Over the past year, I’ve heard a growing narrative in education: “If AI can write code, why should students still learn computer science?”

At first glance, it seems like a reasonable question. Generative AI can produce functioning code in seconds. Agentic AI is beginning to write software, debug applications, automate workflows, and solve increasingly complex problems with minimal human intervention.

If machines can code, haven’t we reached the point where coding no longer matters?

The answer is no.

In fact, current research points to the opposite conclusion. Artificial intelligence isn’t reducing the need for computer science education; it is fundamentally changing what computer science education should become. AI changes what should be taught, not whether it should be taught. (Finnie-Ansley et al., 2025).

We’ve Been Asking the Wrong Question

For years, computer science education has often been viewed through the lens of programming. Students learned syntax. They memorized commands. They wrote programs. They debugged errors. Programming became the visible outcome of learning computer science. But programming was never the ultimate goal. The goal has always been computational thinking.

Computational thinking is the ability to analyze problems, recognize patterns, break complex challenges into manageable parts, design logical solutions, test assumptions, critically evaluate outcomes, and continually improve systems (Lee et al., 2026). Coding was simply one way to develop those habits of mind. AI hasn’t eliminated computational thinking. It has made it even more valuable.

AI Changes What Humans Need to Contribute

When AI can generate code almost instantly, human value shifts. Students no longer gain a competitive advantage simply by knowing how to write syntax from memory. Instead, they must learn to ask better questions. They must understand why an AI-generated solution works, or why it doesn’t. They must recognize faulty logic, identify security vulnerabilities, detect bias, understand data quality, and determine whether an AI recommendation is actually appropriate for the problem at hand.

These are not coding skills. These are computer science skills.

The Future Computer Scientist Looks Different

Tomorrow’s computer science graduates won’t spend all of their time typing code. Instead, they’ll increasingly act as architects, designers, evaluators, and system thinkers.

They will:

• Design intelligent systems that collaborate with humans.
• Evaluate and improve AI-generated solutions.
• Verify outputs instead of blindly accepting them.
• Build secure and ethical technologies.
• Understand data, algorithms, privacy, and cybersecurity.
• Integrate multiple technologies into complex real-world systems.
• Exercise human judgment when AI reaches its limits.
  
  

Ironically, AI makes these deeper competencies more important, not less important.

CS: The New Foundational Literacy

One of the biggest misconceptions is that computer science exists only for students who want careers in software development. This has never been true. Computational thinking has become one of the most transferable skill sets across every profession.

• Healthcare
• Manufacturing
• Agriculture
• Business
• Finance
• Education
• Public service

Every industry is becoming increasingly dependent on intelligent systems and automation. Understanding how those systems work and how to question them is a new foundational literacy.

The Next Digital Divide

For years, educators worried about access to devices and broadband, but today’s challenge is different. The next digital divide is defined by AI literacy. I just read an interesting article published in The Hill by John Mac Ghlionn, “AI is creating America’s next underclass”. The next social divide won’t be between people who have money and people who don’t. It will be between people who know how to leverage AI and people who don’t.

Some students will learn how AI works, how to evaluate it, and how to use it responsibly. Others will simply consume whatever AI produces. This gap matters.

Students who understand AI will shape the future, whereas those who merely use AI will inherit the decisions made by others.

Schools Must Redesign, Not Replace

This is why schools should resist the temptation to reduce computer science because AI can generate code. Instead, we should redesign computer science around the competencies AI cannot replace.

Students need opportunities to:

• Solve authentic, real-world messy problems.
• Build and improve AI-assisted solutions.
• Evaluate ethical implications.
• Collaborate with diverse teams.
• Design systems that serve people.
• Develop the judgment to know when AI output should and should not be trusted.
  

These experiences prepare learners not simply to use technology, but to lead in a world shaped by it.

The Human Advantage

The greatest misconception surrounding AI is that it replaces human thinking. In reality, AI can be designed to place more value on human thinking.

• Routine, busy work becomes automated, leaving room for more complex tasks and problem solving.
• Judgment becomes indispensable.
• Creativity becomes differentiating.
• Ethics becomes essential.
• Systems thinking becomes a competitive advantage.

That is exactly what high-quality computer science education develops.

Looking Ahead

Artificial intelligence isn’t signaling the end of computer science education. It’s signaling the beginning of its next evolution. The future belongs to learners who understand technology deeply enough to question it, improve it, and design it responsibly.

Computer science is no longer just about teaching students how to code. It is about teaching them how to think. And in an AI-powered world, that may be the most important education we can provide.

References

Organization for Economic Co-operation and Development. (2025). What should teachers teach and students learn in a future of powerful AI? OECD Publishing. https://doi.org/10.1787/ca56c7d6-en

Organization for Economic Co-operation and Development. (2026). OECD digital education outlook 2026: Artificial intelligence and the future of learning. OECD Publishing. https://doi.org/10.1787/062a7394-en

Computer Science Teachers Association, & AI4K12 Initiative. (2025). AI learning priorities for all K–12 students. https://csteachers.org/ai-learning-priorities-for-all-k-12-students-about/

Finnie-Ansley, J., Prather, J., Becker, B. A., Denny, P., Luxton-Reilly, A., & Santos, E. A. (2025). Generative AI in computer science education. Cambridge University Press. https://doi.org/10.1017/9781009526884

Lee, Y.-C., Boonprakong, N., Tan, Y., Soh, H., Potanin, A., Kumar, V., Sinha, A. K., Qian, C., Denny, P., El-Assady, M., Oakley, I., Renzella, J., Zhang, A., Singh, J., Lee, W. S., Lin, H.-T., E, J. L., Tang, A., Burnett, M. M., … Cristea, A. I. (2026). Reshaping undergraduate computer science education in the generative AI era. arXiv. https://doi.org/10.48550/arXiv.2606.07545

United Nations Educational, Scientific and Cultural Organization. (2025). AI and education: Protecting the rights of learners. https://www.unesco.org/en/articles/ai-and-education-protecting-rights-learners