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[Anthropic 教育报告：教育工作者如何使用 Claude]

可以理解，关于人工智能在教育中的讨论大多集中在学生如何利用大型语言模型帮助学习和写作上。但教育工作者也在使用 AI。根据最近一项盖洛普调查，教师表示 AI 工具每周平均为他们节省了 5.9 小时的时间。与通常的讨论相反，学生们开始对教授在课堂上使用 AI 表达担忧。

我们此前报道了学生如何使用 AI 的数据[链接]。本次新分析聚焦教授：我们分析了 2025 年 5 月和 6 月期间，全球高等教育专业人士在 Claude.ai 上约 74,000 条匿名对话。我们还与东北大学合作，直接听取教师们在校内使用 AI 的情况。研究结果为大学环境中教育者采用 AI 提供了实证快照。

主要发现：

• 教育者在课堂内外均使用 AI，应用范围涵盖课程材料开发、撰写资助申请、学术指导及管理招生、财务规划等行政任务。
• 教育者不仅使用聊天机器人，还利用 AI 构建定制工具。教师们使用 Claude Artifacts 创建互动教学材料，如化学模拟、自动评分标准和数据可视化仪表盘。
• 教育者倾向于自动化繁琐事务，同时保持对其他任务的参与。需要大量背景知识、创造力或直接学生互动的任务（如设计课程、指导学生、撰写资助申请）多采用 AI 辅助方式；而常规行政工作（如财务管理、记录保存）则更多依赖自动化。
• 部分教育者自动化评分，另一些则坚决反对。Claude.ai 数据显示，评分和评估的 AI 使用频率低于其他用途，但其中 48.9% 属于自动化重度使用（AI 直接执行任务）。尽管教育者对自动化评估持谨慎态度，且调查中评分被认为是 AI 效果最差的领域。

教育者使用 Claude 的识别方法：

我们使用自动分析工具 Clio[链接]，在保护用户隐私的前提下揭示 Claude 使用的广泛模式。鉴于平台未收集职业自报信息，且教育者的 AI 互动涵盖教学、研究、行政及个人学习，识别和分类较为复杂。我们筛选了与高等教育邮箱关联的 Claude.ai 免费和专业账户对话，并自动过滤出与教育者任务相关的对话（如制定教学大纲、评分、开发课程材料），共约 74,000 条。分析聚焦教育者专业任务的 AI 使用，非教育者所有 AI 互动的全面视角。

我们还将对话匹配至美国劳工部 O*NET 职业信息数据库中“高等教育”教学或行政相关任务。结合东北大学 22 位 AI 早期采用教师的调查和质性研究，揭示教育者的动机、顾虑和使用模式。

教育者常见 AI 使用场景：

• 课程开发（57%）
• 学术研究（13%）
• 学生表现评估（7%）

东北大学教师调查显示，另一个常见用途是教师自身学习（占 AI 使用时间约 29%），但此项未纳入 Claude.ai 数据分析。

其他有趣用途包括：

• 创建法律模拟案例
• 开发职业教育和劳动力培训内容
• 撰写推荐信
• 制定会议议程及行政文档

教育者使用 AI 的原因：

1. 自动化繁琐任务（“处理繁琐事务”，“帮助完成筹款中的机械部分”）
2. 协作思考伙伴（“AI 能找到我未曾想到的有效解释方式”）
3. 为学生提供个性化学习体验（“AI 可提供超越单一教师能力的个性化互动学习”）

教育者如何用 AI 构建定制工具：

教育者利用 Claude Artifacts 创建互动教学材料，部分可直接应用于课堂。一位东北大学教师表示：“以前耗时过高的定制模拟、插图、互动实验现在成为可能，极大提升学生参与度。”

主要作品包括：

• 互动教育游戏（逃脱室、平台游戏、模拟）
• 评估工具（带自动反馈的 HTML 测验、CSV 数据处理、评分标准）
• 数据可视化（历史时间线、科学概念）
• 专业学科学习工具（化学计量学游戏、遗传学测验、计算物理模型）
• 学术日历与排程工具（可自动填充、导出图片或 PDF）
• 预算规划与分析工具（教育机构预算管理）
• 学术文档（会议记录、成绩沟通邮件、推荐信、资助申请等）

这标志着 AI 从对话助手向创造性合作者的转变，帮助教育者制作个性化教学资源，降低技术门槛。

增强与自动化的平衡：

分析显示，教育者在 AI 增强（协作使用）与自动化（完全委托 AI）之间存在细微差别。

增强倾向较高的任务：

• 大学教学与课堂指导（77.4% 增强）
• 资助申请撰写（70.0% 增强）
• 学术指导与学生组织辅导（67.5% 增强）
• 学生学术监督（66.9% 增强）

自动化倾向较高的任务：

• 教育机构财务与筹款管理（65.0% 自动化）
• 学生成绩记录与评估（48.9% 自动化）
• 招生与注册管理（44.7% 自动化）

教育者更倾向于将常规行政和财务任务完全交给 AI，而涉及学生直接互动和创造性决策的任务则偏向 AI 辅助。正如一位教授所言，设计课程时“AI 需要指导材料难度和已有内容的背景”。

评分自动化仍具争议。一位东北大学教授表示：“伦理和实际层面，我对用 AI 评估学生非常谨慎。AI 评分准确性不足，学生付费的是我的时间，我有道德义务做好评分工作（可借助 AI）。”大多数教育者认为评分不应完全自动化。

教育者如何重新思考教学内容：

AI 改变了学生学习方式，促使教师调整教学方法。一位教授说：“AI 迫使我彻底改变教学，努力应对认知卸载问题。”

例如，AI 编程工具革新了分析教学，教师能更多讨论分析应用概念，而非调试语法错误。

评估方式也在变化。尽管担忧学生作弊，一些教师重新设计作业，避免 AI 完成。例如，一位教授不再布置传统研究论文，转而设计 AI 难以完成的任务。

未来可能要求学生利用 AI 解决更复杂的现实问题，但这对教师提出更高要求，且学生仍需独立掌握基础技能以评估 AI 产出。

研究局限与注意事项：

• 识别方法限制：筛选仅捕获约 1.5% 高教邮箱对话，可能遗漏许多非专属教育者任务的 AI 互动。
• 教育者范围有限：仅分析高等教育邮箱账户，未涵盖 K-12 教师。
• 早期采用者偏差：样本偏向已接受 AI 的教育者，可能不代表整体态度。
• 调查样本有限：东北大学数据仅代表单一机构。
• 平台特异性：分析聚焦 Claude.ai，可能不适用于其他 AI 平台。
• 时间限制：仅涵盖 2025 年 5-6 月，未反映学年内使用变化。

展望未来：

研究揭示教育者 AI 采用的复杂图景，应用涵盖互动模拟、行政管理等多方面。教育者将 AI 从对话工具转变为创造性伙伴，有望缓解教育资源限制，提升学生体验。

然而，AI 辅助评分仍存争议，效率与教育质量、伦理之间的平衡仍需探索。未来，教育者对 AI 合理使用的看法可能随技术进步和最佳实践演变。

理解学生与教育者 AI 使用的互动也至关重要：学生如何看待教授使用 AI？教育者采用如何影响学生学习行为？

本研究捕捉了教育者积极试验 AI 的阶段，未来需持续对话、政策制定和研究，确保 AI 工具提升而非削弱教育体验。

引用格式（Bibtex）：

@online{benthand2025education, author = {Drew Bent and Kunal Handa and Esin Durmus and Alex Tamkin and Miles McCain and Stuart Ritchie and Ryan Donegan and Jennifer Martinez and Jason Jones}, title = {Anthropic Education Report: How Educators Use Claude}, date = {2025-08-26}, year = {2025}, url = {https://www.anthropic.com/news/anthropic-education-report-how-educators-use-claude}, }

鸣谢：

Drew Bent* 和 Kunal Handa* 设计并执行实验，撰写报告。

Esin Durmus、Alex Tamkin、Miles McCain、Stuart Ritchie、Ryan Donegan 和 Jason Jones 提供宝贵反馈。

注释：

1. 对话数据采集于 2025 年 5 月 22 日至 6 月 2 日的 11 天内。
2. 过滤条件：“该对话是否可能为教育者（教师、教授或讲师）寻求教学内容、评分、研究或行政帮助？排除学生完成作业、论文等。若不确定，保守判断非教育者。”

*未能确定增强/自动化类别的对话未计入相关图表。更多信息见 Anthropic 经济指数研究[链接]。

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.

We find that:

Identifying educators’ use of Claude

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

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.

We complemented our analysis with survey data and qualitative research from 22 Northeastern University faculty members who are early adopters of AI to shed light on educators' motivations, concerns, and usage patterns.

Common uses among educators

The most prominent use of AI, as revealed by both our Claude.ai analysis and our qualitative research with Northeastern, was for curriculum development. Our Claude.ai analysis also surfaced academic research and assessing student performance as the second and third most common uses.

In our surveys, Northeastern faculty reported that another common case was using AI for their own learning (29% of their AI time on average). However, this was not studied in our Claude.ai analysis, given the filtering mechanism and the difficulty of distinguishing between student and educator usage in these learning instances.

Some other particularly interesting uses we discovered in the Claude.ai data include:

• Create mock legal scenarios for educational simulations;
• Develop vocational education and workforce training content;
• Draft recommendation letters for academic or professional applications;
• Create meeting agendas and related administrative documents.

Our qualitative research with Northeastern faculty hints at why educators often gravitate towards these common AI uses:

1. Automation of a tedious task (“It takes care of the tedious tasks”; helps with “rote portions of fundraising”);
2. Collaborative thought partner (“AI can find effective ways to explain concepts to students that I had not thought of myself”);
3. Personalized learning experiences for students (“AI is useful for giving students and me individualized, interactive learning experiences beyond what one instructor could provide”).

How educators are building custom tools with AI

One of the most inspiring findings is how educators use Claude's Artifacts feature to create interactive educational materials. Rather than just having conversations, educators are often building complete, functional resources that in some cases they can immediately deploy in their classrooms.

As one surveyed Northeastern faculty member put it: “What was prohibitively expensive (time) to do [before] now becomes possible. Custom simulation, illustration, interactive experiment. Wow. Much more engaging for students.”

Key creations built by educators

This goes beyond just Claude. One professor described how new AI tools in general enable them to “translate [their] own content into more accessible / engaging forms (interactive pages, simulation, podcast, video).”

These creations represent a shift from AI as conversational assistant to AI as creative collaborator, enabling educators to produce personalized educational materials that might traditionally require significant technical expertise or resources.

The augmentation-automation spectrum

Our analysis reveals a nuanced picture of how educators balance AI augmentation (collaborative use) versus automation (delegating tasks entirely), building upon Anthropic’s prior work on the Economic Index.

Key patterns emerge across different educational tasks in the Claude.ai data:

Tasks with higher augmentation tendencies:

• University teaching and classroom instruction, which includes creating educational materials and practice problems (77.4% augmentation);
• Writing grant proposals to secure external research funding (70.0% augmentation);
• Academic advising and student organization mentorship (67.5% augmentation);
• Supervising student academic work (66.9% augmentation).

Tasks with relatively higher automation tendencies:

• Managing educational institution finances and fundraising (65.0% automation);
• Maintaining student records and evaluating academic performance (48.9% automation);
• Managing academic admissions and enrollment (44.7% automation).

This variation demonstrates that educators’ likelihood to delegate entirely to the AI depends on the task. Aligned with our survey’s results, we see that tasks involving routine administrative and financial management are more likely to be fully delegated than tasks close to direct student interaction (such as creating practice materials or advising on doctoral-level academic research). These AI interactions often require significant context and thus collaboration between AI and professor. For example, as one Northeastern professor put it, when designing lesson plans, “AI needs guidance on the level of material and context with regard to what we have already covered.”

Educators also seem more likely to use AI in an augmentative manner for work requiring creativity or complex decision-making, such as writing grant proposals. When brainstorming, one surveyed professor wrote,

“It's the conversation with the LLM that's valuable, not the first response. This is also what I try to teach students. Use it as a thought partner, not a thought substitute.”

That said, 48.9% of grading-related conversations being identified as automation-heavy remains concerning. Although surveyed professors thought this was the single task that AI was least effective at, it was seen in the Claude.ai data. And even if this represents only 7% of the Claude.ai conversations we studied, it emerged as the second most automation-heavy task. This includes sub-tasks like providing feedback on student assignments and grading their work using rubrics. While it’s not clear to what degree these AI-generated responses factor into the final grades and feedback, the interactions surfaced by our research do show some amount of delegation to Claude.

Using AI in grading remains a contentious issue among educators. One Northeastern professor shared: “Ethically and practically, I am very wary of using [AI tools] to assess or advise students in any way. Part of that is the accuracy issue. I have tried some experiments where I had an LLM grade papers, and they're simply not good enough for me. And ethically, students are not paying tuition for the LLM’s time, they're paying for my time. It's my moral obligation to do a good job (with the assistance, perhaps, of LLMs).”

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.

How educators are rethinking what to teach

Many educators recognize that AI tools are changing the way students learn. That in turn puts pressure on educators to change the way they’re teaching. As one surveyed professor put it:

“AI is forcing me to totally change how I teach. I am expending a lot of effort trying to figure out how to deal with the cognitive offloading issue.”

It’s also changing what professors are teaching. In coding, for example, according to one professor, “AI-based coding has completely revolutionized the analytics teaching/learning experience. Instead of debugging commas and semicolons, we can spend our time talking about the concepts around the application of analytics in business.”

More broadly, the ability to evaluate AI-generated content for accuracy is becoming increasingly important. “The challenge is [that] with the amount of AI generation increasing, it becomes increasingly overwhelming for humans to validate and stay on top,” one professor wrote. Professors are keen to help their students build enough expertise in a subject area to have this discernment.

Assessments also are starting to look different. While student cheating and cognitive offloading remain a concern, some educators are rethinking their assessments.

“If Claude or a similar AI tool can complete an assignment, I don’t worry about students cheating; I [am] concerned that we are not doing our job as educator[s].”

In one particular Northeastern professor’s case, they shared that they “will never again assign a traditional research paper” after struggling with too many students submitting AI-written assignments. Instead, they shared: “I will redesign the assignment so it can't be done with AI next time. I had one student complain that the weekly homework was hard to do and they were annoyed because Claude and ChatGPT was useless in completing the work. I told them that was a compliment, and I will endeavor to hear that more from students.”

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.

Limitations and considerations

This research comes with important caveats:

• Identification methodology: Our filtering, which looked at Claude conversations to infer which were associated with educators, captured only ~1.5% of conversations from higher education emails, limiting us to tasks explicitly linked to educators (e.g. creating syllabi) and likely missing many other educator AI interactions that aren’t exclusively linked to educators (e.g. getting help explaining a difficult concept);
• Limited educator scope: Analysis restricted to accounts with higher education email addresses, excluding K-12 teachers;
• Early adopter bias: We're likely capturing educators already comfortable with AI who may not represent the broader educator population's technological readiness or attitudes;
• Survey limitations: Northeastern University faculty data provides qualitative context but represents a limited sample from a single institution that may not generalize;
• Platform specificity: This analysis focuses on Claude.ai usage and may not reflect patterns on other AI platforms;
• Temporal constraints: The analysis window of May and June does not capture seasonal variations in educator AI usage throughout the academic year.

Looking ahead

Our findings reveal a complex picture of educator AI adoption. The diversity of applications—from building interactive simulations to managing administrative tasks—shows AI's expanding presence across academic functions.

Perhaps most encouraging is how educators are using AI to build tangible educational resources. This shift from AI as a conversational tool to AI as a creative partner could help address longstanding resource constraints in education. As one professor noted, custom simulations and interactive experiments that were once “prohibitively expensive” in terms of time are now possible, creating more engaging experiences for students.

However, there remains tension around AI-assisted grading. Whereas nearly half of grading-related tasks showed automation patterns in our data, surveyed faculty rated this as AI's least effective application. This disconnect—between what's being attempted and what's viewed as appropriate—highlights the ongoing struggle to balance efficiency gains with educational quality and ethical considerations.

These findings suggest that narratives around AI in education will continue evolving alongside the technology itself. Educator views on appropriate AI use, particularly for sensitive tasks like grading, may shift as tools improve and best practices emerge. Equally important for future research is understanding how student and educator AI usage interact—how do students perceive and respond when they know their professors are using AI? How does educator adoption influence student learning behaviors?

Our research captures educators in a moment of active experimentation, building new possibilities while grappling with fundamental questions about their role in an AI-augmented classroom. The path forward will require ongoing dialogue, careful policy development, and continued research to ensure these tools enhance rather than compromise the educational experience.

If you'd like to cite this post, you can use the following Bibtex key:

@online{benthand2025education,
 author = {Drew Bent and Kunal Handa and Esin Durmus and Alex Tamkin and Miles McCain and Stuart Ritchie and Ryan Donegan and Jennifer Martinez and Jason Jones]},
 title = {Anthropic Education Report: How Educators Use Claude},
 date = {2025-08-26},
 year = {2025},
 url = {https://www.anthropic.com/news/anthropic-education-report-how-educators-use-claude},
 }

Drew Bent* and Kunal Handa* designed and executed the experiments and wrote the blog post.

Esin Durmus, Alex Tamkin, Miles McCain, Stuart Ritchie, Ryan Donegan, and Jason Jones provided valuable feedback and discussion.

1 The conversations took place during an 11-day period from May 22 to June 2, 2025.

2 Specifically, we used the following filter, powered by Claude, to identify educator-relevant conversations: “Is this conversation likely to be with an educator (teacher, professor, or instructor) seeking help with instructional content, grading, research, or administrative duties? Make sure to not include students doing their own coursework, research papers, etc. Err on the side of conservatism and assume it's not an educator if you're not sure.”

*In cases where the augmentation/automation category could not be identified, we excluded those from the chart. For more information on these categories, please see our Anthropic Economic Index research.

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