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@article{187258,
        author = {Harshada Mahesh Dhobale and Archana burujwale and Siddhesh Sabnis and Kaushtubh Lanke and Nikita Waghmare},
        title = {AI Mock Interviewer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7392-7399},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187258},
        abstract = {In today’s highly competitive job market, excelling in technical interviews has become crucial for candidates aspiring to secure positions in top companies. However, access to personalized interview preparation tools remains limited, especially those that provide real-time, meaningful feedback. This paper presents the design and development of an AI-powered mock interview platform that aims to bridge this gap by leveraging cutting-edge web technologies and generative AI. The platform is built using a full-stack architecture comprising React and Next.js for a dynamic frontend experience, Clerk for robust user authentication, Drizzle ORM with PostgreSQL for efficient backend data handling, and Gemini AI for generating domain-specific interview questions in real-time. Users can upload their resumes (optional), select their desired job role or interview category, and begin a live interview session where AI-generated questions are posed. Candidate responses are recorded via webcam and analysed using AI algorithms to assess various performance metrics, including tone, fluency, relevance, and confidence. A unique feature of this system is its automated feedback generation module, which provides actionable insights and personalized analytics to help users improve. The feedback is visualized through an interactive performance dashboard, making it easier for users to track progress over time. With scalability and security integrated into its core design, this platform caters to a wide user base including students, fresh graduates, and working professionals preparing for interviews. The proposed system not only enhances accessibility to highquality interview preparation tools but also provides a cost-effective and intelligent alternative to traditional coaching methods. This research highlights the technical architecture, implementation challenges, and evaluation of the platform's effectiveness, offering a blueprint for future developments in AI-driven educational tools. give in few ines in same format},
        keywords = {Mock Interview, Next.js, React, AI Interviewer, Gemini AI, Full-Stack Development, Drizzle ORM, PostgreSQL, Video Feedback System},
        month = {November},
        }