Teaching AI Literacy in PK-12 Education: A Guide to Preparing Students for the Future Part One
Authors: Yerin Seung; info@ciddl.org
Artificial intelligence (AI) continues to impact our daily lives significantly. AI literacy, defined as the knowledge and skills required to understand, interact with, and evaluate AI technologies, has become a crucial competency for students to navigate this landscape of technology. Our previous post explored the concepts, importance, and core components of AI literacy. Building on those foundational concepts, this article focuses on practical activities for teaching AI literacy in educational settings.
Activities to Teach AI Literacy
As outlined by Almatrafi and colleagues (2024), six components of AI literacy include Recognize, Know and Understand, Use and Apply, Evaluate, Create, and Navigate Ethically. Given the debate over the “Create” component, which requires computational skills, this post focuses on five core components, synthesizing insights from research on teaching AI literacy.
Recognize
“Recognize” involves being aware of AI integration in daily applications and distinguishing AI from non-AI tools. This component helps students understand the role of AI in human-AI interactions so they can make decisions and avoid overreliance. Educational programs have adopted AI robots such as the Jibo robot, Anki’s Cozmo robot, and Amazon Alexa to introduce AI in K-12 settings, especially those in the early ages. These tools allow students to interact with AI and get familiar with them. Also, students can learn to distinguish AI-generated content, such as photos and videos, and explore simulations of AI that mimic human-like behaviors. In an environment where hands-on experience is not available, teachers can use AI-related movies and discussions to spark curiosity and understanding.
Know and Understand
“Know and Understand” equips students with foundational AI knowledge, including how AI processes data, the role of sensors and programming, and humans’ role in creating AI systems. A key focus is on machine learning (ML), a branch of AI that enables computers to learn from data and make decisions or predictions without explicit programming. ML seems like a complicated concept, but Su and colleagues (2023) revealed that students as early as preschoolers were able to develop a basic understanding of ML with tools such as PopBots. Amplo and Butler (2023) introduce students to basic ML concepts and problem-solving steps, such as defining a problem, ideation, and prototyping, helping them connect abstract AI concepts to real-world applications. Several studies engage K-12 students in learning ML concepts across grade levels using tools like Scratch, App Inventor, Google Teachable Machine, and Machine Learning for Kids.
Use and Apply
In the “Use and Apply,” students interact with AI and delve into practical applications. They use AI tools to accomplish tasks, integrate AI concepts into various contexts, and adapt AI systems for specific objectives, emphasizing the collaborative role between humans and AI. Building on the foundational knowledge covered in “Know and Understand,” students can apply tools like Scratch, App Inventor, and Google Teachable Machine to solve real-world problems in this stage. Project-based learning is known to be effective in this stage. For example, as mentioned in the study of Van Brummelen and colleagues (2021), students could identify challenges, like difficulties in accessing recipes while cooking, and develop solutions, such as a voice-enabled cookbook app using App Inventor and Amazon Alexa. Beyond advanced tools, simpler AI-powered applications—like navigation, translation, and chatbots—offer opportunities for hands-on learning. As Yetişensoy and colleagues (2023) noted, students discussing AI applications in their daily lives can enhance cooperative learning and deepen their understanding of AI’s practical uses.
Evaluate
“Evaluate” focuses on critically analyzing and interpreting AI application outcomes. This component forsters informed opinions and judgments about AI systems’ performance, fairness, and limitations. Students are encouraged to analyze and interpret the results of their AI projects, identifying the strengths and limitations of AI. For example, Yetişensoy and colleagues (2023) highlighted that after students interact with chatbots in cooperative learning, they can discuss the chatbot’s strengths and limitations and how these natures influence the learning process. Similarly, Amplo and Butler (2023) engaged students with critical thinking on the data bias, limitations of ML models, and their impact on the outcome of the ML project.
Navigate Ethically
“Navigate Ethically” emphasizes understanding AI’s ethical and societal implications, including fairness, accountability, and transparency. Students learn about privacy, bias, and inclusivity to promote the socially responsible use and development of AI technologies. For example, Eguchi and colleagues (2021) developed an AI ethics curriculum for middle schools, combining hands-on and mainly unplugged activities. Students explored the socio-technical nature of AI systems, recognizing how various stakeholders’ goals and perspectives shape AI’s impact on society. Similarly, Ng and colleagues (2022) engaged students in simulating AI applications to highlight the importance of data biases in AI-driven decision-making.
Conclusion
It is important to note that while this blog post categorizes AI literacy into five distinct categories, the components are interconnected. Many studies blended multiple components in their instructional practices to support holistic understandings of the nature of AI. In addition, AI literacy extends beyond computer science since AI is entrenched in our daily lives. However, current AI literacy research often focuses heavily on advanced technological concepts, suggesting the need for interdisciplinary approaches. To expand the realm of AI literacy, educators across the fields need to be equipped with AI knowledge, tools, and ethical considerations. By taking these steps, we can prepare students for a future increasingly shaped by AI and foster a generation of informed, critical, and socially responsible AI users.
Keep the Conversation Going
Have you taught AI literacy to your students? Which components of AI literacy did you focus on? What are some strategies and considerations for teaching AI literacy? Share your experiences and opinions in our community!