🧠Designing Minds: Education for an AI-Augmented Future
Introduction: Standing at the Edge of Abundance
Not long ago, preparing students for the future meant helping them memorize facts, follow instructions, and get comfortable with PowerPoint. Today, those skills are table stakes for the machines. AI writes essays, diagnoses rare diseases, produces hit songs in the style of mid-1990s Björk, and, if you believe the more excitable tech blogs, might even outwit your therapist.
We’re not entering an age where work disappears; we’re entering an age where routine thinking disappears from our job descriptions.
And yet, as we teeter on the edge of a world shaped by artificial general intelligence (AGI)—a world that may usher in unprecedented abundance—we find ourselves asking: What’s left for us humans to do?
The answer isn’t to out-compute the machines. It’s to out-connect them.
Education, then, becomes not a pipeline to employment, but a crucible for creativity, connection, and complexity. It must evolve from siloed memorization into a space where broad thinking, ethical discernment, and curiosity rule the day. If AGI is the new calculator, we must teach students not just math, but meaning.
As someone who has worked across both K–12 and higher education, integrating AI and immersive tools into real classrooms and real faculty development, I’ve seen firsthand what this transition looks like. It’s not a sci-fi leap. It’s a series of small, human-centered changes that add up to something revolutionary.
1. The Pedagogy of Emerging Technology: Equity, Empathy, and Engagement
I didn’t start with AGI in a lab. I started with sand.
While directing the MakerSpace at a school in Los Angeles, I helped introduce a topographic sandbox that projected 3D terrain in real time as students reshaped the sand by hand. They squealed, quite literally, as valleys deepened and mountains rose under their fingers. What had been a dry geography unit turned into a collaborative act of digital creation and embodied learning. It wasn’t just fun—it was sticky. The kind of learning that burrows into long-term memory because it’s tied to movement, imagination, and joy.
That same principle guided our use of VR across the curriculum, from walking through ancient cities in a history class to simulating the human body in biology. We used tools like the relatively inexpensive Quest 2 VR headsets. No expensive gear, no tech wizardry. Just thoughtful integration, rooted in pedagogy, not gadgetry.
These weren’t isolated “tech days.” We trained faculty across subjects to use immersive tools in ways that supported equity and access. One student with ADHD, who struggled in traditional assessments, thrived when he could design an ancient city in VR and explain its social systems aloud. Meanwhile, the PE student who never wanted to participate learned how to box and ended up so engaged he was dripping with sweat by the end of the period. This wasn’t just inclusion—it was empowerment.
2. AI in Higher Education: From Panic to Pedagogy
Fast forward to my current work, where the AI conversation began less with squeals of joy and more with sighs of dread. “Will students use it to cheat?” “Will it make my job irrelevant?” “Is this just Clippy 2.0?”
Valid questions. And the answer to all three is a resounding: kind of, but also not really.
To move the conversation forward, we started hosting workshops that let faculty play with AI tools like ChatGPT in a safe, low-stakes environment. We showed how AI can scaffold essay structure for multilingual learners, generate quiz variations based on reading levels, or even summarize student feedback to improve future instruction.
We went further. We launched an “AI and Academics” resource hub inside our Canvas LMS and helped faculty build custom GPTs tailored to their course materials. Picture a biology bot that references your own syllabus or a literature assistant that speaks in Shakespearean prose, but only covers the books you’ve assigned.
The real breakthrough? Faculty stopped seeing AI as a threat and started seeing it as a teaching assistant with no office hours.
3. The Polymathic Pivot: Why Breadth Beats Depth in the Age of AGI
Let’s talk about Leonardo da Vinci. Or rather, let’s talk about how Leonardo would fail a modern job interview: “Sorry, Mr. da Vinci, we were looking for five years of experience in user-interface design.”
The current education system still prizes narrow expertise. But in a world where AI can out-lawyer, out-diagnose, and out-code us, the advantage shifts.
Welcome to the age of the polymath.
Polymathy—the ability to connect knowledge across domains—was once seen as academic meandering. Now, it may be humanity’s best hope for relevance.
AGI can give you the what. But it’s not great at the why, the how, or the should we even do this at all? Interdisciplinary thinkers excel at framing problems, identifying blind spots, and synthesizing solutions that span design, data, and empathy.
Education systems must catch up. That starts by breaking down subject silos and encouraging students to tackle real-world problems that blend ethics, science, and social impact. It also means teaching data literacy alongside critical theory and pairing logic with aesthetics, so students learn to analyze like scientists and imagine like artists.
We need students who are more like Swiss Army knives—and less like single-use screwdrivers.
4. AGI, Work, and the Post-Scarcity Paradox
“The development of AGI may be the most profound event in human history because it redefines what work, purpose, and creativity even mean.”
— Demis Hassabis, DeepMind
“In the future, the question isn’t ‘what job will you have?’ It’s ‘what problem will you choose to solve?’”
— Tim Urban, What’s Our Problem?
AGI isn’t just a tech upgrade—it’s a civilizational inflection point.
Wait But Why’s deep-dive into the AI timeline illustrates it best: first we build “narrow” AI that’s better than humans at specific tasks, then we build AGI that’s as smart as a human across all domains, and finally we (maybe) get to ASI—Artificial Super Intelligence—that’s smarter than all of us combined.
This isn’t just a tech arms race. It’s a rethink-everything moment.
Dr. Robert Miles, in a YouTube video for Computerphile, describes AGI as the ability to perform any intellectual task a human can do—with the kicker being that it can improve itself. Once that happens, things may escalate quickly. Really quickly.
This is the “intelligence explosion” Tim Urban warns about, and what Ray Kurzweil famously calls the “technological singularity”: a moment when AI surpasses human intelligence so dramatically that our societal trajectory becomes unpredictable.
One likely outcome? A post-scarcity society.
Demis Hassabis has envisioned a future where AGI eliminates repetitive labor, unlocking human potential for creativity, care, and exploration. But abundance doesn’t guarantee access. If education doesn’t evolve to meet this future, inequality will widen, not shrink.
So let’s zoom way out: when AI does the routine, humans must do the remarkable.
5. Teaching With AI, Not About It: Practical Collaboration
Teaching with AI means rethinking roles. It’s not about letting students outsource essays—it’s about helping them use AI to generate ideas, challenge assumptions, or simulate different perspectives.
At CSUN, we helped professors upload syllabi and readings into custom GPTs that served as on-demand tutors. The result? Students got more face time with instructors, while bots handled the boring stuff.
Meanwhile, VR enabled immersive simulations—nursing students practicing triage, environmental science majors exploring coral reefs from within. These are not toys. They are empathy machines.
They make the abstract real, the theoretical tangible, and most importantly—they make learning emotional.
Why emotion matters:
Research shows that emotional arousal enhances memory encoding and consolidation (Tyng et al., 2017). Stress hormones like cortisol and adrenaline released during high-engagement events make memories more vivid and durable (McGaugh, 2018). Mood congruence and state-dependent retrieval also affect what we remember and when.
Educators who create emotionally resonant, engaging learning environments aren’t just “motivating” students—they’re building stronger neural pathways.
6. The Role of Faculty: From Expert to Co-Learner
To teach polymaths, we must become polymaths.
Faculty need space to experiment, collaborate, and yes—fail. Think of it like neural plasticity, but for school culture. Research shows that interdisciplinary collaboration leads to better learning outcomes and improved equity (Darling-Hammond et al., 2017).
Imagine a professional learning community where an engineering prof, a poet, and a game designer co-create a VR module on climate change.
Faculty as jazz ensemble, not solo act.
And yes, some ideas will flop. But as any kindergartner or scientist will tell you—flops are fertile ground.
7. Mind the Gaps: Access, Ethics, and Equity
Let’s not ignore the boring but essential stuff.
Not every student has a device. Not every teacher has the time or bandwidth to experiment. And not every AI model is free from bias.
That’s why education must:
Prioritize affordable, open tools
Build digital literacy into core curricula
Teach students to question outputs, not just consume them
Ethics isn’t extra credit. It’s the core assignment.
8. Conclusion: Design the Future, Don’t Just Predict It
The future isn’t a destination. It’s something we’re building, decision by decision.
We can choose passive consumption or creative agency. Dependency or synergy.
To get the latter, we must:
Embrace polymathy
Leverage AI and XR for equity
Prepare students not just for jobs, but for purpose
We stand at the edge of abundance. But the view only matters if we teach the next generation how to build bridges—not just take selfies at the edge.
Let’s design a world where machines handle the routine, and humans handle the remarkable. Because that’s the real edge we stand on.
