AI Fluency Index

Anthropic Education Report: The AI Fluency Index \ Anthropic Announcements Anthropic Education Report: The AI Fluency Index Feb 23, 2026

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. Prevalence of each AI fluency behavioral indicator across 9,830 Claude.ai conversations, ranked from most to least common and color-coded by competency. Results With our first study, we’ve found two main patterns in Claude use: a strong relationship between AI fluency and iteration and refinement through longer conversations with Claude, and changes in users’ fluency behaviors when coding or building other outputs. Fluency is strongly associated with conversations that exhibit iteration and refinement One of the strongest patterns in the data is the relationship between iteration and refinement and every other AI fluency behavior. 85.7% of the conversations in our sample exhibited iteration and refinement: building on previous exchanges to refine the user’s work, rather than accepting the first response and moving to a new task. These conversations showed substantially higher rates of other fluency behaviors, as the chart below shows: Behavioral indicator prevalence in conversations where the user iterates and refines (n=8,424) versus conversations without iteration and refinement (n=1,406). All behaviors are substantially more prevalent in conversations with iteration and refinement. On average, conversations with iteration and refinement exhibit 2.67 additional fluency behaviors—roughly double the non-iterative rate of 1.33. This is especially pronounced for fluency behaviors related to evaluating Claude’s outputs. Conversations with iteration and refinement are 5.6x more likely to involve users questioning Claude’s reasoning, and 4x more likely to see them identify missing context.

When creating outputs, users become more directive but less evaluative 12.3% of conversations in our sample involved artifacts , including code, documents, interactive tools, and other outputs. In these conversations, people collaborated with AI quite differently.

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.

But this directiveness doesn’t correspond with greater levels of evaluation or discernment. In fact, it’s the opposite: in conversations where artifacts are created, users are less likely to identify missing context (-5.2pp), check facts (-3.7pp), or question the model’s reasoning by asking it to explain its rationale (-3.1pp). Our Economic Index finds that—unsurprisingly—the most complex tasks are where Claude struggles the most, so this seems particularly noteworthy. Behavioral indicator prevalence in conversations with artifacts (n=1,209) versus without artifacts (n=8,621). Description and delegation behaviors increase in artifact conversations, while all three discernment behaviors decrease. There are several possible explanations for this pattern. It might be…