The Dual Divide: Institutional Tier and AI Skills in AI-Driven Recruitment of Engineering Freshers

Introduction/Purpose: The quick adoption of Artificial Intelligence (AI) tools in recruitment has changed entry-level hiring across all sectors. This study looks into two key factors: the institutional tier of a candidate’s degree and their possession of relevant AI skills. The research tests the following hypotheses: (H1) graduates from tier-2 and tier-3 institutions find AI-driven hiring processes more challenging than their tier-1 peers, and (H2) candidates with AI skills are more successful in passing AI-based screening rounds.
Methodology: A cross-sectional survey was conducted with engineering graduates in Maharashtra. Data were gathered on institutional tier, perceived difficulty of AI hiring processes (on a 5-point scale), possession of AI skills, and screening results. Hypothesis 1 was assessed using the Kruskal-Wallis H test. Hypothesis 2 was evaluated with a Chi-Square Test of Independence.
Results: The analysis showed a significant difference in perceived difficulty based on institutional tier (H = 12.47, p = .002), confirming H1. Further analysis found that tier-3 graduates experienced the most difficulty. Additionally, a significant link was found between having AI skills and successfully passing the screening round (χ² = 8.92, p = .003). Candidates with AI skills were 1.87 times more likely to succeed in the screening, strongly supporting H2.
Conclusion: The findings highlight a clear divide in AI-driven hiring. First, there is a structural barrier where graduates from lower-tier institutions face significant disadvantages. Second, there is a skills-based advantage, as having demonstrable AI skills greatly improves a candidate's chances of success, potentially offsetting institutional background.
Implications/Significance: This study points out the risk that algorithmic recruitment could worsen existing educational inequalities. For educators and policymakers, it emphasizes the urgent need to add AI literacy and skills training in curricula, particularly for tier-2 and tier-3 institutions. For employers, it stresses the importance of checking AI tools for bias and considering skills-based hiring to create a more diverse and fair talent pipeline.

AI-Driven Recruitment, Institutional Tier, Skills Gap, Hiring Equity, Engineering Freshers

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