"Intentionality: Guide for Choosing the Right Tool" - Image designed using Google Gemini

Walking through the halls of schools, you may hear “these students have difficulty staying focused and are easily distracted by social media or notifications.”  This demonstrates how technology is often blamed for distraction, shortened attention spans, and disengagement from learning. One may also hear students exclaiming how AI helped them to create a perfect set of images to illustrate their handwritten story.  While these moments seem worlds apart, both perspectives miss something important.  Technology is simply a tool.

Tools are designed to make processes more efficient and yield stronger results. You may have used a hammer to nail two pieces of wood together or a shovel to dig a hole in the garden. Both tools assist in making challenging tasks more manageable. Now imagine seeing those pieces of wood transformed into a beautiful porch staircase or noticing a thriving shrub blossoming nearby in the spring. Most people would not immediately think about the hammer or shovel involved in creating those results unless they intentionally reflected on the process behind them.

Technology in the classroom should function much like those tools. Ideally, it should not dominate our attention when examining student learning. Instead, it should quietly support the process of learning and help students reach meaningful outcomes. The emphasis should remain on what students are learning, how they are thinking, and how they are applying their understanding.

I have often compared technology’s role in education to that of a supporting actress or actor in a movie. Supporting actresses and actors play an essential role in helping create the overall impact of a film, even though they are not the centerpiece. Without strong supporting roles, the story may lose depth and effectiveness. Technology should function in much the same way in education. It should support and enhance learning experiences without becoming the primary focus of the classroom.

When technology becomes the star of the classroom, educators may need to recalibrate their approach and return to intentionality. Learning should always remain at the center. The priority should be the development of student understanding, critical thinking, creativity, and problem-solving rather than the tool itself.

Intentionality begins with clearly identifying what students need to learn and demonstrate. Effective instruction starts with thoughtful planning that supports progress toward intended learning goals. This planning recognizes that students may require different supports, experiences, and pathways as they move through the learning process.

Being intentional also means carefully selecting the tools and strategies that best support student learning. Sometimes that may involve digital tools, while other times non-digital approaches may be more effective. The responsibility of the educator is not to use technology simply because it is available, but to determine which tools and experiences best align with student learning needs.

At the same time, educators must ensure that students are comfortable using a variety of tools that support learning. Preparing students for the future requires providing opportunities to thoughtfully engage with both digital and non-digital resources. Students should experience learning environments that challenge them to think critically, collaborate effectively, and create meaningful work using the tools best suited for the task.

As educators design learning experiences, the focus should remain on creating meaningful learning journeys supported by intentional choices. The tools selected can have a profound impact on the learning process, the quality of student thinking, and the final product students create. However, the tool itself should never overshadow the purpose of learning.

Perhaps the most important question educators can ask is not, “What technology should I use today?” Instead, the question should be, “What learning experience do my students need, and what tools will best support that experience?” When intentionality guides those decisions, technology becomes what it was always meant to be, a powerful tool that supports deeper learning for all students.

This post was authored by Brian Whitson and refined through a collaborative process with AI (Chat GPT and Google Gemini). The technology was used to sharpen the introductory narrative and ensure the specific examples used—such as AI in creative storytelling—accurately reflect the current educational landscape.  Examine how I used AI to support the final version of this blog post.  Yes, I value the importance of disclosing AI use and demonstrating how I used it.

AI Literacy Relationship to Other Disciplines (page 17) | https://ailiteracyframework.org/wp-content/uploads/2025/05/AILitFramework_ReviewDraft.pdf

​As Artificial Intelligence (AI) becomes increasingly ubiquitous in our world, the importance of developing strong AI literacy programs in K–12 schools has become impossible to ignore. AI has rapidly entered our daily lives, and we cannot simply hope it fades away or choose to avoid it altogether. Instead, we must ensure that students understand what AI is, how it works, and how to use it appropriately. Equally important is helping them grapple with the legal, moral, and ethical dilemmas associated with this technology. At its core, this work requires a return to the foundations of strong digital citizenship so that students and future generations can engage with AI in informed, responsible, and thoughtful ways while recognizing both its opportunities and its limitations.

In my work with educators, I often encounter two dominant reactions to AI. Some lament its use, expressing concern that students will lose the ability to think critically and creatively. Others are optimistic, intrigued by AI’s promise and its perceived ability to do things that “have never been done before.” The reality, however, lies somewhere in between. Most AI applications today function by rapidly identifying patterns and connecting preexisting “dots” to generate new arrangements and configurations of “dots” that can feel extraordinary at first glance, largely due to their speed and scale. Our immediate responsibility is clear: we must create learning experiences that help students understand how AI works, how it is trained, and how it can be used in positive, meaningful, and ethical ways. This reality underscores the need for intentional AI literacy experiences that allow students to explore both the promises and the pitfalls of AI technologies.

AI Literacy Begins with Digital Citizenship
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At the heart of AI literacy is digital citizenship, which is closely connected are media literacy and digital literacy. While these terms each carry distinct emphases, they all reflect ideas about understanding and using digital technologies responsibly and in ways that respect the dignity and well-being of others. Rather than treating these literacies separately, we must prioritize learning experiences that help students make sense of digital tools and prepare them for a world that will continue to evolve alongside technology.
In the work my colleagues and I do to support educators in digital teaching and learning, we are often asked a deceptively simple question: What should we teach about AI? This question naturally leads to a discussion of AI literacy frameworks. While AI tools and applications have proliferated rapidly, frameworks designed to guide AI literacy instruction have emerged at a much slower pace. In many ways, using AI appears far more glamorous than teaching how it actually works. Yet without a clear instructional foundation, meaningful learning is difficult to achieve.

The Digital Promise AI Framework 

In response to these requests for direction for both students and adults, I often rely on two AI literacy frameworks. The first is from Digital Promise and was the first framework I used to design AI learning experiences. This framework is straightforward and organized around three core components: Understand, Use, and Evaluate. Educators often appreciate not having to navigate overly complex terminology, and despite its simplicity, the framework is robust enough to support a wide range of learning activities.

One activity I frequently use involves sharing an AI-generated image of the downtown area when I am facilitating professional learning. I ask the experts in the room, the educators who live and work in that community, to respond to two questions: What do you notice? and What do you wonder? The responses are always fascinating. Educators are typically quick to point out surface-level inconsistencies: “There are no people,” “The streets are too clean,” or “There are no cars.” While these are all good observations, I less frequently hear comments such as “The trees do not have shadows on any side.” This often leads to a conversation about why we gravitate toward what feels like the “low-hanging fruit” when evaluating AI-generated content. I then ask participants to identify which component of the Digital Promise AI Literacy Framework they were using in this activity. This reflection helps them connect the framework to an authentic experience and deepens their understanding of how AI literacy can be embedded in meaningful ways.

Empowering Learning in the Age of AI Framework

The second framework I often reference comes from a joint collaboration between the European Union and the Organisation for Economic Co-operation and Development (OECD), supported by Code.org. This collaboration resulted in the publication Empowering Learning in the Age of AI: An AI Literacy Framework for Primary and Secondary Education, with the most current version available in draft form as of May 2025. This publication makes two particularly valuable contributions to AI literacy.

First, it identifies four key AI competencies: Engaging with AI, Creating with AI, Managing AI, and Designing AI. These competencies encompass the knowledge and skills individuals need to interact effectively with AI systems. The framework also emphasizes the attitudes required for responsible AI use and includes a strong focus on ethics. One aspect of this publication that resonates deeply with me is AI literacy’s connection to other disciplines, including data science, digital literacy, media literacy, design thinking, ethics, and computer science. The visualization illustrating these relationships (found on page 17 of the publication) serves as a powerful reminder of the interdisciplinary nature of AI and what I hope my own son is experiencing in his education.
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Designing Experiences That Lead to Real Understanding

As educators move forward in helping students and others understand how AI works, along with its opportunities and limitations, it is important to remember that this responsibility is shared. While selecting a framework or model can provide helpful structure, what matters most are the learning experiences we design and the opportunities we create for students to deepen their understanding of AI in real-world contexts. As I often share, my son and his peers are very good at telling adults what they think we want to hear. Yet when they are asked to demonstrate their understanding, it becomes clear that many still struggle to apply their knowledge of AI meaningfully. This struggle, however, is not a failure; it is where learning and growth occur. Real-world experiences that challenge assumptions and invite students to consider multiple perspectives are essential. ​

Just last week, my extraordinary colleague, Meredith Ward Hill and I were sharing a session on Computational Thinking at ISTE Live - ASCD Annual Conference in San Antonio.  The session, “Solving the 🧩Puzzle:  Putting the Pieces Together via Computational Thinking” was created originally to help North Carolina educators who teach in the K-5 setting to connect their curriculum to Computational Thinking.  


Computational Thinking involves the use of “special thinking patterns and processes to post and solve problems” (Jane Krauss and Kiki Prottsman “Computational Thinking {and Coding} for Every Student).   This session was developed to support a strong foundation in Computational Thinking for elementary educators to enable a successful entry for students as they complete the Computer Science requirement for graduation in North Carolina.  {I cannot stress the importance of this book by Jane Krauss and Kiki Prottsman as it has a lot of relevant information.  While some of their examples are older since the book was written several years ago, it provides a solid foundation on Computational Thinking and its elements}.

In creating this session, we sought to help educators connect the four pillars of Computational Thinking to the curriculum and learning activities that are used in the classroom.  Those pillars with a brief description are below:

Pattern Recognition - analyzing and reviewing data to determine a connection or similarity between these data.  This is much like putting the pieces of a puzzle together where you look for the shapes needed as well as the matching images.  

Abstraction - ignoring certain pieces of information in order to move closer to a solution; think about how often we help students learn to ignore certain pieces of a text in order to answer a question or complete an activity.  This helps to ensure that students are not “hung up” on unnecessary details and can focus on the task.

Decomposition - this involves breaking a challenge into simpler tasks.  In the classroom, we often ask students to make a large task, such as multiplying three digit numbers, into smaller pieces so the smaller piece is more manageable.  

Algorithm - a set of directions to be followed much like a recipe.  We often see this reflected in math processes that are taught.  It can also involve helping students learn how to construct sentences in a foreign language class where they have to conjugate verbs with the appropriate subjects.

I also like the emphasis that is placed on problem posing and solution with Computational Thinking.  Too often, I worry that many educators shy away from Computational Thinking due to their lack of experience with it or perception of it as being something that only coders do.  At the heart of Computational Thinking is problem solving where specific processes are applied.  Yet, we need to broaden our lens to include helping students learn to identify problems.  By finding problems to solve, our students see a real world connection to what they are learning and see meaning in what they are learning in school.  If we can connect the skills and information that students learn in school to the world in which they live, then we can engage them in meaningful work that has value and authenticity.  

In this session, we wanted participants to understand and live the four pillars as they sought to solve problems.  We introduced the related vocabulary and gave them a chance to practice and assess themselves.  However, this was simply not enough though - we wanted them to experience the four pillars in a way that was impactful so we worked on several problems for them to solve including an activity where they are tasked with adding up the numbers between 1 and 200 in 30 seconds with no devices permitted.  This activity was taken from Code.org.  We then moved into using the second part of this activity where the participants worked on determining the directions for a game with no directions.  During each of these activities, we asked participants to identify and explain which of the four pillars of Computational Thinking that they used.  This helped to reinforce the four pillars in an experiential way that connected their definition to their application.  

A new highlight to this session involved the application of Computational Thinking to AI.  We shared an activity from MIT Media Lab from “An Ethics of Artificial Intelligence Curriculum for Middle School Students“ by Blakeley H. Payne supervised by Cynthia Breazeal, published August 2019 from the MIT Media Lab, licensed under Creative Commons.  

In this activity, the Teachable Machine website is used to classify images of cats and dogs.  This activity helped to connect the importance of Computational Thinking to a real life application.  As we experienced in the demonstration during our session, the size of the data set used to train the AI is important.  Also, we saw that AI really wants to please its user, in fact so much that it even tried to classify me as a cat or a dog (as a side note, it felt that I was about 65% dog).  This activity has many interesting applications that can be used with students to better understand AI and Computational Thinking including the importance of having a variety of different sets and biases.  One of the images of a pomeranian was incorrectly identified as a cat.  This allowed us to have a more lengthy discussion about what data points that AI may be using to classify images. Additionally, this could lead to a more lengthy discussion about the importance of AI classifying images or real people correctly.  Imagine being in a self-driving car and failing to realize that a pedestrian is crossing the street.  

We were so incredibly fortunate to have an amazing group of participants from all over the world including Brazil, Guam, Hawaii, and the United States.  This session was extremely memorable to me as the group was very participatory and collaborated.  We all learned so much together.  Here is some feedback that we received from the attendees:

“I enjoyed the unplugged activities that drove home the pillars of Computational Thinking. The activities stretched my understanding of AI bias in outputs.”

“The connection that I made with computational thinking and my current teaching practices was powerful. Thank you for sharing so many resources and activities that I can use in my classroom.”

As we move forward, we must continue to help classroom educators learn more the four pillars of Computational Thinking and connect these pillars with the learning activities occurring in their classrooms.  ISTE has created Computational Thinking Competencies to guide educators as well.  Be sure to check out Code.org for incredible activities that are both plugged and unplugged.  Our ISTE presentation and related resources also contain additional opportunities for educators to learn more about how to connect Computational Thinking to their classroom.