Mapping Multidisciplinary Subject Combinations to Career Outcomes: A Computational Model Inspired by NEP 2020

The National Education Policy (NEP) 2020 establishes a transformative framework for Indian education, emphasizing flexibility, interdisciplinarity, and holistic learning. However, students often face difficulty linking multidisciplinary subject selections to meaningful career outcomes. This paper presents a computational model that maps multidisciplinary subject combinations to potential career trajectories, aligning with NEP 2020’s learner-centric vision. The proposed framework integrates artificial intelligence (AI) reasoning and natural language processing (NLP) techniques, including TF- IDF and cosine similarity, to analyze student inputs—subjects, IQ, EQ, and aptitude—and recommend career domains consistent with NEP guidelines. The model is implemented using Fast API, MongoDB, and React, enabling real-time processing, subject mapping, and result visualization. Experimental evaluation demonstrates that computational reasoning can effectively operationalize NEP’s multidisciplinary philosophy, improving policy-aligned career guidance for secondary-level learners.