Learning Beyond Data

Finland’s national core curriculum names seven transversal competencies that every student must develop. The first one is not literacy or numeracy. It is “thinking and learning to learn.” Everything else — multi-literacy, managing daily life, ICT, contributing to community — comes after that foundational capacity is in place.

Kulkarni described the moment this was made visible to her: she was on a flight and met an English language teacher who said students now use AI at home and come to class with answers they cannot replicate or explain when asked to do so in the room. The fluency is borrowed. The understanding is not there. Finland’s answer is not to ban the tools but to build the third layer of assessment that most systems ignore: assessment as learning — self-assessment and peer assessment, where students develop the capacity to evaluate their own thinking.

On what makes a teacher passionate: “Teachers who have their own philosophy become passionate because they know the purpose of their teaching.” The national curriculum in Finland gives teachers autonomy precisely because the structure is clear enough that each teacher can fill it with their own meaning. Passion, she argued, cannot be mandated or measured. It comes from knowing yourself well enough that your qualities flow naturally into your teaching.

She also introduced her own addition to the Five Ps of creativity (Person, Process, Product, Place): Patience. “Our patience level is three seconds,” she noted. Everything about the digital environment is designed to eliminate waiting. Learning requires exactly the opposite: the willingness to sit with something long enough for it to become yours.

She closed her contribution with an activity she does in workshops: ask everyone to write “You wow me because…” on a piece of paper and pass it to a neighbour. “We rarely appreciate each other,” she said. Happiness hormones are created in that moment. She recommends it in schools, for adults, at home. The learning environment is not about infrastructure — it is about whether people feel safe enough to share their worries and their questions.

Gupta brought the largest institutional perspective in the room — 381 colleges, half a million students — and used none of it. Instead she ran two live experiments that the panel will not forget.

Her conclusion from both demonstrations: AI cannot change two things — guru and sahiti (classmates and companions in learning). The relationship between teacher and student, and the relationship between learners themselves, produce something no retrieval system can replicate.

Gupta grounded the purpose of education in the Taittiriya Upanishad’s three goals for shiksha: shika earth curry (it should generate livelihood), yash curry (it should bring glory), and suk curry (collective happiness — never individual alone). “Shiksha is not only to produce a renunciate (tyagi) but also someone who can stand in the world with confidence (vaibhav).” The teacher’s gift is the confidence a student carries when they leave campus.

On the state of teaching: “How do you make a good teacher when you don’t make teaching the last available job?” When you treat the teacher’s position as equivalent to any top professional, she said, passionate people will come. The problem is not the individuals. It is the status we have assigned to the vocation.

Ahuja cited an article from The Atlantic asking: where have all the geniuses gone? Einstein, Niels Bohr, Feynman — what they had in common was not a special school. They had “many on one” relationships — many teachers for one student. That relationship has been replaced by the church model, and AI is accelerating the replacement. “Guru-shishya parampara has been alive and well for the longest time. The world is getting into church mode and AI is accelerating that path.”

At SMU, experiential learning means students work with actual companies — not simulated problems. He brings Fortune 500 CEOs as guest speakers. The partnership between SMU, Dallas companies, and startups is a deliberate attempt to reconstruct the kind of encounter Montessori described: learning through joy, through play, through contact with real stakes.

He quoted an MIT Sloan Management Review arc he finds clarifying: software eats the world (Marc Andreessen, 2011) → AI eats the software → philosophy eats AI. Which brings us back, he said, to 5,000 years of our tradition. He also noted something consistent across every AI-in-healthcare conversation he has: “Nobody has ever said I wish I had better technology. Everyone says: better culture, better strategy, better people.”

His one rule in teaching: “Students don’t care how much you know until they know how much you care.” And on the question of money and motivation: he wanted to directly push back on a cultural habit. “Making money is not bad. I want to get rid of this notion.” Passion and prosperity are not opposites. Competence — the feeling of being genuinely capable — is what young people most want to feel. And that can only be built through engagement, not through being told the answer.

Metri opened with the single distinction that anchors everything: perpetual learners are going to lead the world. Lifelong learning was once optional. Now it is essential. The question is what kind of learning, and for what.

On competition versus cooperation, he offered Maruti Suzuki as a live case: 13 passenger car companies in India all purchase steel together from ArcelorMittal, achieving a 55% discount through cooperation. Then they compete fiercely in the market. “Six out of ten highest-selling cars always belong to Maruti.” He connected this to dharma: principled cooperation as the foundation within which competition is healthy. The Japanese precedent — no natural resources, no domestic market, and yet the world’s dominant economy for 15 years (1980–1995) — was built entirely on people management and teamwork.

On assessment in the age of AI: “Assess for the prompt, not for the answer. AI gives the answer. You assess the intelligent question.” IIM Nagpur is also assessing courage, confidence, and tenacity — qualities that no automated system can grade but that every employer and every community needs. He closed by coining a distinction — borrowing a word from Neerja Gupta — between shloka (a pinpointed answer) and gaveshana (research, from the word go meaning cow — when a cow was lost, finding it was a huge, open-ended task): “Are you approaching AI for a shloka or for gaveshana? How many other sources are you reaching out to?”

Pandey wove a consistent thread through the session without foregrounding himself in it. His most direct statement of the central argument: “Analysis is being outsourced. Decision is the most important thing. The machine gives the analysis. We have to decide. And discernment and decision are deeply connected to values, which are reflected in character.”

He opened the session by asking everyone to feel their feet on the floor, take one conscious breath, and silently thank the people who have shaped their learning — before a single slide appeared. He shared that the Dalai Lama once gave him two words when asked for advice: “Breathe properly.” He said he still finds new meaning in those two words every day. Breathing connects concentration, the power to memorise, empathy, the meaning of life, and usefulness to others.