From Artificial Intelligence to Augmented Intentionality

Why I think the real shift in AI is not just stronger output, but a new relationship with meaning

There was a time when computers felt magical.

That may sound strange now, because most of us live inside layers of computing so thoroughly that we barely notice them anymore. We move a mouse, drag a window, tap a phone, type into a text box, and expect the world to respond. The machine does what it does so quickly, so consistently, and so quietly that the miracle disappears into familiarity.

But the miracle is still there.

Even something as ordinary as moving a pointer across a screen is a staggering act of engineering. The computer has to keep track of where the pointer was, what was under it, what belongs there when the pointer moves away, where the pointer is now supposed to be, how the screen should be redrawn, and how all of that should happen fast enough that the human being operating it experiences continuity instead of flicker. We no longer stop to admire it, because our astonishment has been replaced by habit.

That loss of astonishment matters, because it hides the larger story of what computers have been becoming.

The Arc of Computing

For a long time, computers were fundamentally about execution. They were extraordinary amplifiers of repetition. They were brilliant at carrying out instructions, applying formulas, sorting records, storing information, and repeating tasks at scale. Give the machine something precise, and it would do it again and again without complaint. That repeatability has always been one of the computer’s superpowers.

This is why spreadsheets matter. This is why databases matter. This is why programming matters.

If I have three thousand numbers and want to add ten percent to all of them, the computer can do that almost instantly. If I want those same numbers sorted, filtered, summarized, graphed, or stored for later retrieval, the computer can do that too. The power comes from the fact that a machine can apply a process relentlessly, accurately, and at speed. That is not trivial. It is one of the foundational reasons computing transformed the world.

But there has always been a limit to that kind of power.

Traditional computing is excellent at doing what it has been told. It is much weaker at understanding what was meant. If I hard-code the wrong percentage into a spreadsheet, the computer will repeat that mistake faithfully. If I store the wrong value in a database, the database will preserve the error beautifully. Older computing systems gave us amplification, but not much interpretation.

That is one reason the story of computing can be told as a progression:

• first we learned to work with data
• then we learned to shape data into information
• then we learned to analyze information through business intelligence
• and now we are stepping into something else entirely

Data by itself is inert. It can be stored and retrieved, but storage alone does not make something meaningful. Information emerges when the data is given structure, relationship, and form. Business intelligence goes further by helping us identify trends, correlations, projections, and patterns. It helps us make better decisions by broadening what we can see.

But contemporary AI is not simply a stronger reporting layer.

It seems to understand.

Understanding as the New Interface

I do not mean that in a mystical sense, and I do not mean it as a claim that AI is conscious in the same way humans are conscious. I am not making that argument. I am making a narrower and, I think, more practical claim: modern AI systems can often work with language, context, pattern, and purpose in a way that feels much closer to understanding than anything earlier computing gave us.

That matters immensely.

For most of computing history, if I wanted a machine to help me, I had to adapt myself to the machine. I had to think in terms of commands, formulas, buttons, fields, and predefined structures. If the system did not match the shape of my thought, then my thought had to be flattened until it fit the software.

That is no longer the whole story.

Now I can start with the mess.

I can start with half-formed ideas, with nonlinear thoughts, with a spoken monologue, with an intuition that I can feel but cannot yet explain cleanly. I can start with context, confusion, purpose, and fragments. Then I can bring those into a conversation with an AI system that is capable of helping me reorganize, restate, refine, and challenge what I am trying to say.

That is a different relationship than the one I had with earlier software.

For me, this is not an abstract observation. It is a daily working reality.

I have spent years trying to articulate ideas that do not naturally arrive in polished paragraphs. They arrive as a mesh. They arrive as partial structures, analogies, intersections, questions, and directions of thought that are all connected to each other before they are ever linear. Voice memos helped, but only partially. Dictation helped, but only partially. Transcription gave me searchable text, which was already a major improvement over raw audio, but the transcription itself was still often too messy to be useful on its own.

The problem was not that I had no thoughts.

The problem was that the mechanism for translating those thoughts into readable form was too blunt.

Written language is linear. Thought is often not. At least mine is not.

This is where AI changed the game for me. It gave me a system that could work with the nonlinear texture of what I was saying and help shape it into linear language without entirely losing the underlying structure. That is not the same thing as having the AI think for me. It is much closer to having a patient editorial partner who can help me discover what I was trying to say in the first place.

That distinction is important enough that I want to state it directly:

I do not use AI because I want to do less thinking.

I use AI because I want to stay with my thinking longer.

If AI results in me thinking less, then I am probably using it poorly. If it helps me think more deeply, revise more carefully, structure more clearly, and communicate more honestly, then I am using it well.

From Tool to Thought Partner

This matters especially in education.

One of the common fears around AI is that it will allow students to bypass learning. That fear is not irrational. It is absolutely possible to use AI in shallow ways. It is possible to use it to simulate effort, generate polished language without understanding, or substitute output for thought. If that is how it is used, then yes, it undermines learning.

But that is not the only way to use it.

The more interesting educational question is not simply whether AI is present. The more interesting question is what kind of thinking the AI is supporting.

That is why I increasingly talk about AI as a thought partner rather than only as a tool.

A tool completes a task.

A thought partner helps develop an idea.

A tool gives me output.

A thought partner helps me clarify intention.

That is a dramatically different educational posture. It suggests that the role of AI in a classroom is not primarily to replace effort, but to support reflection, drafting, revision, simulation, questioning, and perspective-taking. It suggests that students can use AI to externalize their thinking, test their assumptions, and refine what they mean. It suggests that a teacher can use AI not only to generate content, but to design better learning environments, shape more adaptive instruction, and give more precise support.

Why I Call It Augmented Intentionality

This is where the phrase that keeps coming back to me is not artificial intelligence, but augmented intentionality.

The word artificial does not get me very far. The practical issue is not whether the machine is “fake.” The practical issue is whether it can take up my intention and help me act on it more effectively.

That is what I keep seeing.

When I bring a rough idea, a spoken transcript, a lesson design problem, a writing challenge, or a student-support issue into an AI conversation, what matters most is not that the system has a stockpile of facts. What matters most is that it can work with what I am trying to do.

It can often see the difference between:

• writing something quickly and writing something well
• generating feedback and giving useful feedback
• delivering content and creating value for learners
• solving the wrong problem efficiently and solving the right problem intentionally

That is why intentionality matters so much.

It is not enough to ask the system to do a task. The deeper move is to explain who it is being in the interaction, why the work matters, who the work is for, what constraints matter, and what kind of quality is actually required. That is when the interaction stops being merely transactional and starts becoming collaborative.

Teaching, Precision, and Value

I see this very clearly in my teaching.

I do not think of teaching as content delivery. Content comes naturally. It can always be delivered. What matters more is investigation, experimentation, trial, error, care, and discovery. Teaching is not merely the act of putting information in front of students. Teaching is the act of helping students encounter something in a way that changes what they can do with it.

And I hold students in a very particular way.

I think they are giants.

They are carrying regular life, work, stress, family, expectations, uncertainty, and then on top of all of that they are trying to learn difficult things. Some of them are taking five courses at once. Some are struggling with language. Some are struggling with confidence. Some are trying to become more precise because they know precision matters. They deserve serious effort in return.

That affects how I use AI.

My intentionality as an instructor is not efficiency for its own sake. It is value for students.

If I use AI to help grade work, that does not mean I am outsourcing care. It means I am trying to create space for more precise feedback, faster iteration, better pattern recognition, and better spot-checking. The important part is not the automation by itself. The important part is whether the system is helping me fulfill my actual responsibility to the learner.

The Software Is Downstream of the Intention

The same thing is true when I build tools.

When I built a small system for a high-school classroom visit, the intention was not “build a web app.” The intention was: I have one hour, about thirty students, mixed readiness, and very little tolerance for friction. How do I give them real value quickly? How do I reduce the drag of typing errors, setup confusion, and slide fatigue? The software was downstream of the teaching problem. The app existed in service of the intention.

That same pattern repeats in other systems I have built.

The HISTEditor was not mainly about creating another text editor. It was about helping me write academically while reducing the cognitive burden of APA 7 formatting, citations, references, and paragraph-level thinking. LMSAI was not about automating schoolwork. It was about interrogating course structures, assignments, and materials responsibly so I could understand them better and make more informed decisions. SimulCorp was not about imitating enterprise software for its own sake. It was about immersing students in workplace-like structures so that project management, timesheets, and workflow systems would feel less alien when they encountered them in the real world.

In every case, the real question was not “What app should I build?”

The real question was “What intention am I trying to fulfill?”

That is the shift.

Once you start asking that question consistently, AI stops looking like a magical answer machine and starts looking like a system that can help you design, refine, and carry intentional structure.

Conversation as a Customization Surface

That is also why I think conversation is becoming part of the interface.

For a long time, programming was the primary way we customized computers. That is still true in many ways, and I do not think programming is going away. But conversation now gives us a second layer. It allows a human being to work from purpose downward instead of always from mechanism upward.

That does not remove the need for rigor. In many cases, it increases the need for rigor, because now the challenge is not just whether the code compiles. The challenge is whether the intention has been framed well enough, constrained well enough, and reviewed carefully enough that the output is actually useful.

And that brings me to where I think we are now.

We are moving into a world where the most valuable people may not be the ones who know every technical detail in advance. They may be the ones who can lead, investigate, frame intention clearly, question outputs intelligently, and keep refining until something meaningful emerges.

That is the space I increasingly see as my own.

I am not valuable because I already know every answer. I am valuable because I am not stopped by not knowing. I investigate. I test. I iterate. I pull together systems, structure, language, and people until a pathway appears. AI strengthens that process because it allows me to move faster through ambiguity without pretending that ambiguity is gone.

Closing

So when I say that I think AI is best understood as augmented intentionality, what I mean is this:

The deepest value of the system is not that it can produce an answer.

The deepest value is that it can help a human being take a rough, partial, messy, deeply felt intention and move it toward clarity, structure, precision, and action.

That is a different kind of computing than what we are used to.

It is not merely storage.

It is not merely retrieval.

It is not merely calculation.

It is not merely automation.

It is a computing environment that can begin to participate in the shaping of meaning.

Carefully.

Provisionally.

Never beyond human responsibility.

But far enough that we need a better framework for understanding what is happening.

For me, that framework is this:

Computers used to help us execute.

Then they helped us organize.

Then they helped us analyze.

Now they are beginning to help us fulfill intention.

And if we learn how to use that well, the result will not simply be faster work.

It will be better thinking.