Most people encounter generative artificial intelligence through an application. They upload a document, ask a question, and receive an answer without having to think much about where the model is running or who controls the infrastructure behind it. But for a university, those questions matter. They shape what happens to institutional data and which models students, staff, faculty, administrators, and researchers can use. They also determine how much control the institution keeps over the systems it depends on. 

Artificial intelligence is changing how universities search, summarize, analyze, and generate information. AI is powerful, which is perhaps why less experienced users are sometimes surprised by this limitation:

his piece tackles a problem that’s becoming more relevant as AI tools spread into everyday work: how do you check the quality of something an AI produced, when there’s no answer key to grade against?