Redesigning University Job Fairs: An AI-Supported, Major-Specific Approach to Improving Hiring Alignment

James Mardi

Co-Presenters: Jose Marchena Marchena, Meera Patel

College: Hennings College of Science Mathematics and Technology

Major: MS.COMPUTER/SCIENCE

Faculty Research Mentor: Dunni Adenuga

Abstract:

University job fairs are designed to connect students with employers, yet many operate as generalized, one-size-fits-all events with limited industry- or role-specific opportunities. STEM and graduate students frequently encounter employers advertising broad positions misaligned with their training. This structural mismatch contributes to low hiring conversion rates (reported as low as 6.74%), high cognitive load during events, and declining student engagement with career services. Existing solutions—such as ePortfolios, AI career assessments, and virtual fairs—primarily focus on improving individual student readiness rather than redesigning the systemic structure of the fairs themselves.

This study applies journey mapping to examine student experiences before, during, and after job fairs, identifying points of information friction and structural misalignment. A comparative evaluation tests traditional generalized fairs against major-specific, role-focused formats to assess differences in relevance and hiring alignment.

We propose an AI-supported, major-specific career fair model that integrates structured pre-matching. Students complete strengths assessments and upload portfolios, while employers submit detailed role requirements. An AI system aligns candidates with relevant recruiters and schedules structured mini-interviews.

By restructuring the event rather than “fixing” the student, this approach reduces mismatch, improves clarity, and aims to increase hiring effectiveness and student engagement.