The future is unpredictable. We know that. So, when we try to design learning experiences that “prepare students for the future,” we have to recognize that we don’t always know what they’ll need. In this article, I explore the counterintuitive reality that the best way to prepare students for the future is by empowering them in the present. And one of the best ways to get there is through deeper learning.

Listen to the Podcast

If you enjoy this blog but you’d like to listen to it on the go, just click on the audio below or subscribe via iTunes/Apple Podcasts (ideal for iOS users) or Spotify.

 

AI Is Changing Our World

In 2017, I stood in a university innovation lab, surrounded by high-tech gadgetry. It looked amazing but I wasn’t sure how any of this technology could possibly impact me. I was amazed by the state-of-the-art robots with their hydraulic limbs and the latest Virtual Reality simulations. But in this moment, I was stuck staring at an ordinary flat screen monitor.

The researcher leaned forward and said, “Ready to see something mind-blowing?”

“Yes, please,” I answered. Everything I had seen thus far was already mind-blowing. But this . . . well . . . it was a simple flatscreen monitor.

He clicked a button. All at once, a student work sample appeared. This was the opposite of mind-blowing. It was Calculus homework. I read the student’s question about fluid flow and heat transfer and nodded as if I understood the equation. However, this was an aspect of Calculus I either never learned or never understood. Either way, I could only vaguely grasp what was going on.

The professor clicked a button. Two additional writing samples appeared – each offering their own form of feedback for the student. Still not mind-blowing in the least.

“Which one is human-generated?” he asked.

I read through both responses and shook my head.

He cleared his throat and asked, “Can you determine which of these feedback samples was generated via machine learning?”

I studied both answers again, looking for clues in the syntax. I tried to find the humanity within the language. Perhaps some idioms or casual language to clue me in to the human element. But I couldn’t tell the difference. Both options were clear and concise with just a touch of colloquial friendliness. I shook my head and studied the writing yet again, but the difference was indistinguishable.

“Are they both the AI?” I asked.

“It’s easy to think that. The grad student is an engineer, and we tend to be a little robotic when giving feedback,” he answered.

“I can’t tell the difference between them,” I confessed.

“The correct answer is B,” he said.

I stood in the lab baffled by the progression of machine learning. This wasn’t merely “close enough.” This was it. Artificial Intelligence had finally arrived, and I felt as though I had officially failed the Turing Test. I couldn’t tell whether I was interacting with a human or a machine.

He then continued, “Don’t feel bad. The students can’t tell the difference either. To them, it’s just a chat with an expert.”

“So, you didn’t tell the students which one was the AI?” I asked.

“That’s part of the experimental study,” he responded. “We want to see how students respond to AI.”

I felt like I had failed the Turing test.

My head swirled with questions of the Eliza Effect and the notion of singularity. I thought about deep fakes and the future of humanity. I’m pretty sure an image from Blade Runner popped into my head and maybe a scene of Data from Star Trek TNG.

I started wondering what this would mean in the future. But even so, it still felt just like that . . . the distant future. I began interviewing experts and reading everything I could get my hands on. Still, I was shocked when I first saw ChatGPT.

 

The Dawn of the Generative Age

When ChatGPT launched in late 2022, it felt like a tipping point. Suddenly, generative AI wasn’t just a futuristic idea, it was something anyone could use. People were writing poems, solving math problems, and holding conversations with a machine that sounded remarkably human. It wasn’t just a new tool; it was a cultural moment that sparked awe, debate, and a wave of experimentation almost overnight. I began blogging about this topic more frequently and created this sketch video:

Meanwhile, AI continues to evolve. In just the last few months, we have seen Generative AI grow by leaps and bounds. We have witnessed a shift from quick, surface-level responses toward deeper reasoning and more nuanced thinking. It’s starting to make complex connections and weigh trade-offs. It’s not quite there yet but it feels as though is closer to thoughtful decision-making. To be clear, this shift doesn’t mean AI is truly thinking. But it does mean students need to engage with it more like a thought partner and less like a search engine.

And this is just the start.

AI is reshaping the world faster than most of us can process. It is changing the nature of work, creativity, communication, and even how problems. For decades, we told students to climb the corporate ladder, following a linear path toward success. But that ladder is gone. In its place is a complex maze filled with twists, dead ends, and shifting paths. Our students will need to navigate that maze.

AI is not a shortcut through the maze. It isn’t even a map. It’s more like a tool (similar to that of a compass) that students will need to use wisely as they navigate this unpredictable future. If students want to thrive, they will need curiosity, adaptability, and the ability to learn continuously. So, what does it mean to navigate this maze? How do we, as educators, adapt to this new reality? The counterintuitive answer is not to “prepare students for the future.” Just stick with me, I promise.

 

The Problem with “Preparing for the Future”

When I was in the seventh grade, one of my teachers called the entire class up to the front of the room. He held up a shiny golden disc in wild excitement.

“This will change education forever,” he said, eyes gleaming. “Someday, you’ll be able to pick up one of these discs and learn exactly what you need to learn. No more taking notes from a teacher. You won’t have to learn from someone like me.”

I felt uneasy about learning from a golden disc instead of a human. I liked my teacher. I liked how he would change his explanations on the spot just by reading our body language. I liked the way he made us laugh. I enjoyed the inefficient way he got off topic and we randomly learned things that weren’t in the textbook. And I loved those collaborative labs that we did each week. Why would I want to replace him with a golden disc?

“This is the future of education, kids,” he said with a grin. “This will change things forever.”

But it did not change education forever. I haven’t seen a laser disc in three decades and I feel pretty confident that my own kids’ teachers will not be replaced by golden discs. Not now. Not ever.

I have lived through each iteration of “the future of education.” With the advent of the Internet (it was once capitalized), we could hop on the Information Superhighway and get answers to all the questions we had. We could simply Ask Jeeves and he’d let us know what to do.

Then it was one to one devices followed by interactive whiteboards and then netbooks. The next wave involved smartphones and tablets, then the promise of adaptive learning programs, and eventually virtual reality.

With each wave, we have seen a focus on the future. It might be a computer programming class or a robotics program. While these programs are great, the value isn’t in learning highly technical skills. It’s in learning the essential skills that students will need in an unpredictable future. Programming is important because it teaches logic and problem-solving. And even now, as Generative AI can do most of the programming for us, I think programming still has a place. My son is majoring in engineering right now partly because of the robotics and programming experiences he had in high school. However, those same skills can be learned by playing chess. Robotics is vital for collaboration, problem-solving, iterative thinking, and engineering. But those same skills can be learned through a low-tech maker project. What ultimately matters and truly endures, are those skills students will use as they navigate the maze.

As we think about AI, the focus has to go beyond “training students how to use AI tools.” I’m a huge fan of prompt engineering and I even developed the FACTS Cycle as a way to do prompt engineering with students:

But I don’t think the goal should be prompt engineering. It should be the habit of being intentional and focused. It should involve the skill of information literacy. But if our goal is simply “learn to use AI” the skills will be obsolete quickly. Instead, we need to focus on the the core competencies that students will need as they navigate the maze.

 

The Future Has Never Been Known

In 1984, psychologist and political scientist Philip E. Tetlock began a project to see how well expert researchers could predict the future. Over the next two decades, Tetlock gathered 82,361 probability estimates about the future. The end result was staggering. Expert researchers, with access to classified information and connections to powerful change-makers, were dreadful at forecasting the future.

There was, however, a silver lining. Tetlock found a group of researchers who regularly outperformed their peers. These experts tended to be flexible thinkers and more open to new insights. They also had seemingly disconnected interests that they drew upon. He called these researchers “foxes” for their nimble, flexible approach. According to Tetlock, foxes “draw from an eclectic array of traditions, and accept ambiguity and contradiction.”

He called the other group hedgehogs because they tended to stay burrowed down in their knowledge as experts in a single subject. In other words, if we want to know about the future, we  need to ask, “What does the fox say?” That’s right, I made a bad dad joke right there.

Six years later, the Intelligence Advanced Research Projects Activity launched a contest to see how well research teams could forecast the future. While most teams chose well-known experts,  Tetlock and Barbara Mellers created the Good Judgment Project. Instead of choosing experts, they asked for volunteers and chose a team from the 3,200 applicants. Here, they focused on people who had diverse interests and tended to read works in unrelated disciplines.

The Good Judgment Project team crushed the competition. So, what made this team different than everyone else? First, they were intellectually humble. They knew that the future was unpredictable and thus they were more likely to seek out other opinions. Furthermore, they were divergent thinkers. Rather than viewing information as isolated and siloed, they made connections between ideas and disciplines. Finally, they were curious.

One of the top team members described the team as “curious about, well, really everything.”

If this doesn’t surprise you, it’s because this curiosity is what we, as educators try to cultivate in our students. This is what happens every time teachers do projects and experiments and debates that inspire wonder and creativity in their students.

While it might not seem like a big deal, these are the experiences that inspire students to become the innovators of the future.

It’s easy to choose futurism over innovation and forget just how clueless we humans are about the future. As teachers, we can’t predict what Artificial Intelligence will look like in the upcoming decade. We can’t predict what new disruptive technologies will change our world. But we do know that our students will need to be foxes rather than hedgehogs and as teachers, we can craft the learning experiences that empower students to become those foxes.

 

Helping Students Develop the Depth Advantage

In order to navigate the maze, our students will need to be flexible and nimble. They will need to be divergent thinkers and collaborators. In other words, they will need to be foxes.

In order to stand out in a world of AI, our students will need depth. Surface-level knowledge and basic task completion will no longer be enough when machines can generate passable content in seconds. What will set students apart is their ability to think critically, solve complex problems, connect ideas across disciplines, and create original work that feels human and meaningful. Deeper learning will allow students to do what AI cannot. They will need to build authentic connections and approach challenges with curiosity and resilience. In a world flooded with quick answers and instant content, depth will be the true advantage.

The William and Flora Hewlett Foundation first developed their proposal for deeper learning in 2010. While I love their initial framework, I have revised and expanded these ideas based on some of the significant changes we have seen in the last fifteen years. Here is how I define the eight core competencies of deeper learning.

• Focus: Deeper Concentration in a Distracted World
  Students need to develop the ability to concentrate for longer stretches of time. In a world of constant notifications and interruptions, focus is becoming a rare and valuable skill.

• Mastery: Deeper Understanding in an Age of Instant Answers
  Instead of skimming the surface, students need to go deep into concepts and skills. Mastery means they can apply knowledge flexibly and creatively accurately over time.

• Problem-Solving: Deeper Thinking in a World of Smart Machines
  Students need to learn how to solve real, meaningful problems that require critical thinking, creativity, and persistence. It’s about applying knowledge to new and complex situations, not just memorizing solutions.

• Curiosity: Deeper Discovery in a Sea of Information
  In an age where information is everywhere, students need to stay curious. It’s curiosity that drives inquiry, innovation, and the motivation to keep learning even when answers aren’t obvious. It’s what allows them to be intellectually humble and ask the questions AI hasn’t yet asked.

• Self-Direction: Deeper Drive in a Changing World
  Students must learn how to manage their own learning, set goals, and adapt when things change. Self-direction helps them navigate uncertainty and take ownership of their growth.

• Resilience: Deeper Effort when Faced with Big Challenges
  Deeper learning requires struggle and students need the resilience to push through confusion, boredom, and frustration. They learn that growth often happens when the learning is the most challenging.

• Collaboration: Deeper Connection in an Isolated World
  Working well with others has never been more important. Students need to build skills in communication, empathy, and teamwork in ways that foster real collaboration.

• Communication: Deeper Divergence in a Sea of Sameness
  Students need to develop a clear, authentic voice that stands out. Whether it’s writing, speaking, or listening, communication should reflect deep thinking and connect meaningfully with an audience.

At its core, deeper learning is a pedagogical model. It’s an approach to teaching that moves away from shallow learning and into deeper mastery, deeper engagement, and deeper effort. Sometimes it looks traditional (like extended silent reading) and other times it looks cutting edge (using an AI chatbot to develop deep understanding through inquiry). But the common thread is depth in a culture of shallow.

At the student level, deeper learning is a skill that students can master, a habit that students can develop, and a mindset that students can internalize. When this happens, we empower students in the present so they can face an unpredictable future.

 

A Deeper Dive into Deeper Learning

I’m currently working on the final stages on a new book about deeper learning. Note that the book cover right now is still in the early prototyping stages. I might add some details to my sketch.

Description:

Teachers are facing unprecedented challenges. Student attention is declining. Burnout is on the rise. Compliance-driven learning is leaving everyone frustrated. But deeper learning offers a hopeful alternative. In a world of instant answers, fast clicks, and constant noise, The Depth Advantage offers a different path rooted in deeper learning. Our students don’t just need more information at a faster speed. They need depth to thrive in a rapidly changing world shaped by AI and automation.

Through research-driven insights and practical strategies, The Depth Advantage explores how we can create classrooms where curiosity is cultivated, resilience is strengthened, and mastery is pursued at a slower, more deliberate pace. Using personal stories and creative sketches, Dr. Spencer lays out the foundations of deeper learning by identifying eight core competencies that meet today’s challenges head-on: Focus, Mastery, Problem-Solving, Curiosity, Self-Direction, Resilience, Collaboration, and Communication. Each of these pillars provides a way to help students grow not just academically but as human beings who are ready to thrive in a complex world.