Implementing Large Language Model API For Interview Training Based On Job Description

Abstract

Good communication skills are essential for prospective workers, particularly college students, to clearly convey their abilities and understanding during job interviews. Despite the high demand for strong communication skills among employers, many students struggle with inadequate experience and skills, leading to reduced confidence and lower chances of securing desired jobs. Existing tools and resources for improving communication skills are limited, especially free ones, leaving students underprepared and less confident in interviews. To address this issue, a comprehensive solution is needed that provides free, customizable tools to enhance students' interview communication skills. Invisor is a web-based self-interview training application that leverages AI and Machine Learning to analyze users' behavior, responses, and facial expressions during simulated interviews. Accessible via desktop, laptop, or mobile devices, Invisor offers interactive and voice-recorded feedback to help students prepare for interviews effectively. By employing AI technologies, Invisor generates personalized interview questions based on job descriptions, enhancing the realism of the interview experience and providing tailored feedback to improve users' responses. Developed using agile methodologies, Invisor has undergone iterative enhancements, integrating user and stakeholder feedback promptly to improve usability and functionality. The combination of AI-driven simulations and agile development practices enables Invisor to meet current user needs and anticipate future demands, providing a robust tool for interview preparation in a competitive job market. This paper outlines the development and implementation of Invisor, demonstrating its effectiveness in enhancing interview skills and confidence among students and job seekers.

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