A study of candidate experience in AI driven recruitment

Ajay Kuikel

The increasing use of artificial intelligence (AI) in recruitment has changed hiring practices, mainly in early stages such as candidate screening, communication and assessment. AI-driven recruitment has been mainly praised for enhancing efficiency, consistency, and scalability. There is limited research on how candidates, particularly structurally vulnerable groups such as international students, experience these technologies. This study explores the lived experiences of international students interacting with AI-driven recruitment tools, mainly focus on the perceptions of fairness, transparency, trust and overall candidate experience with AI driven recruitment tools. Guided by the interpretivist paradigm, this study adopted a qualitative phenomenological approach to capture subjective experience on AI-based hiring processes. Semi-structured interviews were conducted with thirteen international students from Excelsia University College who had engaged with an AI-driven recruitment system. Thematic analysis was used to identify key patterns and insights emerging from participants’ narratives. The findings show that participants often perceived AI-driven recruitment as efficient, convenient, and helpful in streamlining the recruitment process. Candidate satisfaction was shaped mainly by fairness, transparency and quality communication. The key elements of candidate experience included system usability, technical reliability, personalisation, feedback quality and opportunities for human interaction. Lack of transparency and limited explanatory feedback led to decreased trust and raised concerns about algorithmic fairness, particularly among international students navigating unfamiliar labour market contexts. The participants suggested improvements, including enhanced transparency, more meaningful feedback, better personalisation and integration of human support along with automated processes. The study contributes to theory by extending Expectation Confirmation Theory and Equity Theory into AI-driven recruitment contexts, highlighting the socio-technical nature of candidate experience. In practice, it provides recommendations for organisations to design more transparent, inclusive, and candidate focus AI driven recruitment systems.

A study of candidate experience in AI driven recruitment

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