of research gaps.
