First things first - let’s define a few things:
We can’t ignore the presence of AI in schools. AI is shaping the lives of students. Right now.
Just like cell phones, social media, and every other advancement since the creation of educational systems, educators are left to figure out how to adapt to the new status quo.
Because AI in the classroom needs to be managed. It can’t be ignored.
Think of how many AI touchpoints are in our world today. Recommendation engines. Search results. Navigation apps. Generative AI. Hiring algorithms. Medical diagnostics.
And when they aren’t taught how to judge these results, our students are often left with bad reports, false facts, and biased opinions.
The systems that hold these programs together are simpler, and more complex than we realize. They are written by humans, with human error, and yet we give them our complete trust.
Students deserve to know how their tools work. They deserve to have the chance to learn and evaluate the outputs.
This is where computer science becomes essential. AI literacy isn't just about recognizing when a chatbot makes a mistake. It's about understanding patterns, data, algorithms, abstraction, inputs and outputs, and the ways computers process information differently than people do. Those concepts don't begin with AI. They begin with computer science.
So how do we prepare students? By high school, many students have already spent years interacting with AI-powered systems. Waiting means allowing these technologies to shape how students search, communicate, and make decisions. So how do we begin teaching AI Literacy at the elementary and middle school levels?
A lot of people assume education around AI Literacy has to wait until 9th or 10th grade because AI is "too complicated."
But in a recent conversation between Bryan “BT” Twarek of CSTA and Keisha Tennessee of the Virginia Department of Education (VDOE), Keisha shared that “we already know how to teach complex things. Like learning how to ride a bicycle.”
So how do we get from bikes to bytes?
Computer science education is increasingly becoming a world where the tool cannot be the teaching. We cannot expect a child to learn about VEX robotics and then instantly build their own robot. We can’t teach SCRATCH block coding and expect a student to immediately begin coding in Python.
However, the solution has been right in front of us the entire time. Scaffolded learning, just like learning to ride a bike.
You don't teach a six-year-old how to mountain bike by tossing them on a trail.
You teach balance.
Then pedaling.
Then steering.
Then traffic.
Then trail riding
The same can be done with AI Literacy. In elementary school, we can start with patterns, inputs and outputs, evaluation, and general computational thinking.
Middle school adds explicit AI systems (yes, middle schoolers CAN build AI bots and programs), evaluating outputs, understanding training data, and bias.
High school adds neural networks, regulation, ethics, policy, and technical implementation.
The conversation quickly shifts away from “How do we teach the latest tools?” toward “How do we design curriculum to support future-proof skills?” The focus becomes curriculum design.
The activity often stays the same. The sophistication changes.
So, we don’t teach AI Literacy at the ‘mountain bike level’ to elementary school children. That would be silly. But we do teach evaluation, judgement, and that computers process information very differently than humans do. Then we grow and expand on those concepts.
One step at a time, we build the structure and skills that support broad, sustainable capabilities in our students and educators. We teach enduring concepts like resilience, problem solving, and computational thinking. We teach systems thinking, where students learn how each piece is part of the whole. Because when these students finally become young professionals, whether they are managing an army of bots, a team of developers, or simply writing code themselves, the skills inherent in computer science will take them farther than any individual programming language they may learn.
Whether a school is technology rich but curriculum poor or vice versa, the end result should be the same - focus on the skills, not the tech.
I started this post about a week ago. Since then, three new versions of ChatGPT have been released. We are far past the point where we can expect to ‘keep up’ with the latest apps, tech, and tools in the world of education. Every day looks different than the one before. This is why CodeVA has chosen to focus our attention on skills, not tools. When a student is able to question, evaluate, understand, and apply reason to their work - they are better prepared to navigate whatever comes next.. We want to empower educators to prepare their students for that future. That’s the larger goal behind AI Literacy.
Great learning has never depended on having the newest tool. It depends on thoughtful curriculum and skilled educators.
Watch the full conversation
Bryan “BT” Twarek of CSTA and Keisha Tennessee of VDOE dig much deeper into AI literacy, curriculum, equity, assessment, and what educators need to prepare students for an AI-enabled world.
Watch the webinar → https://home.edweb.net/webinar/compsci20260513/