We've all heard the saying, "the best way to learn is by doing." But let's be honest—sometimes that advice falls short. How many of us have spent hours staring at a blank screen, paralyzed by the fear of getting it wrong? Or worse, spent days wrestling with a problem only to realize we were solving the wrong one all along? The truth is, real learning doesn’t always come from repetition or rigid structure. It often emerges in the messy, unpredictable moments when curiosity takes the wheel. That’s the real magic—when passion ignites a path forward, even when no map exists.
What truly sparks transformation? It begins with identifying what genuinely excites you—those quiet, persistent questions that nag at the back of your mind. Is it the rhythm of a machine learning model? The story hidden in a shard of pottery? The emotional arc of a symphony coded in Python? Whatever it is, nurture it. Then, surround yourself with others who feel the same pull. Shared curiosity is contagious. When minds aligned by wonder come together, they don’t just solve problems—they redefine them. And don’t be afraid to break the rules. The most groundbreaking ideas rarely emerge from following the playbook. They bloom in the margins, in the experiments that don’t fit the rubric.
This is precisely what unfolded during my time at MIT in their course on "Intelligent Systems." At first, it sounded like another academic routine—lectures, readings, lab assignments. But from the very first week, it turned into something far more alive. Instead of diving straight into code, we started by sculpting clay models of ancient Mongolian energy systems—yes, actual clay, not digital renderings. We spent entire afternoons shaping forms that represented wind patterns, solar flow, and spiritual energy. It wasn’t just art; it was a tactile meditation on systems thinking. The goal? To build a robot—yes, a robot—that could “dance” to live jazz improvisation created right there in the studio. And somehow, we made it happen.
But the real revelation came not from the robot’s movements, but from the connections we began to see. As we progressed, we discovered that the same principles governing the flow of energy in a Mongolian yurt applied to the architecture of neural networks. That the patterns in ancient ceramic glazes mirrored the statistical distributions of voting behavior in digital democracy systems. That the way a community decides what counts as “clean energy” echoes the way an algorithm determines what data is worth prioritizing. These weren’t coincidences—they were threads connecting disciplines that most people assume are unrelated. Suddenly, history wasn’t just dates and empires. It was a dataset. Art wasn’t just aesthetic—it was a form of data encoding. And science wasn’t cold logic—it was a narrative waiting to be told.
In a world where technology is increasingly intertwined with academia, I'm often asked if AI can replace human interaction in the learning process. The answer isn't that straightforward - and it's not necessarily black-and-white.
1 Debunking the notion of replacement
2
The truth is, we need both: humans and machines working together to co-create knowledge. By leveraging machine learning algorithms for data analysis, we can free ourselves from tedious tasks, allowing us to focus on higher-level thinking - but not at the expense of human interaction.
A recent study found that students who used AI tools in their academic work reported a 25% increase in overall satisfaction with their studies. The algorithm was able to provide personalized feedback and help identify knowledge gaps, allowing them to dive deeper into topics they struggled with.
3
But what really matters isn't the tool itself - it's how we use these technologies together.
For instance, when I taught a class on human-computer interaction in computer science, students were initially hesitant about working with AI-powered chatbots. But as they delved deeper into their projects, they realized that these machines weren't just tools - they could be partners. By incorporating real-world data and user feedback, the bots became more empathetic and better able to understand human needs.
This collaboration, when done right, can lead to some amazing breakthroughs. Like the time I was working on a project with my colleagues from anthropology and history departments.(Yes, we were that geeky). We used AI to analyze ancient texts and identify patterns in trade routes - but what really amazed us was when the algorithm revealed an overlooked manuscript hidden away for centuries. It turned out this lost text had been written by a female scholar who had made significant contributions to our field.
The discovery, while incredible, also raised questions about representation and bias in historical records.
1 Decoding the past with interdisciplinary curiosity
2
I've seen firsthand how machine learning can help us uncover hidden stories - but it's only when we approach these technologies with a critical eye that they truly become powerful tools for social change. In my experience, collaboration between disciplines isn't just about working together; it's also about questioning our own assumptions and biases.
A few years ago, I co-taught a course on machine learning for social science researchers with an expert in cultural anthropology. We used AI to analyze climate change models - but what really struck me was how much we could learn from each other's perspectives.
The process of collaboration wasn't always easy, but it led to some remarkable discoveries.
One class session remains etched in my memory: a room full of 80+ students, from computer science to anthropology, locked in a passionate debate about whether machine learning could be used to detect bias in historical records. Another week, we used AI to analyze the chemical composition of real ceramic shards, tracing trade routes across Central Asia and uncovering forgotten recipes for glaze that had been lost for centuries.
This kind of interdisciplinary approach, when combined with a willingness to question our own assumptions - can lead us down some truly unexpected paths. For instance,
A student once told me they were working on a project analyzing the impact of social media on rural communities, using machine learning algorithms to identify patterns in online discourse.
As they delved deeper into their research, I realized that I had no idea about how these platforms truly affected people outside of urban areas - and this student taught me so much.
4
In the end, what matters most isn't whether AI can replace human interaction in academia; it's how we use technology to amplify our own abilities - and co-create knowledge with others.
The power lies not just in machines or algorithms but in our willingness to learn from each other. And that's something no one can replicate
with a single line of code.
And it wasn’t just about the projects. It was about the culture—the unspoken rule that it’s okay to be wrong, as long as you’re brave enough to try. The summer course I took, where PhD students became pottery detectives, was a perfect example. Armed with microscopes, spectrometers, and algorithms trained on centuries of ceramic data, we examined actual archaeological fragments. We weren’t just identifying origins—we were reconstructing ancient trade networks, decoding cultural exchange, even uncovering lost recipes for pigments. It was like *Indiana Jones* if he had a PhD in archaeometry and a love for Python. And the thrill wasn’t just in the discovery—it was in the realization that history isn’t static. It’s alive, and we’re the ones holding the tools to listen to it.
Then there’s the Music Technology and Computation program—where robots don’t just calculate, they compose. Students spend weeks building AI systems that generate music based on emotional states, heartbeat rhythms, or even the weather. One project involved training a neural net on a century of opera scores, then using it to create a new aria in the style of Verdi, complete with dramatic pauses and tragic crescendos. Another student designed a wearable device that translates brainwave patterns into real-time musical improvisations. This isn’t just about technology—it’s about empathy, expression, and the deep human need to be heard. When Beethoven meets machine learning, something beautiful happens: the machine doesn’t replace the artist. It becomes a collaborator.
Even the most human elements are reimagined. In Mongolia, MIT engineers and anthropologists don’t just install solar panels—they sit with nomadic families around campfires, asking what “clean energy” really means to them. Is it electricity? Yes. But also the ability to read at night without fear of a candle’s flame. The time saved from not gathering firewood. The warmth that comes with stability. This isn’t top-down innovation—it’s co-creation. It’s design rooted in dignity, not efficiency. And the results? Solar systems that don’t just function—they matter. They’re not just data points on a graph. They’re stories told in sunlight, in silence, in the shared laughter of a family watching a movie for the first time.
Democracy, too, gets a creative reboot. The Strengthening Democracy Initiative treats elections not as fixed systems, but as dynamic ecosystems. By analyzing real-time sentiment data, voting patterns, and civic engagement metrics, researchers build digital models that simulate how changes in policy or platform design affect public trust. It’s like giving democracy a diagnostic tool—identifying weaknesses, testing interventions, and even simulating new forms of participation. One project even used augmented reality to let citizens experience how gerrymandering distorts representation. It’s not about replacing democracy—it’s about deepening it, making it more transparent, more responsive, more human.
What makes MIT’s approach so powerful isn’t just the brilliance of its faculty or the sophistication of its labs. It’s the spirit of play. There’s joy in the collision of ideas: a chemist explaining molecular resonance through poetry, a historian teaching coding by visualizing the Silk Road as an animated network, a robot learning to improvise jazz with a human musician. These aren’t just interdisciplinary—they’re inter-joyful. The Institute doesn’t just prepare students for careers. It hands them a kaleidoscope and says, “Now go make something beautiful.” And in doing so, it reminds us that education is not a straight line. It’s a spiral—ever-expanding, ever-surprising.
So whether you're decoding ancient ceramics, teaching machines to feel music, or reimagining democracy with data and dreams, MIT’s programs don’t just teach you how to think. They teach you how to wonder. In a world that often feels too serious, too divided, too tired, that kind of spark isn’t just valuable—it’s revolutionary. It’s education as adventure, as resistance, as possibility. And if there’s one lesson I’ve learned from my time there, it’s this: the most important thing you can do with your mind isn’t to master a subject. It’s to stay curious. To keep asking, “What if?” To believe that even the quietest clay vessel, when held with wonder, might just whisper the future.
