AI-Driven Personalized Learning and Career Recommendation System

Abstract

Data Structures and Algorithms (DSA) is a crucial requirement in the competitive technology market, but existing material, such as Data Structures sheets, provides a one-size-fits-all solution. This deprivation of individuality will result in poor learning processes because students are unable to concentrate on their own areas of weaknesses. This is also evident in career navigation, as students struggle to align their qualifications with relevant job vacancies, resulting in a poor and unfocused application process. The proposed research paper provides an intelligent, automated platform that bridges this divide by providing tailored career suggestions and learning plans. The technology assesses the user's coding skills and combines content-based filtering with language model refinement to produce a unique study plan that targets the user's areas of weakness [5]. The technology examines such variables as time complexity and approach to provide automated analysis of code submissions. It also compares job vacancies relevant to the skills displayed by the user. The primary contribution of this research is the creation of a one-stop shop that will automatize personalized mentoring. We demonstrate how AI can be applied in a practical way to close the feedback loop in technical education, providing students with a more direct and efficient path to success in the software field.