Cornell University researchers are building a new way to evaluate job candidates for AI-era roles: work simulations that let applicants demonstrate their skills before an interview, rather than relying on resumes and credentials. The project, part of Cornell's AI-Ready Workforce Initiative, recently received an honorable mention from the Laude Institute along with a $100,000 seed grant.
The team is led by Rene Kizilcec, associate professor of information science at Cornell Bowers, and includes ILR School professors JR Keller and Michèle Belot. Their approach uses AI to convert real job duties into practice activities candidates can work through ahead of an interview, with trained human reviewers handling the scoring.
The problem with skills-based hiring
The initiative starts from a simple observation: organizations don't have reliable evidence of who can use AI well, learn from it, and improve over time. Credentials and job histories describe where someone has been, not what they can do right now. And relational skills that matter in AI-mediated work - building trust, communicating under pressure, repairing misunderstandings - are hard to observe at scale.
Instead of self-reported skills or one-off interviews, the Cornell team builds case-based simulations. Participants confront realistic scenarios with source materials, constraints, and deliverables, while AI helps generate the cases and organize the evidence. Trained human reviewers own the interpretation and scoring, producing a more inspectable record of how someone thinks, adapts, and delivers.
What the researchers bring
Keller, an associate professor of human resource studies at Cornell's ILR School, focuses on the practical side of talent management. His work helps translate the simulation concept into assessment frameworks organizations can use for hiring decisions, promotion pathways, or workforce-wide upskilling programs.
Belot, the Frances Perkins Professor of Industrial and Labor Relations and Professor of Economics, adds a labor economics perspective. Her joint appointment positions her to examine how the tool plays out in the labor market - how employers signal what they value, how workers respond, and how practice-based evaluation might reshape hiring and mobility.
For HR professionals, the shift matters because it reframes the hiring funnel. Candidates demonstrate role-relevant capability before the interview happens, giving both sides a clearer signal of fit. For related training on AI in talent management, see this AI Learning Path for HR Managers.
A framework for AI-ready work
The researchers define AI-readiness through three competencies:
- AI Fluency - delegating tasks to AI, describing problems clearly, discerning good outputs from bad ones, and acting diligently on AI-assisted work.
- Relational Fluency - building trust, communicating under pressure, and sustaining collaboration in an AI-mediated workplace.
- Adaptive Flexibility - picking up new skills, responding to feedback, and improving performance across repeated attempts.
Each simulation follows a six-step work pattern: plan, prepare, collaborate, verify, adapt, and deliver. That structure gives reviewers a consistent way to link behavioral evidence to specific competencies, rather than relying on a single opaque score.
Why this matters for HR now
The initiative supports multiple talent decisions: hiring and promotion, upskilling and internal mobility, and organization-wide tracking of AI-readiness over time. Because practice-based evaluation can be repeated and refined, it reveals something resumes never will - how quickly a candidate adapts when given feedback.
Keller and Belot's team was one of two Cornell groups to receive a Laude Institute honorable mention this cycle. The other, led by computer science professor Rachee Singh, is developing AI agents to train and assist surgeons in remote telesurgery.
The seed grant signals this research is moving from concept toward real pilots. For HR leaders, the work offers a concrete alternative to static credentials - a way to evaluate candidates on demonstrated capability, not just claimed experience. As the project moves from funding to implementation, it's worth tracking both for what it teaches about AI-ready talent and about evaluating talent more broadly. For more on how AI is reshaping HR practice, see AI for Human Resources.
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