GRIND/OPTIMIZING THE CANDIDATE EXPERIENCE: A STUDY OF AI GENERATED INTERVIEW QUESTIONS AND THEIR EFFECTIVENESS IN TECHNICAL HIRING USING CUSTOMIZED WEB APPLICATION

Abstract

The rapid development of artificial intelligence (AI) has transformed much of the hiring process, particularly technical hiring. In this paper, we present GRIND, a customized web-based tool that seeks to enhance the candidate experience by leveraging AI-generated interview questions. This paper explores the use of AI-driven question generation to enhance the efficiency as well as equity of technical interviews while also attempting to nullify interviewer bias and enhance candidate engagement. GRIND uses advanced natural language processing (NLP) to generate customized technical questions autonomously from job descriptions and applicant profiles. It provides an intuitive experience for recruiters to construct, view, and use these questions, with the option for applicants to respond to them in an open setting. Using quantitative and qualitative measures, the research investigates the impact of AI-driven questions on candidate performance, satisfaction levels, and fairness perceptions against traditional interview practices. The key findings indicate that AI-based questions are capable of maintaining or even improving the technical standard of interviews, while ensuring increased consistency and responsiveness. Applicants reported an interactive and less stressful encounter, which they credited to the individualized and unbiased nature of the questions asked. Recruitment managers benefited from reduced preparation time and increased confidence in the objectivity of the assessment process. This dissertation contributes to the growing body of work on artificial intelligence in human resource environments by presenting the concrete benefits and challenges of using AI-generated content in actual recruitment processes. The GRIND application is a pilot case for the recruitment technologies of the future, illustrating the degree to which AI is not only able to augment technical recruitment processes but also to introduce a more diverse and candidate-centric interview experience.