This week, I had the opportunity to lead a dynamic session for educators on how students can use physical computing to not merely understand AI, but also to question outputs, consider ethics, and foster responsibly in a digital age.

AI tools are no longer futuristic. They are here, embedded in our daily lives. Students may engage with predictive text, facial recognition, recommendation systems on streaming services, and more on a consistent basis. The constant exposure to AI powered tools leads to a need for students to go beyond surface-level exposure. Students need experiential, purposeful learning to help them see how AI works, why it matters, and what their role is as future creators and citizens.

Why Physical Computing + AI?

The speed at which AI systems produces output can make them appear almost magical. Physical computing can help demystify the technology and support critical skepticism of AI outputs. In the recent session, educators explored how microcontrollers like microbit can bring AI to life in tangible ways. In connecting sensors to produce inputs to AI models or logical decision-making algorithms, students can see how AI uses data to “make decisions”. You can recreate similar experience by exploring activities at https://microbit.org/ai/

Physical computing helps demystify AI. As students build and test systems that make decisions based on the data used to train it, conversations around ethics, bias, and responsibility emerge. Student experience with physical computing provides tangible evidence of how systems process information and determine outputs.

Pedagogical Power

Educators left the session with more than just technical knowledge of how to use physical computing tools to teach AI concepts. The session also focused on pedagogy meant to spark curiosity and foster real world problem solving. Pedagogical strategies included the following.

• Inquiry-based learning: Educators were encouraged to begin lessons or units with a thought provoking question such as “How do sensors gather data?” and “What could go wrong if a system misinterprets a signal?”
• Computational thinking: While infused throughout the lesson educators experienced as students, after the experience I pointed out computational thinking practices embedding into the lesson which included breaking down problems, modeling solutions, and noting patterns in data.
• Ethical exploration: Computer science practice 1 asks students to “Foster an Inclusive Computing Culture”. As such, lesson participants considered varied ethical questions about AI such as: Who created this model? What voices were left out? What happens if a system gets it wrong? Who might be negatively impacted by AI outputs from this model? How might we increase accessibility and validate diverse perspectives in a refined design?
• Student agency: Rather than merely asking students to answer questions, we increase relevance and infuse social justice when we ask our learners to seek problems that might be alleviated or solved by their learning. In the physical computing + AI lesson, educators indicated that their learning about AI systems could help them ideate automated sensor solutions for healthcare management, natural disaster preparedness, and more.

Educators experienced firsthand how engaging relevant, physical computing is for students, particularly when it is combined with AI concepts. Students enjoy observing a project that moves, reacts, or responds. Additionally, physical computing also provides instant feedback to fuel learning.

AI Literacy is Digital Literacy

If we want our students to thrive in an AI-powered world, we have to do more than just mention the term “AI” in class. We need to help them become critical thinkers, responsible creators, and ethical citizens.

Physical computing, paired with AI and real-world problems, offers a way to ground abstract ideas in concrete, creative exploration and gives educators a meaningful path toward teaching AI in a way that is accessible, relevant, and engaging.