Tool: Guide to Developing Staff AI Training

Higher education institutions position themselves as engines of workforce development, preparing students for careers in an evolving economy. Yet many of these same institutions have underinvested in developing their own workforce. As AI reshapes work across every sector, this gap becomes harder to sustain.

Colleges and universities will struggle to credibly prepare students for an AI-influenced labor market if their own staff lack the skills to navigate that same landscape. Most institutional AI conversations have centered on faculty and instruction. Meanwhile, staff across the institution are already experimenting with AI tools, often without guidance or guardrails. They handle sensitive student data in systems increasingly touched by AI. They serve as first points of contact for students navigating AI-affected processes. And they are essential to the operational transformation that institutional AI strategies require. A student-facing advisor using AI without training poses different challenges than a faculty member doing the same. An IR team without AI fluency cannot help leadership understand what these tools make possible. An IT department evaluating AI vendors without clear frameworks may introduce tools that create more problems than they solve. Staff

AI training is not a parallel track to faculty efforts. It is a necessary complement. This guide offers a framework for identifying what different staff roles need, so institutions can build or source training that fits their context rather than adopting a one-size-fits-all approach.