Abstract

In today’s competitive job market, the transition from college to a successful career hinge on a student’s ability to navigate technical interviews with confidence and competence. Traditional interview preparation methods, although invaluable, often lack the adaptability required to address the unique needs and experiences of individual students. This paper solves this problem by presenting a comprehensive overview of an Automated Interview System that utilizes NLP and deep learning techniques to mimic the process of job interviews. The system comprises 3 modules, the first one being the resume analysis system, which is built on state-of-the-art NLP architecture. The subsequent module utilizes the output from the resume model to generate a set of relevant interview questions through semantic analysis by webscraping. One of the most innovative parts of this system is its ability to generate dynamic questions based on the candidate’s previous response using LSTM. The last module assesses the candidate’s responses and provides a summary of the complete interview process, providing expected answers to asked questions, and highlighting the areas of improvement required. This research outlines the platform’s development and its impact on students’ readiness, highlighting the potential to transform career readiness for college students.