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.
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.
Measuring AI fluency
To quantify AI fluency, we use the 4D AI Fluency Framework, developed by Professors Rick Dakan and Joseph Feller in collaboration with Anthropic. This framework helps us define 24 specific behaviors that we take to exemplify safe and effective human-AI collaboration.
Of these 24 behaviors, 11 (listed in the graph below) are directly observable when humans interact with Claude on Claude.ai or Claude Code. The other 13 (including things like being honest about AI’s role in work, or considering the consequences of sharing AI-generated output), happen outside Claude.ai’s chat interface, so they’re much harder for us to track. These unobservable behaviors are arguably some of the most consequential dimensions of AI fluency, so in future work we plan to use qualitative methods to assess them.
For this study, we focused on the 11 directly observable behaviors. We used our privacy-preserving analysis tool to study 9,830 conversations that included several back-and-forths with Claude on Claude.ai during a 7-day window in January 2026.1 We then measured the presence or absence of the 11 behaviors; each conversation could display evidence of multiple behaviors. We assessed the reliability of our sample by checking whether our results were consistent across each day of the week, and across the different languages in our sample (we found that they were).2 This, finally, gave us the AI Fluency Index: a baseline measurement of how people collaborate with AI today, and a foundation for tracking how those behaviors evolve over time as models change.
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