A Survey of AI-Driven Mock Interviews Using GenAI and Machine Learning (InterviewX)

With the accelerated demand for assessment tools that work efficiently and at scale, innovations in automated interviewing platforms have proliferated. In this research, InterviewX- a system enabled with artificial intelligence, works on RAG (Retrieval-Augmented Generation) and QLoRA (Quantized Low-Rank Adaptation). InterviewX aims to create an environment for an actual interview where domain-specific questions can be triggered and give personalized feedback in preparation for a candidate-an effective answer. Fine-tuning on large language models is enabled in InterviewX through 4-bit NormalFloat quantization for maximum computational efficiency with little or no installation effort. RAG also integrates real-time information retrieval while keeping the questions up to date with industry standards. The review mechanism within the platform used by InterviewX aligns the GPT-4 evaluation and the human assessment in identifying the areas and points of improvement in both the quality of question generation and analysis of candidates' feeding. In that way, InterviewX presents a scalable, low-cost, quality interview simulation solution for the preparation of job seekers and assessment of recruiters.