Oct 27 2025
Artificial Intelligence

Why Computer Science is the Foundation of AI Literacy

Supporting students and teachers in computer science builds early technology skills in K–12 schools.

When I left the classroom 12 years ago, computer science was still treated like a niche pursuit, something for the few. Today, it’s the engine of change across every sector. In K–12 education, it is tied to a growing understanding that computing is a creative, human-centered discipline that empowers people to solve real problems in their communities. With artificial intelligence-supported development, more students can move from idea to prototype faster than ever.

Computer science education isn’t just about using computers; it’s also about understanding how they work and the problems they can solve. The Computer Science Teachers Association’s (CSTA’s) K–12 Computer Science Standards focus on the knowledge that lets students shift from being consumers to creators who can decompose problems, design and test algorithms, model systems and evaluate trade-offs. That foundation is precisely what AI literacy requires.

If we reduce AI literacy to only teaching students how to prompt a chatbot, we’ll repeat a mistake from 20 years ago, when many schools rebranded computer science as learning to use word processors. We spent the next two decades rebuilding true computer science programs. Now, we have a chance to learn from the past. Students deserve to understand the data, models and limitations that underpin AI systems — and to build their own computational artifacts that leverage AI responsibly to solve problems they care about.

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The Importance of a Well-Supported Computer Science Teacher

When schools ask me what students need to learn computer science, my answer starts with people. The single most important resource is a well-prepared, well-supported computer science teacher. Devices, connectivity and software matter — students need reliable access to computers and high-quality internet at school and at home — but great teaching is what transforms access into learning.

AI will play a growing role in teaching and learning, but we should be cautious about locking into specific products before the evidence is clear. Invest first in teacher learning communities, professional development and curriculum coherence. Districts that cultivate a strong community of computer science teachers will be ready for any new software or curriculum they want to add.

A Practical Roadmap for K–12 Districts

To help districts, teachers and curriculum leaders take the next step, CSTA convened experts across education and industry to publish “AI Learning Priorities for All K–12 Students.”* It’s a concise set of learning priorities that every student should experience. We boil it down to four connected outcomes:

  1. Understand how AI works and where it fits. Students should grasp core ideas — data, representation, basic model behavior — and recognize appropriate contexts for AI use.
  2. Use and critically evaluate AI systems. That includes benefits, limitations and societal impacts, with an explicit focus on ethics, bias, privacy and accountability.
  3. Create with AI, responsibly. Students shouldn’t just consume outputs; they should build projects that incorporate AI components while practicing safe, transparent and testable design.
  4. Develop the habits of innovation and persistence. Problem-framing, iteration, debugging and reflection remain the heart of computing and of effective AI use.

These priorities are intentionally technology-agnostic. They will be incorporated into the revised 2026 CSTA K–12 Computer Science Standards to give schools a roadmap that will remain relevant even as AI tools change.

CSTA exists to make this work possible in real classrooms. We support the world’s largest community of computer science teachers with professional learning, implementation guidance, standards alignment and local chapters to provide peer support. The CSTA K–12 Computer Science Standards give districts a practical, shared language for curriculum selection and program design, and our professional development and resources help teachers translate that guidance into daily practice. 

There is no AI literacy without computer science education. If we want students to shape a world transformed by AI, we must invest in the people who teach them, the programs that welcome them and the standards that focus on foundational learning outcomes to prepare students for the long term.

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