09 March 2026
AI Is Changing Work, Not Just Replacing It: What the Anthropic Report Means for Schools
Recent research from Anthropic analysing millions of real interactions with its AI assistant Claude AI provides a detailed picture of how artificial intelligence is actually being used in the workplace. Rather than focusing on speculation about future automation, the report examines how people are already using AI in their jobs.
The findings provide useful insight for schools, particularly when thinking about how we prepare young people for future careers.
What this means for teachers
One of the clearest findings in the report is that AI is more often used to support people’s work rather than replace them entirely. In many professions, AI tools are helping workers complete tasks such as writing, analysing information, or generating ideas, while humans remain responsible for judgement, decision-making and quality control.
For teachers, this reinforces the importance of focusing on skills that go beyond simply producing answers.
Students increasingly need opportunities to:
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evaluate information critically
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question and verify outputs produced by AI tools
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explain their reasoning
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apply knowledge in unfamiliar contexts
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combine ideas from multiple subjects
In a world where AI can quickly generate text or code, the value of education lies less in producing content and more in understanding, critiquing and improving it.
This means classrooms should increasingly prioritise:
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critical thinking
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problem solving
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creativity
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collaboration
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deep subject knowledge
These are the skills that allow people to work effectively alongside AI systems rather than be replaced by them.
What this means for students
The research also shows that AI is widely used in fields related to software development, technical writing, and data analysis. These are areas where digital tools already play a major role, and AI is now becoming part of everyday workflows
For students studying computing, this does not mean fewer opportunities. If anything, the opposite may be true.
Computing students will likely be expected to:
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understand how AI systems work
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integrate AI tools into software development workflows
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evaluate AI-generated code or solutions
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design systems that incorporate AI technologies
Professional programmers are already using tools such as AI coding assistants to generate code, debug programs and explore alternative approaches. However, these tools still require strong foundational knowledge. Developers must understand algorithms, data structures, and software design in order to recognise when AI output is incorrect or inefficient.
For students considering careers in technology, this means that core computing knowledge remains essential, but it will increasingly be combined with the ability to use and manage AI tools effectively.
Why students need to focus on digital and AI literacy
Perhaps the most important message from the report is that AI is spreading quickly across many professions, not just technology roles. Jobs involving writing, analysis, communication and digital information are already seeing widespread use of AI tools.
As a result, digital literacy is no longer just about knowing how to use a computer. Increasingly, it involves understanding:
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how AI systems generate responses
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the limitations and biases of AI tools
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how to verify information produced by AI
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when it is appropriate to use AI support
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how to combine human judgement with automated tools
This broader set of skills is often referred to as AI literacy.
Students who develop strong AI literacy will be better equipped to:
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use AI tools productively
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avoid common mistakes or over-reliance on automation
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critically assess the outputs of digital systems
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adapt to new technologies as they emerge
In many ways, these skills mirror traditional goals of education: questioning information, understanding evidence, and applying knowledge thoughtfully.
Theoretical capability and observed exposure by occupational category Share of job tasks that LLMs could theoretically perform (blue area) and our own job coverage measure derived from usage data (red area).