Artificial Intelligence in Recruitment: Understanding the experiences of Human Resource Professionals and Candidates

Rathos, Chris (2025) Artificial Intelligence in Recruitment: Understanding the experiences of Human Resource Professionals and Candidates. Masters thesis, Dublin, National College of Ireland.

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Abstract

The study explores the use of Artificial Intelligence (AI) in recruitment considering the experience and perception of applying AI in recruitment with regard to the Human resource (HR) professionals and job seekers. AI applications such as resume screening algorithm, virtual assistant, and biometric assessment are aiding the hiring process to make better decisions and be more efficient. However, analytical aspects of transparency, justification, and development of human judgment in the process of recruitment take centre stage because of these innovations.

The main research issue aims to question the functionality of AI systems within the context of recruitment jobs and the creation of ethical complications to both the employers and the job candidates. Based on a pragmatic paradigm of research study, the investigation uses a mixed-methods research design, which will include two custom surveys. The two surveys provided quantitative and qualitative data respectively on the prevailing trends on AI usage and qualitative insights about the attitudes and experience of stakeholders on the same.

The results show that the HR practitioners noted greater efficiency and less bias in the early resume-screening stage but both groups see many issues with transparency, algorithmic decisions and the possibility of the loss of human touch. Specifically, the job seekers express the need of having a hybrid model which involves AI-based tools and human practitioners.

The research comes to the conclusion that to ethically and effectively use AI in the recruitment process, organisations are advised to embrace transparent practices, involve various stakeholders in designing AI systems and focus on recruitment decisions made on the basis of fairness and explainability.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sands, Stephen UNSPECIFIED
Subjects:	Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > Human Resource Management H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > Human Resource Management > Recruitment
Divisions:	School of Business (- 2025) > Master of Arts in Human Resource Management
Depositing User:	Ciara O'Brien
Date Deposited:	09 Dec 2025 13:51
Last Modified:	09 Dec 2025 13:51
URI:	https://norma.ncirl.ie/id/eprint/9020

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