Anthropic Education Report: How educators use Claude

Understandably, much of the conversation of AI in education focuses on how students are using large language models to help them study and write. But educators use AI too. In a recent Gallup survey, teachers reported that AI tools saved them an average of 5.9 hours per week. And in an inversion of the usual discussion, students have begun expressing concerns about professors using AI in the classroom.

We previously reported data on how students were using AI. Our new analysis looks at professors: we analyzed ~74,000 anonymized conversations from higher education professionals across the world on Claude.ai this past May and June. 1 We also partnered with Northeastern University to hear directly from faculty how they were using AI within the university. Our findings provide an empirical snapshot of educator AI adoption, specifically in university settings.

In this research, we used our automated analysis research tool that reveals broad patterns of Claude usage while protecting users’ privacy.

One path forward may be to uplevel assignments based on these newfound tools and expect students to tackle more complex, real-world challenges that remain difficult even with AI assistance. However, this is a moving target given AI’s continual improvements and may put a significant burden on the educators themselves. Additionally, students still need to develop foundational skills independently of AI to effectively evaluate its outputs.

While there are ways AI feedback can support a student’s development, such as through automatic systems providing formative feedback (e.g. those being built by educators in Claude Artifacts), most educators seem to agree that grading shouldn’t be anywhere close to fully automated.

Studying higher education professionals’ use of Claude.ai presents unique challenges, as we don’t currently collect self-reported occupational data on our platform. Unlike students who often explicitly mention coursework or assignments, educators’ AI interactions span teaching, research, administration, and personal learning, making them harder to identify and categorize.

Using our privacy-preserving tool, we analyzed conversations from Claude.ai Free and Pro accounts associated with higher education email addresses and then automatically filtered conversations for educator-specific tasks—such as creating syllabi, grading assignments, or developing course materials. 2 This filtering yielded approximately 74,000 conversations from a period in May and June. Our analysis should be viewed as an exploration of how educators use AI for profession-specific tasks, not a comprehensive view of all educator AI usage.

We also matched each conversation to the most appropriate task from the comprehensive list of educator tasks in the O*NET database of occupational information from the U.S. Department of Labor. We identified educator tasks as tasks associated with “Postsecondary” teaching or administrative occupations.