Every Teacher Is Already a Computer Science Teacher. They Just Don’t Call It That.

A coworker said something to me recently that stuck, and it didn’t come out of a formal conversation or anything structured. It was one of those moments that happens in passing but stays with you. I had asked her if she ever thought about joining CSTA, and without hesitation she looked at me and said, “I don’t teach computer science!” The reaction was quick, almost surprised, and that sentence hit me just as much as my question seemed to hit her. For a second I just paused—not because I disagreed, but because I knew exactly why she felt that way.

I understood what she meant immediately. There is no coding block in our schedule, no formal CS curriculum, nothing labeled “computer science” or “technology.” From her perspective, computer science lives somewhere else, taught by someone else, in a completely different space. And honestly, that’s not just her, it’s how a lot of teachers see it. But as I walked away from that moment, I kept thinking the same thing over and over again: that’s not actually true. 

In our classrooms, students are constantly doing the kind of thinking that sits at the core of computer science—they just don’t call it that. They look for patterns in what they read, they sequence ideas so they make sense to someone else, they explain their thinking step by step, and when something doesn’t work, they go back, figure out what went wrong, and try again. The work is there, but because we don’t call it computer science, it starts to feel distant, foreign, advanced, or technical, like something separate from what we already do. That’s where the disconnect actually lives, not in the practice, but in how we define it.

In elementary classrooms especially, that thinking is already embedded in everything we do because we don’t teach in isolation. An ELA block is rarely just ELA, it’s often grounded in science, where students are reading, building knowledge, and explaining ideas at the same time. A science block might require heavy reading, writing, and discussion, where language becomes the tool students use to process and communicate understanding. Social studies, literacy, and content don’t sit separately, they overlap constantly.

So when we talk about computer science, it doesn’t need to exist as something separate to be real. It’s already there, living inside the work we do every day. When a student is solving a math problem and realizes something is off, then goes back to explain where their thinking broke down and fixes it, that’s debugging. When students follow or create step by step directions—whether they are solving a problem, explaining a process, or retelling a story—that’s an algorithm. When I introduce coding concepts, even something as simple as text-based blocks, I connect it to things they already understand, like building with Legos, where each piece has a role, order matters, and if something is out of place, the whole structure changes. 

That connection matters because now CS is not something new; it’s something familiar with a different name. A lot of this work doesn’t even require devices. Unplugged activities, like having students give directions, identify patterns, or break down a task into steps, are already part of how we teach. We just haven’t always connected those moments back to computer science.

In a bilingual classroom, this becomes even more visible because students are constantly working across language, identifying patterns, building meaning, and organizing their thinking in ways that others can understand. They are not just answering questions, they are constructing explanations, sequencing ideas, and making connections between what they know and what they are learning. That is structured thinking. That is computational thinking. 

The barrier has never been ability, it has been identity. Teachers don’t see themselves as computer science teachers, so they assume it’s not part of their work, but when you shift the lens, you start to see that it has been there the entire time. And now with AI entering classrooms, this moment becomes even more important. AI can either support this kind of thinking or replace it. If it’s used as an answer machine, students skip the process, they receive output before they’ve had time to think, explain, or make sense of anything, and the steps that matter most begin to disappear. But if AI is used within a structure, it can actually strengthen those habits. This shows up the same way in math. A student will solve a problem, get an answer that doesn’t make sense, and instead of starting over, we go back and look at where the thinking broke down. They explain the mistake, fix it, and try again. That process is the same kind of thinking we talk about in computer science, we just don’t label it that way. 

In my classroom, I use a simple cycle: diagnose, draft, and calibrate. We start by figuring out what we are trying to understand, then we use AI to generate an idea or a draft, and finally we go back and adjust it so it actually fits what we need. That last step is where the thinking stays with the student, and it mirrors an idea that already exists in computer science classrooms: when something doesn’t work, you don’t throw it out, you examine it, adjust it, and try again. That’s not new thinking, that’s familiar thinking being used in a new space.

If we want more students to engage with computer science, we can’t wait until later grades to introduce it as something separate from everything else they experience. We need to start earlier, not by adding another subject, but by naming and building on the thinking that is already happening in classrooms. Elementary teachers already know how to integrate content, they already know how to build connections across disciplines, and they already know how to support students in developing the kinds of thinking that computer science depends on. When teachers begin to see themselves in that work, everything shifts. Computer science stops feeling like something outside of their role and starts becoming something they are already part of. Students begin to understand that this way of thinking is not reserved for a specific class or a specific group of people, it’s something they are already developing every day. Every teacher is already doing this work. They just don’t call it that.

Engage with Your Community

That is part of why communities like CSTA have become so important. They create spaces where educators can explore these conversations together, share classroom practices, and rethink what computer science learning can look like across grade levels and subject areas.

Each year, CSTA New York hosts an annual conference that brings educators together around conversations on interdisciplinary learning, AI literacy, and the evolving role of computer science in K–12 education. This year’s conference will take place on November 14, 2026 at SUNY New Paltz.

Conversations like these matter because they help broaden how we think about computer science education itself—not as something isolated from the rest of the school experience, but as something that can meaningfully connect with literacy, multilingual learning, science, creativity, and problem solving across classrooms. 

If you are outside New York, I also encourage you to explore and connect with your local CSTA chapter as well.

https://newyork.csteachers.org/home

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