In conversations about artificial intelligence in education, the focus often drifts toward tools, platforms, and policies. But underneath all of this lies a deeper skill set that AI both depends on and helps develop: computational thinking.
As TeachAI notes, “One of the major benefits of learning about AI is developing computational thinking, a way of solving problems and designing systems that draw on concepts fundamental to computer science and are applicable to various disciplines.”
In other words, engaging with AI is much more than simply using chatbots or experimenting with machine learning models. It’s an opportunity to strengthen the very habits of mind that prepare students to approach problems systematically, creatively, and critically.
The relationship is twofold. On one hand, understanding how AI systems function requires skills like decomposition, pattern recognition, and abstraction which are the building blocks of computational thinking.
On the other hand, working with AI offers authentic practice in these skills, giving learners a chance to see how they apply not just in computer science but across subjects like math, science, the humanities, and even the arts.
In today’s classrooms, this matters more than ever. Students are growing up in a world where AI is embedded in everything from search engines to healthcare to social media. By framing AI as a context for developing computational thinking, educators can shift the conversation away from fear of replacement and toward empowerment helping students see themselves as thinkers, designers, and problem-solvers.
What Is Computational Thinking?
There isn’t just one definition of computational thinking (CT). Over the years, scholars and organizations have offered slightly different ways of framing it, depending on whether they were writing for computer scientists, K–12 educators, or policymakers. But despite the different wordings, the heart of the concept remains the same: it’s about using the core principles of computer science as a lens for problem-solving.
Two of the most widely cited definitions capture this idea clearly. Computer scientist Jeannette Wing (2006) described computational thinking as “solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science” (p. 33).
A more recent formulation from TeachAI echoes this, calling CT “a way of solving problems and designing systems that draw on concepts fundamental to computer science and are applicable to various disciplines.”
Both definitions emphasize two things. First, CT is not just about programming or learning to code. It’s a way of approaching problems that can be applied broadly, from analyzing a science experiment to organizing an essay or designing a classroom project.
Second, CT is about transfer, it gives learners the ability to take practices from computer science, like abstraction, decomposition, or algorithmic thinking, and apply them in contexts far beyond the computer lab.
In other words, computational thinking isn’t only a technical skill. It’s a mindset. And it’s a mindset that helps students (and teachers) navigate the increasingly digital, data-driven world we live in.
The Circle of Literacies
When we talk about computational thinking, it’s easy to imagine it as a standalone skill set. But in practice, it sits at the center of a wider ecosystem of literacies that every learner needs in today’s world. These literacies overlap and reinforce one another, creating a foundation for both everyday digital participation and deeper problem-solving with AI.
Computer literacy is often the starting point, knowing how to use everyday devices, software, and digital tools. From there, digital literacy broadens the scope, emphasizing the ability to create, communicate, and navigate effectively in digital environments. Closely linked is information literacy, which equips learners to find, evaluate, and apply reliable information, a critical safeguard against misinformation.
As data becomes central to nearly every field, data literacy adds another layer, giving students the capacity to read, analyze, and interpret data for insights. Then there’s AI literacy, which builds on these skills to ensure learners can use, question, and critique AI systems responsibly.
Other literacies deepen the problem-solving toolkit. Procedural literacy develops an understanding of rules, processes, and systems, the logic behind how digital and computational environments work. Computational literacy extends this further by combining tools, logic, and collaboration to explore ideas across different domains.
At the heart of this circle is computational thinking itself: the practice of applying abstraction, decomposition, algorithms, and evaluation to tackle complex problems. Together, these literacies form a kind of “constellation,” where each supports the others. For educators, the key is not to treat them as separate checkboxes, but as interconnected habits of mind that help students become fluent, critical, and creative participants in a digital and AI-driven world.
Characteristics of Computational Thinking (ISTE)
Computational thinking has been carefully defined for K–12 education by organizations like the International Society for Technology in Education (ISTE) and the Computer Science Teachers Association (CSTA). Their joint framework lays out a set of core characteristics that make CT concrete and teachable in classrooms.
The first is formulating problems, or framing challenges so they can be tackled with computer-based tools and approaches. Once the problem is identified, the next step is organizing data: logically structuring, sorting, and analyzing information to make it useful.
Using abstractions comes next: simplifying complexity by highlighting patterns, creating models, or working with simulations that strip away unnecessary details. This is followed by algorithmic thinking, the practice of designing clear, step-by-step procedures that lead to workable solutions.
Of course, not every solution works the first time. That’s why evaluating solutions is essential: testing and refining different options to find the most efficient and effective path. Finally, computational thinkers learn to generalize, taking methods that worked in one situation and applying them to new contexts, across subjects and disciplines.
Collectively, these six characteristics create a practical roadmap for developing computational thinking. As ISTE emphasizes, the goal is not to turn every student into a computer scientist, but to give all learners a set of habits and strategies they can carry into science, math, the humanities, and everyday life. As Wing (2006) puts is, “CT’s essence is thinking like a computer scientist when confronted with a problem” (p. 39).
Related: 6 Foundational AI Guides for Teachers
Computational Thinking and AI Literacy Poster
To make these ideas more practical, I created a visual that captures the key literacies and characteristics of computational thinking, drawing on insights from ISTE, TeachAI, and other sources mentioned above. My goal was to design something teachers can quickly share with their students or use as a reference in professional development.
The poster highlights how AI literacy connects with computational thinking, and how a wider circle of literacies, from data literacy to procedural literacy, all come together to support problem-solving in today’s classrooms.
Feel free to use this resource in your own teaching. You can display it in your classroom, include it in a lesson, or even use it as a conversation starter in staff meetings.
This poster is also available in PDF version
References
- International Society for Technology in Education, & Computer Science Teachers Association. (2011). Operational definition of computational thinking for K–12 education. Supported by the National Science Foundation under Grant No. CNS-1030054. Retrieved August 23, 2025, from https://cdn.iste.org/www-root/Computational_Thinking_Operational_Definition_ISTE.pdf
- Ruiz, P., Mills, K., Lee, K., Coenraad, M., Fusco, J., Roschelle, J., & Weisgrau, J. (2024). AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology. Digital Promise. https://doi.org/10.51388/20.500.12265/218
- TeachAI. (n.d.). Principles for AI in Education. In AI Guidance for Schools Toolkit. Retrieved August 23, 2025, from https://www.teachai.org/toolkit-principles
- Wing, J. (2006). Computational thinking. Communications ofthe ACM, 49(3), 33-36.
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