AI-Powered Interview Assistant Chatbot
In today’s rapidly evolving digital landscape, artificial intelligence (AI) is revolutionizing howindividuals prepare for and attend job interviews. This project presents the development of an AIPowered Interview Assistant Chatbot designed to simulate real-life interviewscenarios, assist candidates in enhancing their performance, and help recruiters streamline thepreliminary screening process. The chatbot leverages cutting-edge Natural LanguageProcessing (NLP) techniques to engage in dynamic, human-like conversations, adapting its questionsand feedback based on the user's responses.Key features of the system include voice-to-text conversion, text-to-speech synthesis, and contextaware response generation, creating a seamless voice-based interaction that mirrors a humaninterviewer. The voice-to-text module ensures accurate transcription of userresponses, while the text-to-speech module enables the bot to vocalize questions in a natural tone,making the interview experience more immersive and interactive. Additionally, thechatbot is trained on diverse datasets covering various industries and job roles, allowing it to conductinterviews tailored to specific domains.To evaluate user responses, the system integrates machine learning models capable of sentimentanalysis, grammar checking, and keyword extraction, providing users withconstructive feedback on their communication skills, confidence level, and answer quality.This feedback helps users identify areas for improvement and build confidence before appearing foractual interviews.This AI-powered solution serves as a smart assistant for both job seekers and hiringprofessionals. It reduces time and resources spent on initial rounds of interviews, promotesinclusive and unbiased evaluation, and offers scalable interview practice opportunities. Futureenhancements may include emotion recognition, multilingual support, and personalizedcoaching, pushing the boundaries of AI in recruitment and career development.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-05-07
- DOI
- https://doi.org/10.5281/zenodo.15359928
- Primary Topic
- Biological and pharmacological studies of plants
- Type
- article
- Field-Weighted Citation Impact
- 0.00