Determinants of undergraduate student's willingness to use al-based recruitment : The role of awareness, trust, and perceived fairness

The increasing use of artificial intelligence (AI) in recruitment is transforming how organizations screen and select candidates. As this shift continues, it is important to understand how future job seekers, particularly university students, respond to AI-driven hiring systems. This study examines the effects of awareness of AI, perceived fairness, and trust in AI decision-making on the willingness to use AI-based recruitment systems. The study specifically targets undergraduate students at Universiti Utara Malaysia (UUM). A quantitative approach was used, and data were collected through an online survey. The research framework was based on the Technology Acceptance Model (TAM), supported by psychological theories of trust and fairness. Partial Least Squares Structural Equat ion Modeling (PLS-SEM) was applied to analyze responses from 300 UUM undergraduate students. Findings show that awareness of AI and perceived fairness have significant positive effects on students’ willingness to use AI in recruitment. Although trust in AI was statistically significant, its effect was minimal. This study provides practical insights for employers and AI developers to improve the fairness, transparency, and communication of AI tools used in hiring processes