Beyond the Resume: Identifying AI-Ready Candidates Before the Interview

University recruiters are drowning in applications, and AI slop resumes have made the signal-to-noise problem significantly worse. Every candidate claims similar skills, lists similar projects, and uses the same language. The cost of a bad early-stage decision: wasted interview slots and new-grad hires who struggle to contribute in an AI-ready world where the baseline expectation has shifted dramatically.
This panel will discuss frameworks for predicting new-grad success in an AI-integrated workplace. Drawing on a peer-reviewed study of dozens of tech hiring managers, we'll walk through the 7 traits they most consistently associate with high-performing new graduates, and what separates AI-ready ""company hires"" (someone talented, coachable, and positioned to grow) from a candidate who will plateau quickly.
Panelists will share how companies have retrained their screening process to surface these traits before a single interview is scheduled.
Attendees will leave with specific, practical tools:
Which resume signals reliably indicate genuine project experience versus AI-assisted padding.
Screening call questions that reveal coachability and problem-solving capabilities.
How to spot the 7 hiring manager traits in a resume/conversation, and how project-based work, open source contributions, and event participation can serve as stronger proxies for potential than GPA or coursework alone.