A SYSTEMATIC REVIEW OF AI POWERED TECHNICAL INTERVIEWER FOR STUDENTS
A SYSTEMATIC REVIEW OF AI POWERED TECHNICAL INTERVIEWER FOR STUDENTS
Authors:
Bhawana Kumari1, Annu Garg2, Rohit Sharma3,
Aman Singh4
1+2+3+4 Department of Computer Science and Engineering & Raja Balwant Singh Engineering Technical Campus Bichpuri, Agra
Abstract - In this paper, we propose an AI-Powered Interviewer for students to practice for domain-based interviews. The system includes NLP and speech recognition in to which we have put in a dynamic and very real setting for the students’ interview preparation. The interviewer dynamically generates domain specific questions based on user selected fields such as Web Development or Data Science. It supports both text and voice as input, and uses OpenAI Whisper for speech to text. The system supports resume based smart interviewing where you upload the resume, it gets parsed by Pyresparser and Spacy to extract the relevant information, extract the skills and generate the relevant questions. Also it runs a feedback and tracking system which logs in to a students’ performance over time, which in turn highlights their strong and weak points as well as key areas for growth. We have put together this platform with React.js and Tailwind CSS for the front end, FastAPI for the back end and we are using MongoDB for the database. For NLP portions we have used the Transformers and Sentence-Transformers libraries. The system has performed remarkably well for increasing student performance, serving personalized learning experiences, and delivering salient feedback to prepare for interviews.
Key Words: AI Powered Interviewer, NLP (Natural Language Processing), Resume-Based Questioning, Technical Skill Assessment, Student Interview Preparation.