Anthropic Education Report: The AI Fluency Index

People are integrating AI tools into their daily routines at a pace that would have been difficult to predict even a year ago. But adoption alone doesn't tell us much about the impact of these tools. A further, equally important question is: as AI becomes part of everyday life, are individuals developing the skills to use it well?

Previous Anthropic Education Reports have studied how university students and educators use Claude. We found that students use it to create reports and analyze lab results; educators use it to build lesson materials and automate routine work. But we know that any person who uses AI is likely to improve at what they do. We wanted to explore this further, and to understand how people using AI develop "fluency" with this technology over time.

In this report, we begin answering that question. We track the presence or absence of a taxonomy of behaviors that we take to represent AI fluency across a large sample of anonymized conversations.

Specifically, we found substantially higher rates of behaviors that fall within the broader themes of "description" and "delegation." For instance, these conversations are more likely to see users clarify their goal (+14.7pp), specify a format (+14.5pp), provide examples (+13.4pp), and iterate (+9.7pp) compared to non-artifact conversations. In other words, they're doing more to direct AI at the outset of their work.

Kristen Swanson designed the research, led the analysis, and wrote this report. Zoe Ludwig and Drew Bent contributed to framework alignment, messaging, and review. The 4D Framework for AI Fluency was developed by Rick Dakan and Joe Feller. Zack Lee provided technical support. Hanah Ho helped visualize the data. Keir Bradwell, Rebecca Hiscott, Ryan Donegan and Sarah Pollack provided communications review and guidance.

In line with our recent Economic Index , we find that the most common expression of AI fluency is augmentative —treating AI as a thought partner, rather than delegating work entirely. In fact, these conversations exhibit more than double the number of AI fluency behaviors than quick, back-and-forth chats.

But we also find that when AI produces artifacts—including apps, code, documents, or interactive tools—users are less likely to question its reasoning (-3.1 percentage points) or identify missing context (-5.2pp). This aligns with related patterns we observed in our recent study on coding skills .

These initial findings present us with a baseline that we can use to study the development of AI fluency over time.