International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
AI-Powered Video Interview Screener Priya Rathod1, Mantra Chavan2, Sarvesh Bhoir3, Piyush Rohra4, Swati Kulkarni5, Shubhangi Chintawar6 1, 2, 3, 4 Students, Department of Computer Engineering Vivekanand Education Society’s Polytechnic
Mumbai, India
5, 6 Lecturer, Department of Computer Engineering Vivekanand Education Society’s Polytechnic
Mumbai, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Traditional recruitment processes are often time-consuming, subjective, and inefficient due to manual
shortlisting and synchronous interviews. With the increasing number of applicants for a single role, organizations face challenges in maintaining fairness, consistency, and efficiency during initial screening. This paper presents an AI-powered video interview screener designed to automate and streamline the recruitment process. The proposed system integrates aptitude testing, asynchronous browser-based video interviews and Natural Language Processing (NLP) techniques for transcript generation and response evaluation. Candidates can complete assessments and interviews at their convenience, while recruiters gain access to automatically generated scores, transcripts, and comparative insights through a centralized dashboard. The system is developed using Next.js for the frontend and Supabase for backend services, including authentication, database management, and secure cloud storage of video responses. By minimizing human intervention in early-stage screening, the platform reduces bias, eliminates scheduling conflicts, and improves scalability. The proposed solution enhances recruiter efficiency and candidate experience and engagement, making the recruitment process more transparent, flexible, and data-driven. Key Words: AI Interview Screener, Recruitment Automation, Video Interview, Natural Language Processing, Cloud Computing, Supabase, Next.js.
1. INTRODUCTION Recruitment and candidate evaluation are critical processes for organizations and educational institutes, as they directly influence the quality of talent selection. With the rapid growth in the number of applicants for a single role or opportunity, traditional recruitment methods such as manual shortlisting, written tests, and synchronous interviews have become inefficient, time-consuming, and difficult to scale. These methods often depend heavily on human judgment, which may introduce bias and inconsistencies in candidate evaluation. In recent years, digital recruitment platforms have attempted to address these challenges through online assessments and virtual interviews. However, many existing systems focus on isolated stages of recruitment, such as either aptitude testing or video interviews, without providing an integrated and automated workflow. Additionally, the lack of transparency in evaluation criteria, limited customization for institutes, and scheduling constraints continue to affect both recruiters and candidates. To overcome these limitations, this paper proposes an AI-powered video interview screener that automates the early stages of the recruitment process. The system is designed to combine an initial quiz-based aptitude assessment with asynchronous video interviews, allowing candidates to participate at their convenience. The quiz section serves as the entry-level evaluation stage, enabling institutes to assess candidates’ subject knowledge and reasoning skills in a controlled and structured manner. The interview section further evaluates communication skills and response quality through recorded video answers. The proposed system leverages modern web and cloud technologies to ensure scalability, accessibility, and security. The frontend is developed using Next.js, enabling a responsive interface, while Supabase is used for authentication, database management, and secure cloud storage. An Artificial Intelligence layer based on Natural Language Processing (NLP) generates transcripts from video responses and performs objective analysis of candidate answers. By reducing manual intervention and introducing standardized evaluation mechanisms, the system aims to minimize bias, eliminate scheduling conflicts, and improve recruitment efficiency.
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