Developing Students’ AI Literacy and Data Science Skills with Computational Thinking
Quinn Burke, Keun-woo Lee, and Sharin Jacob
Digital Promise
Topics
We’ve all had the experience of truly purposeful, authentic learning and know how valuable it is. Educators are taking the best of what we know about learning, student support, effective instruction, and interpersonal skill-building to completely reimagine schools so that students experience that kind of purposeful learning all day, every day.
Computational thinking routines teach concepts like abstraction, decomposition, and algorithms—across the curriculum—building a foundation for AI literacy and data science skills.
If you’re a teacher curious about computational thinking (CT) but unsure where to start, you’re not alone. Many educators–even experienced ones–find the terminology daunting at first. Concepts like abstraction, decomposition, and algorithmic thinking can feel like a whole new language. The good news? You don’t need to be a computer science expert to start integrating computational thinking into your teaching. You just need the right tools and some time to build your computational thinking muscle.
Start with Screen-Free, Hands-On Ways to Embody Computational Thinking
Before diving into high-level concepts or programming languages, many teachers benefit from “unplugged” activities–hands-on, screen-free ways to embody computational thinking ideas. The entry-level computer science website CS Uplugged has been a longstanding resource for educators to introduce their students to computational concepts without screens. For example, one favorite activity involves a simple dance routine that teaches students the nature of “loops” and how moves (not unlike code) can repeat and speed up (or slow down) to create fun and unusual dances . When students move through these concepts physically, they gain a deeper, intuitive understanding of them. By embodying the idea of a loop first, it then becomes that much more intuitive to sit down at a computer and apply it through code.
For the past eight years, Digital Promise has been working with a wide range of schools and districts to find these key entry points to computational thinking, and our Computational Thinking website has resources to help you get started. Our Literacy and Computational Thinking Toolkit is a teacher-friendly resource full of highly visual, accessible materials, including sample routines, anchor charts, and examples of how CT intersects with literacy instruction—especially helpful for early adopters.
Build AI Literacy with Computational Thinking Fundamentals
Having worked with over a dozen school districts nationally through our K-12 CT Pathways program, what is also clear is that districts who start unpacking computational thinking among their teachers and students early on are much better positioned to be informed consumers of AI as both a concept and a tool. Central to the machine learning characteristic of AI is large data sets and using algorithms to sort such data and identify recognizable patterns, enabling AI to make predictions, detect trends, and/or support decision-making. Although AI involves computation at an exponentially more advanced level, it still relies on fundamental concepts—such as sorting, sequencing, and pattern recognition—that can also be explored through those same “unplugged” activities at entry levels. At Digital Promise, we see computational thinking as a key component to help districts get their minds around what is referred to as “AI literacy” – namely, those key skills that underlie machine learning, including data science and algorithmic thinking. These are the building blocks that allow students not only to use AI, but also to understand it, evaluate it, and ultimately create with it.
Digital Promise’s AI Literacy Framework outlines these three core modes of engagement: use, understand, and evaluate. Each entails computational thinking as part of their respective foundations. For example, understanding how an AI model works requires students to think critically about data inputs and outputs and how algorithms operate—a natural extension of computational thinking principles.
We’re also proud of Digital Promise’s Responsibly Designed AI product certification, which helps school systems navigate the fast-changing landscape of edtech tools with clear standards for data privacy, bias, and transparency.
Collaborate with Colleagues to Apply Computational Thinking in K-12 Education
AI certainly represents a new horizon for learners, introducing capabilities and challenges that redefine how knowledge is acquired, applied, and assessed. It marks nothing short of a paradigm shift in education, demanding new approaches to teaching critical thinking, problem solving, and digital literacy. It is not simply a matter of “using” AI but also ably understanding and evaluating it, and, for this, learners need to have the opportunity to flex their own mental understanding of computational thinking through regular computational thinking routines.
If you’re just getting started with computational thinking, you don’t have to go it alone. There’s a growing community of educators, researchers, and designers working to make computational thinking relevant, accessible and meaningful—no matter your content area. From dance-based loops to data-driven AI instruction, we’re excited to keep building this future with you.
Listen: Computational Thinking and Early Literacy
NGLC is grateful for our collaboration and partnership with EDU Café Podcast that brings fresh voices and insights to the blog. Listen to the EDU Café episode where Digital Promise's Dr. Quinn Burke, Dr. Sharin Jacob, and Keun-woo Lee discuss the integration of computational thinking and literacy in education, particularly for young learners.
Learn More about Computational Thinking on the Next Gen Learning Blog
Literacy and Computational Thinking for Young Learners - Sharin Jacob, Digital Promise
Using computational thinking routines in elementary English Language Arts (ELA) deepens literacy practices for all students and can be especially helpful for multilingual learners.
Integrating Computational Thinking into ELA Classrooms - Keun-woo Lee and Alessandra Rangel, Digital Promise
Computational thinking supports core literacy goals like organizing ideas, identifying patterns, and building arguments, without requiring extra English Language Arts instruction.
Explore Additional Computational Thinking Resources from Digital Promise
This article originally appeared on the Digital Promise blog on August 5, 2025. Photo at top courtesy of NGLC.
Quinn Burke, Keun-woo Lee, and Sharin Jacob
Digital Promise
Quinn Burke is the senior director of computational thinking and learning pathways at Digital Promise.
Keun-woo Lee is a project director for the Powerful Learning Design Lab at Digital Promise, where she leads design based research to support the needs of learners and educators in our increasingly digital world.
Sharin Jacob is a researcher in computational thinking and learning pathways at Digital Promise.
