AI Literacy Gap

Picture a normal week at work. You open a chat window with a large language model to help you respond to a client, summarize a report, or outline a presentation. The model responds with clean, persuasive text. You skim it, change a few phrases, and send it along. Hours later, a colleague notices that a regulation is misquoted or that a reference cannot be found anywhere outside the AI response.

Scenes like this emerge in classrooms, clinics, and public agencies. A student uses an AI assistant to generate study notes, but fails to realize that it misstated a concept before she confidently takes the exam. A manager relies on an AI summary of survey responses, and an important minority concern disappears in the aggregation. Many users have strong digital skills and extensive experience with search engines, messaging apps, and productivity tools, yet still struggle to distinguish between strong and weak AI outputs. When the interface feels familiar and the writing sounds polished, it is easy to forget that the system is generating predictions rather than retrieving facts.

The gap also appears in the opposite direction. Some people avoid generative AI entirely because they feel overwhelmed, fear making a mistake, or worry that using AI breaks an unstated rule. Colleagues describe how they save hours with AI support, while less confident users stay on the sidelines. From the outside, this looks like a choice; in practice, it often reflects unequal access to clear explanations, guided practice, and psychological safety around experimenting with AI.