The Importance of AI Literacy

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.

Earlier today, I learned that I successfully completed the requirements to be named an ISTE certified educator.  ISTE, the International Society for Technology in Education, is the premiere organization supporting educators in using technology and digital learning to transform education.  With strong roots in curriculum, instruction, and pedagogy, ISTE forms a global network of educators who seek to make learning more relevant, accessible, and purposeful for students.

ISTE certification requires commitment, effort, and a willingness to reflect and evaluate your instruction practices and their alignment with technology and digital tools.  The ISTE certified educator program is centered on the ISTE Educator Standards.  These standards include:

1). “The Learner - Educators continually improve their practice by learning from and with others, and exploring proven and promising practices that leverage technology to boost student learning.

2).  The Leader - Educators seek out opportunities for leadership to support student empowerment and success to improve teaching and learning.

3).  The Citizen - Educators inspire students to positively contribute to and reasonably participate in the digital world. 

4).  The Collaborator - Educators dedicate time to collaborate with both colleagues and students to improve practices, discover and share resources and ideas, and solve problems.  

5). The Designer - Educators design authentic, learner-driver activities and environments that recognize and accommodate learner variability.

6).  The Facilitator - Educators facilitate learning with technology to support student achievement of the ISTE Standards for Students.

7).  The Analyst - Educators understand and use data to drive their instruction and support students in achieving their learning goals.”

​The ISTE certified educator program involves two components:  

1) A classwork component that takes 5-8 weeks, requiring both synchronous class meeting and asynchronous work. 
2) the submission of a portfolio showcasing the applicant’s work and its connection to each of the ISTE Educator Standards. 

My experience, like many of my cohort members' experience, involves a time commitment, lots of thought and reflection, and intentionally planning for ways to demonstrate competence in each of the standards.  There are a total of 25 individual criteria that require evidence such as images, student work, and narrative explanations.  The evidence submitted demonstrates the candidate’s ability to meet the criteria.  Of the many things that I have done in my career, I will say that this ISTE certification process was one of the most rigorous and challenging growth opportunities that I have experienced.

After completing this process, I now think and approach the work that I do to support educators and students with a very different lens - that of the ISTE Educators Standards.  Earlier this week,  I was in a high school  statistics class and instantly started connecting what the teacher was doing with several of the ISTE Educators Standards.  I clearly saw the teacher had created authentic, learner-drive activities supported by technology (Designer) and the teacher using technology to analyze student learning (analyst) in less than 5 minutes.  

The ISTE certification process is very rigorous and requires time and intentionality.  I had to spend time reviewing and digesting the standards and its related indicators and consider how my practices demonstrated competence in the various standards.  In many cases, I simply had to consider what evidence that I had available to show that competence.  In other cases, I had to design new learning experiences with learners to help demonstrate that competency. 

This process was at times frustrating, exhilarating, exhausting, and rewarding.  On the day when I submitted my final portfolio, I immediately felt relief but then realized how much I had learned and grown as a result of this experience.  It took me almost a year to complete the entire process but it was so transformative in helping me grow.  As I begin to construct professional learning for adults, I now think about how I can help those educators to create their own learning goals, support their learning through digital tools, and how to help them grow and reflect on their learning. 

Additionally, I also think about what skills and experiences that our students need to be prepared for a world that is ever changing and evolving.  I also think about how we can better harness the power of technology and digital tools to better support and scaffold the learning of our students.   Additionally, we must also couple this with the need to ensure that we model and mentor others in being digital citizens who contribute to make our world a better place for everyone.  

ISTE certification is, indeed, rigorous and challenging but the transformative opportunities it offers for growth as an educator are truly infinite.  It will change how you approach and use technology and digital tools in the best possible way.  The end result is that we are better able to support the learners with which we work to create revolutionary opportunities and experiences to learn that are truly transformative. 

​Also I would like to express my appreciation to the North Carolina Digital Learning Initiative Fund supported by the NC General Assembly for supporting public school educators across North Carolina in obtaining their ISTE certification.  


Revised original written blog with feedback from Chat GPT on January 12, 2024