When people ask how to get better results from artificial intelligence, they usually expect the conversation to begin with prompts.

They want to know which words to use, which framework to follow, or which tool will give them the strongest output. Those questions make sense. Most of us were introduced to generative AI through a blank text box, so it is natural to assume that the quality of the result depends primarily on what we type into it.

But the longer I work with AI, the more convinced I become that prompting is not the most important skill.

The quality of what we receive from AI depends on the quality of the thinking we bring to it.

Artificial intelligence can help us organize information, generate possibilities, analyze patterns, and accelerate work that once took hours. What it cannot do on our behalf is decide what matters, whose experience should shape the solution, which problem is worth solving, or what kind of future we are trying to create.

Those decisions still belong to us

That is why AI needs a Human SPARK.

Not because the technology is incapable, but because capability without direction is not the same as progress.

How Artificial Intelligence Is Changing Work and Learning

Let’s zoom out for a second.

Every major technological shift changes the way people work, communicate, and create value. The printing press expanded access to information. The Industrial Revolution reorganized labor. The internet transformed how we search, share, and build. Smartphones placed extraordinary computing power in our pockets and changed our relationship with time, attention, and one another.

Artificial intelligence is part of that longer story, but it is also different in an important way.

Previous technologies often helped us perform physical tasks or access information more efficiently. AI is beginning to participate in activities we have traditionally associated with thinking itself. It can draft, summarize, interpret, simulate, recommend, and generate. It can produce work that appears creative, strategic, or analytical within seconds.

That is why the conversation feels so consequential.

We are not simply deciding whether to adopt another tool. We are reconsidering how decisions are made, how expertise is defined, how learning is assessed, how teams collaborate, and how organizations create value.

Yet many institutions are responding to this moment as though AI were simply another software platform to add to an already crowded list.

They purchase licenses. They schedule training. They teach people how to use a chatbot. They ask employees or students to experiment. Then they wonder why the deeper work has not changed.

You don’t need more tools. You need a smarter way to work.

If we add AI to inefficient systems without redesigning those systems, we may complete the same tasks faster, but we will not necessarily create better outcomes. We may even scale the very problems we hoped technology would solve.

The real opportunity is not simply to use AI.

It is to reconsider what the work should look like now that new possibilities exist.

The advantage has never belonged to the tool

When I graduated during the 2007 recession, I believed I had done what I was supposed to do. I had worked hard, earned the credentials, and prepared for a career based on the rules I had been taught.

Then the economy changed.

Like many people entering the workforce at that time, I discovered that being qualified for the world I had expected did not guarantee that the world would still be there when I arrived.

I was laid off from my first teaching position. At the time, it felt deeply personal. I questioned whether I had made the right choices and whether the path I had worked toward still existed.

One of the books that helped me understand what was happening was Seth Godin’s Linchpin. It challenged the idea that the safest strategy was to become highly compliant, follow the established path, and wait for the system to reward you. Instead, Godin argued that value increasingly belonged to people who could create, connect, solve meaningful problems, and contribute in ways that could not easily be reduced to a checklist.

That insight changed the way I thought about work.

The world was not simply asking me to acquire another skill. It was asking me to reconsider who I needed to become.

Over the years, that question led me into new roles, new industries, and new ways of thinking. I learned about design thinking and began to understand that innovation was not the result of having the most impressive idea. It began with empathy, observation, curiosity, and the willingness to question assumptions.

I worked across K–12 education, higher education, and technology. Each environment gave me a different view of how people and institutions respond to change. The organizations that adapted most effectively were not always the ones with the most resources or the newest tools. They were the ones willing to learn, listen, experiment, and redesign.

Technology played a role in every part of that journey, but technology was never the advantage by itself.

The advantage came from the habits that made it possible to respond to change with intention rather than fear.

That lesson matters even more now.

AI will continue to improve. The models we find impressive today will eventually feel ordinary. The interfaces will become easier, the outputs will become stronger, and many capabilities that currently require training will become embedded into the tools people already use.

Knowing how to operate one platform will not be a lasting advantage.

Knowing how to think, learn, question, and redesign will be.

Your Human Advantage begins before the prompt

Much of the early conversation about generative AI focused on prompt engineering. People were told that better wording would lead to better results, and to some extent, that is true.

Clear instructions help.

Context helps.

Examples help.

But a well-written prompt cannot compensate for unclear thinking.

AI can produce a polished response to the wrong question. It can create an efficient solution to a poorly framed problem. It can reinforce assumptions we never paused to examine. It can give us more of what we asked for without helping us determine whether we should have asked for it at all.

That is why the most important work often happens before we open an AI tool.

Before asking AI to create something, we need to understand the situation.

Before asking it to solve a problem, we need to consider who is experiencing that problem and why.

Before asking it to improve an idea, we need to define what improvement means.

Before asking it to write on our behalf, we need to decide what we actually believe.

This is what I mean by prompting the human before prompting the machine.

The future is not asking us to become more mechanical in our thinking. It is asking us to become more intentional about the qualities that make our thinking valuable.

Our ability to notice what others overlook.

Our willingness to care about another person’s experience.

Our capacity to imagine alternatives that do not yet exist.

Our judgment about what should be prioritized.

Our courage to question systems that no longer serve people well.

Our ability to connect ideas, people, and possibilities in ways that create something new.These are not soft skills on the margins of technological progress.

They are what give technological progress direction.

From prompt framework to thinking framework

When I first developed the SPARK framework, I introduced it as a way to help people communicate more clearly with AI.

SPARK stands for:

Situation: What is happening, and what context does the AI need to understand?

Problem: What challenge are you trying to address?

Aspiration: What better outcome are you hoping to create, and why does it matter?

Results: What would a useful, specific outcome look like?

Kismet: What are 3-4 ways I can approach this?

At first glance, SPARK may appear to be a prompting framework. It certainly helps people write more effective prompts because it encourages them to provide context, clarify the challenge, and define the desired result.

But over time, I realized that its greatest value had very little to do with helping machines understand us.

SPARK helps us understand our own thinking.

The Situation asks us to slow down long enough to examine the context rather than rushing toward a solution.

The Problem forces us to clarify the challenge instead of treating the first visible symptom as the root issue.

The Aspiration reconnects the work to purpose. It asks not only what we want to produce, but why the outcome matters and who should benefit from it.

The Results require us to define success clearly rather than accepting vague improvement.

The Kismet creates room for discovery. It reminds us that collaboration with AI should not be limited to confirming what we already know. It can help us encounter alternatives, questions, and connections we may not have considered.

This is why I no longer think of SPARK primarily as a prompt framework.

It is a thinking framework.

It structures the human reasoning that should take place before and during our collaboration with AI. The prompt becomes stronger because the thinking becomes stronger.

That distinction matters because platforms will change. The language of prompting may become less visible as AI becomes embedded into everyday workflows. We may interact through voice, video, agents, or systems that anticipate our needs without requiring a carefully constructed paragraph.

But the need for clear thinking will not disappear.

If anything, it will become more important.

The easier it becomes to generate an answer, the more responsibility we carry for evaluating whether it is the right answer.

AI should expand judgment, not replace it

There is a temptation to evaluate AI primarily by how much work it can remove from our plates.

That is understandable. People are overwhelmed. Teachers, leaders, healthcare professionals, entrepreneurs, and teams across industries are managing too many responsibilities with too little time. The promise of completing work faster is not trivial.

But efficiency is only one measure of value.

A faster process is not automatically a better process. An automated decision is not automatically a fairer decision. A polished output is not automatically an accurate or meaningful one.

The strongest uses of AI do more than reduce effort. They improve the quality of our thinking.

A leader might use AI to identify assumptions in a strategic plan, simulate how different stakeholders could respond, and explore alternatives before making a decision.

A teacher might use AI to redesign an assignment so students have more ways to demonstrate understanding, while still applying professional judgment about developmental needs, classroom culture, and academic goals.

A student might use AI to challenge an argument, compare perspectives, receive feedback, and revise an idea rather than simply asking the system to produce the final answer.

A team might use AI to surface patterns across interviews or survey data, but still return to the people represented in that data to understand what the patterns mean.

In each case, AI contributes to the work without becoming the sole author of the decision.

That is the shift we need to make: from using AI as an answer machine to using it as a thinking partner.

A thinking partner can help us expand possibilities, examine blind spots, test assumptions, and move more quickly through early drafts. It can give us something to respond to, question, improve, or reject.

But partnership still requires participation.

If we stop bringing our own curiosity, experience, values, and judgment to the process, we do not become more innovative. We simply become more dependent.

What this means for education

Nowhere is this conversation more urgent than in education.

For generations, schools have been organized around the assumption that access to information is limited and that demonstrating knowledge often means reproducing the correct answer independently.

AI complicates that model because information, explanation, and content generation are now available on demand.

The instinctive response has been to focus on control.

How do we prevent students from using AI?

How do we detect it?

How do we create assignments that cannot be completed with it?

Those questions may be necessary in specific contexts, but they are not enough. They keep us focused on preserving the mechanics of the current system rather than examining whether those mechanics still serve our goals.

The better question is not simply how to keep AI out of learning.

It is how to design learning that develops the judgment students will need when AI is present.

That means helping students investigate real problems, evaluate sources, explain their reasoning, make decisions, create original work, and reflect on how their ideas changed.

It means designing assessment around the learning process rather than only the final product.

It means giving students opportunities to interview people, gather evidence, test ideas, receive feedback, and revise.

It means teaching them to ask not only, “What can AI produce?” but also, “What do I think, what evidence supports it, who could be affected, and what responsibility do I have for the outcome?”

The future of education cannot be reduced to teaching students how to use AI responsibly. It must help them become people capable of making responsible decisions in a world shaped by AI.

That is a much larger assignment.

It requires us to move beyond digital literacy and toward human agency.

Students need to know how to use powerful tools, but they also need a strong enough sense of identity, purpose, and judgment to decide when not to use them, when to question them, and when to create a different path entirely.

If education continues preparing students to compete with machines at producing predictable answers, we are preparing them for a contest they are unlikely to win.

If we help them become curious researchers, thoughtful collaborators, ethical decision-makers, creative problem-solvers, and architects of their own learning, we prepare them to do what technology cannot do on its own: give possibility meaning.

The future is a design challenge

We often talk about the future as though it is something approaching us from a distance.

We ask what AI will do to work, what it will do to schools, and what it will do to society. That language makes change sound inevitable and positions people as passive recipients of technological progress.

But technology is not destiny.

The systems we build, the incentives we create, the behaviors we reward, and the decisions we make will determine how AI shapes our lives.

That means the future is not only something to predict.

It is something to design.

Design begins with empathy. It asks us to understand the people affected by the problem before deciding what the solution should be.

Design requires us to frame the problem carefully, because the way we define a challenge determines the possibilities we can see.

Design invites experimentation. It allows us to create, test, learn, and improve rather than waiting for certainty that may never arrive.

Most importantly, design reminds us that people are not obstacles standing in the way of implementation. Their experience is the reason implementation matters.

This is where our Human SPARK becomes essential.

AI can help us move more quickly through the design process. It can organize research, generate prototypes, analyze feedback, and offer alternatives. But it cannot replace the human responsibility to decide which future is worth building.

That responsibility belongs to leaders, educators, students, designers, policymakers, parents, and communities.

It belongs to all of us.

Who do you want to become?

When people begin using AI, they often ask what the technology can help them do.

I think there is another question worth asking first:

Who do you want to become?

Do you want to become more curious or more certain?

More thoughtful or simply faster?

More capable of understanding others or more efficient at avoiding them?

More willing to question assumptions or more dependent on convenient answers?

Technology can support many different versions of us.

It can help us become more creative, more informed, and more generous with our time. It can also encourage us to outsource effort, accept shallow conclusions, and move so quickly that we stop noticing the human consequences of our decisions.

AI does not determine which path we take.

Our habits do.

The question of human advantage is not about proving that people are superior to machines. It is about deciding which human qualities we want to strengthen as machines become more capable.

That work is ongoing.

We do not discover our Human SPARK once and keep it forever. We develop it through the questions we ask, the problems we choose to solve, the people we listen to, and the courage we bring to moments of uncertainty.

We design it every day.

AI still needs a Human SPARK

AI will continue to become faster, more accessible, and more capable. Tasks that seem impressive today will become ordinary, and the tools that currently require training will become easier to use.

That is not the variable we control.

What we can control is whether we invest as intentionally in developing people as we do in adopting technology.

We can teach people to slow down before solving.

We can help them frame better problems.

We can create cultures where curiosity is valued, experimentation is safe, and empathy is treated as a strategic advantage.

We can design learning that strengthens judgment instead of rewarding compliance.

We can use AI to create more time for relationships, reflection, and meaningful work rather than simply filling every available moment with more output.

The future does not need people who can only write better prompts.

It needs people who can recognize what matters, imagine what could be better, and use every tool available to help bring that possibility to life.

AI still needs a Human SPARK.

Not because the technology is incomplete, but because every meaningful innovation still begins with a person who notices something others overlook, cares enough to improve it, and has the courage to imagine a better way forward.

That has always been true.

I believe it always will be.