When we talk about artificial intelligence, the conversation often focuses on what machines can learn. At AI Connect, I reframed that question by asking what happens when we place the human in the role of teacher.

The first five things to teach a computer are intentionally simple. Teach it how to communicate. Teach it how to play music. Teach it how to draw pictures. Teach it how to do math. Teach it how to play games.

Together, these form a practical and human centered entry point into AI literacy. More importantly, they change the learner’s relationship to technology. The student is no longer just a user of AI tools. They become an active participant in shaping how the system works.

When I think about how far we have come in training intelligent systems, I often reflect on one of my earliest experiences. In 1997, while serving in the Air Force, I coded a system called Swapper in Perl. Its purpose was straightforward: save humans time by automating routine tasks. Building Swapper was my first real experience training an agent to operate within real‑world constraints. It required iteration, careful observation, and a deep understanding of how small rules could shape larger outcomes. Swapper was not perfect, but the act of teaching it how to work laid the foundation for how I think about automation and AI today.

What still surprises me is its longevity. Just last night, a colleague I served with reached out and told me, “I still use Swapper to this day.” Knowing that something built decades ago is still reducing friction and saving time reinforces a simple truth: the most meaningful systems are shaped by thoughtful teaching, not just technical capability.

That experience is why I believe the most powerful way to learn AI is to become the teacher.

Learning by Teaching

When a student teaches a computer how to communicate, they must think carefully about language, context, and meaning. Teaching a computer to play music or draw pictures introduces creativity, pattern recognition, and expression. Teaching math requires logic, structure, and abstraction. Teaching games brings in rules, strategy, feedback loops, and adaptation.

In every case, the act of teaching forces clarity. The human must decide what is essential, what rules matter, what can change, and what should remain fixed. Teaching across these five areas helps learners recognize which domains resonate most strongly with them. Some discover an interest in creative expression. Others are drawn to logic, systems, or strategy. AI becomes a mirror that helps learners identify where they may want to specialize.

Systems Thinking in Practice

These five areas do not exist independently. Communication shapes how games function. Math underpins music and drawing. Games rely on rules, incentives, and feedback. Creativity emerges through constraints.

This is systems thinking in action. Rather than treating AI as a single tool or application, learners begin to see it as a network of interconnected domains. Changes in one part of the system affect the whole. This understanding is foundational for working with complex technologies and for navigating real world infrastructure.

Curiosity as the Engine

Curiosity is what drives this process forward. Once learners realize they can teach a computer, the next question becomes what else it might do. Curiosity encourages experimentation, iteration, and deeper inquiry into how systems behave under different conditions.

Learning accelerates not because answers are predetermined, but because exploration is encouraged and supported.

Innovation Through Exploration

Innovation grows naturally out of curiosity. As learners move between communication, creativity, logic, and play, they begin to combine ideas in new ways. Games become learning tools. Music becomes data. Drawings become models. Math becomes a creative language.

By positioning AI as something to be taught rather than simply consumed, innovation becomes grounded in understanding rather than novelty.

Ethics as a Core Skill

Teaching a computer also introduces ethical responsibility. Learners must decide what a system should say, what it should create, and what it should refuse to do. They must confront the reality that bias, values, and assumptions are embedded in rules and data.

Ethics is not an add on to AI education. It is part of the core skill set. When learners define boundaries and constraints, they begin to understand that technology reflects human choices.

The Human Infrastructure Around AI

AI does not exist on its own. It depends on human infrastructure that includes educators, learners, institutions, communities, and shared values. Teaching the first five things to a computer makes this visible. Every system reflects the people who design it, train it, and deploy it.

By grounding AI education in teaching, systems thinking, curiosity, innovation, and ethics, we prepare learners not just to work with AI, but to steward it responsibly.

The future of AI will not be shaped by machines alone. It will be shaped by the humans who decide how, what, and why, to teach computers.

This post was inspired by AIConnect 2026, a collaborative effort between the Sault Area High School & Career Center and the EUPConnect Collaborative to bring practical, accessible AI education to the Eastern Upper Peninsula, focusing on engagement, protection, and connection.