The Futures We Teach For

The Futures We Teach For

How AI is creating an opportunity to redesign primary education in India

A few months ago, I was stuck at a signal in Lower Parel. The traffic wasn't moving.

Around me were hundreds of people trying to get somewhere, either to offices, meetings, appointments, or deadlines. Basically, The usual rhythm of a city that is constantly in motion.

Then my attention shifted to two children playing on the divider. One had fashioned a toy out of a discarded milk packet. A string had been attached to it, transforming it into something between a kite and a pull toy. The other had attached makeshift wheels to an empty Alphonso mango carton and was racing it along the pavement.

Neither toy had been purchased.

Neither had been designed for play.

Yet both had been transformed through imagination.

What struck me wasn't the ingenuity itself. It was the contrast!

While hundreds of adults sat frustrated in traffic, these two children seemed entirely absorbed in the moment. They were experimenting, prototyping, testing ideas, negotiating rules, solving problems, and creating value from whatever resources happened to be available around them.

In many ways, they were doing what designers do. They were making sense of their world. That moment stayed with me because it raised an uncomfortable question:

How many forms of intelligence does our education system fail to recognise?

When we talk about the future of education, we often talk about technology. Today, that conversation is increasingly centred around Artificial Intelligence. But perhaps the more important question is not how AI will change education.

Perhaps the more important question is whether AI gives us an opportunity to rethink what we consider learning in the first place.

Different Realities, Different Futures

India has never had one educational experience. It has always had many.

A child attending an elite private school in Bengaluru experiences a fundamentally different reality from a child attending a government school in rural Maharashtra.

A child growing up in a fishing community in Kerala may learn through participation, observation, and community knowledge systems. A child in Mumbai may learn through digital platforms, structured extracurricular activities, and formal coaching environments.

Both are learning.

Yet educational systems often struggle to recognise the legitimacy of different forms of knowledge and intelligence.

This tension is reflected in how India itself is often perceived.

Films such as Slumdog Millionaire and The Best Exotic Marigold Hotel present two very different versions of India. One focuses on scarcity and survival. The other celebrates resilience, warmth, and improvisation.

Both narratives contain fragments of truth. Neither fully captures the complexity of the country. Education suffers from a similar problem. We often reduce learning to measurable outcomes while overlooking the diverse ways intelligence, creativity, and capability emerge across different contexts.

The children at the signal may never appear in conversations about innovation.

Yet what I witnessed that afternoon was resourcefulness, experimentation, systems thinking, and creativity in action. Qualities we routinely claim to value in the future workforce.

The question is whether our educational systems know how to recognise them.

Looking Beyond Outcomes

Educational debates frequently focus on outcomes.

Board examination scores.

Literacy rates.

Enrollment numbers.

University admissions.

Employment statistics.

These measures matter.

But they can sometimes distract us from the systems that produce them.

Across India, many government primary schools continue to face persistent challenges. Teacher shortages remain widespread across several states. Multi-grade classrooms are common. Infrastructure quality varies significantly. Teachers often balance teaching responsibilities alongside administrative workloads and reporting requirements.

These are not new problems. Nor are they purely operational.

Many reflect deeper structural assumptions about how education should be organised, delivered, and assessed.

Before discussing AI, it is worth acknowledging that technology alone will not solve these challenges.

A school without teachers cannot be fixed by software. A classroom without basic infrastructure cannot be transformed by algorithms.

The future of education cannot be downloaded. It must be designed.

An Inherited System

Many of the structures underpinning modern education were created during an era that prioritised standardisation.

Uniform curricula.

Age-based progression.

Centralised assessments.

Fixed pathways.

In colonial India, education was also shaped by administrative objectives—creating consistency across a vast territory and producing a workforce capable of serving bureaucratic systems.

While much has changed since then, many of these assumptions remain embedded within educational institutions today.

The result is a system that often assumes learning should happen at the same pace, in the same way, and lead toward similar outcomes.

But India's realities are far more diverse. This is where conversations around decolonisation become relevant.

Decolonising education is not simply about revising textbooks.

It is about questioning whose knowledge is valued, whose aspirations are prioritised, and whose futures educational systems are designed to serve. It is about recognising that there may be multiple pathways to success rather than a single idealised route.

Why AI Changes the Conversation

Much of the public discourse around AI focuses on implementation.

Can AI support teachers?

Can it personalise learning?

Can it improve outcomes?

These are useful questions.

But they are not the most interesting ones.

The more interesting question is what assumptions become obsolete when intelligence becomes abundant. If AI can instantly translate content across languages, what does that mean for linguistic accessibility?

If personalised tutoring becomes available at scale, what happens to one-size-fits-all approaches to learning? If information becomes increasingly accessible, what skills become most valuable?

Critical thinking?

Curiosity?

Judgement?

Collaboration?

Creativity?

The significance of AI may not lie in its ability to automate educational tasks. Its significance may lie in forcing us to rethink why educational systems were designed the way they were in the first place.

Research from institutions such as Stanford University and the University of Oxford increasingly points towards AI's potential to augment learning experiences, support personalised instruction, and reduce barriers to access. Equally important, however, are the questions these technologies raise about equity, governance, and the future role of educators.

In this sense, AI is less a solution and more a catalyst.

The Needs of Today and the Needs of Tomorrow

Design futures work often distinguishes between present needs and emerging needs. Today's needs are relatively clear. We need more teachers and better infrastructure. Improved foundational literacy and numeracy, more equitable access to educational resources, and greater support for underserved communities.

These needs remain urgent.

But tomorrow's needs are beginning to emerge.

How do children learn alongside intelligent systems?

How do we teach discernment in an age of information abundance?

How do we ensure technology strengthens cultural and linguistic diversity rather than eroding it?

How do we prepare children for futures that do not yet exist?

These are not technology challenges. They are design challenges.

Why Design Research Matters

One of the biggest risks in educational transformation is assuming that India represents a single context.

It does not. India contains thousands of educational realities.

A government school in Gadchiroli.

A municipal school in Mumbai.

A residential school in the Northeast.

A village school in Rajasthan.

Each operates within a different social, cultural, economic, and environmental context. Design research helps us understand these realities beyond statistics.

It helps us understand how children learn outside classrooms.

How parents define success.

How teachers navigate constraints.

How communities imagine futures for the next generation.

Without this understanding, there is a risk that AI simply reinforces existing inequalities rather than helping address them.

From Research to Policy Design

Research helps us understand what is happening. Policy design helps us decide what to do about it. For decades, educational policy has largely relied on standardisation and scale. Yet the future may require something different.

It may require systems that balance national goals with local realities. Systems that create consistency without enforcing uniformity.

Systems that allow for adaptation rather than assuming every context requires the same intervention.

This is where policy design becomes essential.

The challenge is not designing smarter tools.

The challenge is designing smarter systems.

The Futures We Teach For

Perhaps the most important question is not how children learn. It is what futures they are learning for.

For decades, educational success has often been framed around a familiar pathway:

School.

University.

City.

Job.

But the futures emerging around us may not be so linear.

Remote work, distributed economies, local entrepreneurship, climate adaptation, community-led enterprises, and AI-enabled industries are already reshaping how opportunity is created and distributed.

The career pathways available to a child in a village should not simply be simplified versions of urban career pathways.

They should reflect local strengths, emerging opportunities, regional economies, environmental realities, and diverse aspirations. This conversation extends beyond primary education and deserves deeper exploration.

Yet it begins here.

The assumptions embedded into primary education influence what children believe is possible long before they begin making career decisions.

Looking Forward

The future of education in India will not be determined by AI alone. Nor will it be solved through technology.

The deeper opportunity lies in using this moment to examine the assumptions underpinning the system itself.

AI may prove valuable not because it teaches children faster. But because it encourages us to ask better questions.

Questions about equity.

Questions about relevance.

Questions about local knowledge.

Questions about multiple forms of intelligence.

Questions about the futures we imagine for children.

The future of education may not be about teaching children to work alongside AI. It may be about using AI as a catalyst to redesign the futures we teach for.

References

Bill & Melinda Gates Foundation (2025) Education Systems Partnership and AI-Enabled Learning Initiatives. Available at: https://www.gatesfoundation.org (Accessed: 9 June 2026).

Manzini, E. (2015) Design, When Everybody Designs. Cambridge, MA: MIT Press.

Pritchett, L. (2013) The Rebirth of Education: Schooling Ain't Learning. Washington DC: Center for Global Development.

Stanford University Human-Centered Artificial Intelligence (2025) AI and Education Research Overview. Stanford University.

Stickdorn, M., Hormess, M., Lawrence, A. and Schneider, J. (2018) This Is Service Design Doing. Sebastopol: O'Reilly Media.

Times of India (2025) Teacher vacancies in government schools continue to impact learning outcomes. Available at: https://timesofindia.indiatimes.com (Accessed: 9 June 2026).

University of Oxford (2025) Artificial Intelligence, Learning Systems and Educational Futures. Oxford Internet Institute.