International Journal of Science, Engineering and TechnologyAuthors: Parikshit Jaybhaye, Hemangi Koli, Nandini Thakkar, Om Latkar, Prof. M. S. Patil
Abstract: In the contemporary competitive job market, ef- fective interview preparation is crucial for candidate success. However, traditional methods often lack personalization, realism, and comprehensive feedback. This survey provides a compre- hensive overview of AI-powered interview preparation systems that leverage Natural Language Processing (NLP) and Machine Learning (ML) to offer personalized mock interviews and de- tailed performance analytics. We examine the key components of these systems, including resume parsing, adaptive question gener- ation, multimodal response evaluation (encompassing knowledge, behavioral cues, and speech analysis), and the provision of action- able feedback. The study explores various technical approaches, such as semantic matching for question selection, Large Language Models (LLMs) for dialogue generation, and advanced evaluation metrics. Furthermore, we identify open challenges and future research directions, such as enhancing multimodal analysis, improving the adaptability of AI interviewers, and addressing ethical considerations. By synthesizing recent advancements, this survey aims to elucidate the potential of AI to revolutionize interview training, making it more accessible, effective, and aligned with real-world requirements.
