Crack It Up: Practice-Based Application for Interview Rounds

Abstract

To develop an AI-powered IOS application that simulates realworld interview scenarios and enhances interview preparedness by providing real-time feedback on verbal and non-verbal communication.

The purpose of this thesis is to demonstrate the development and implementation of "Crack It Up: Practice Based Application For Interview Round," an innovative application for iOS, for students to enhance the essential skills for gaining employment. The application focuses on students' preparatory practices for assessments on campus and beyond, for example, aptitude assessments, coding tests, and even training experiences. The process was designed in the programming language Swift in an Xcode environment so students can interactively practice multiple-choice questions in real time while measuring their progress and performance within areas of their academic and professional disciplines.

Overall, this project aims to create pathways to learning that connect the traditional academic learning environment to job market demand, while providing a personalized, approachable, and timely resource that supports students in preparing for competition in preparation for testing and interviews. Each student would be able to practice questions about aptitude or coding that have been contextualized in their respective disciplines. Thus, while the app addresses a narrow scope of preparation, the app can be designed and utilized at scale across all academic disciplines.

Moreover, the present study points to future improvements that will entail developing a virtual AI-based personalized interviewer that can hold dynamic mock interviews, offer personalized findings, and adjust questioning dynamically based upon both the students' skills and career goals. This will act as a tool for continual building of the students' confidence and skills with respect to interviewing in real world contexts.

Overall, the study offers a contribution to the educational technology for employability of students which will (if build) serve in scalable, adaptable, and student-sustaining way to prepare students for future employment assessments. The study indicates the potential of educational technology (via combinations of mobile technology and artificial intelligence) to improve graduate attributes associated with employability in a competitive labor market.