E-Paper
AI for social good: How young minds can shape the next decade
This article is authored by Rahul Ramachandran, director, Partnerships and Initiatives, KRUU.
The phrase AI for social good sounds reassuring. It suggests intention. Responsibility. Progress. But it has also become increasingly vague.
For decades, social good meant addressing visible deficits. Vaccinations. Poverty. Literacy. Access to basic services. These challenges remain urgent and unresolved. At the same time, a different category of problems has emerged. Media bias. Information warfare. Algorithmic manipulation. Polarisation. Erosion of trust.
First-world and third-world problems have never been further apart in form. Yet they coexist in the same global system, often amplifying one another. This is where Artificial Intelligence (AI) changes the equation.
AI does not distinguish between these categories. It applies computational power at the same pace to all of them. Whether the problem is vaccine distribution or misinformation spread, AI accelerates analysis, modelling, and solution-building equally.
In that sense, AI is a true equaliser. Not because it is inherently fair, but because speed is now universally available. What determines outcomes is no longer capability alone. It is direction.
Most conversations about AI in education still begin with tools. Personalised learning platforms. Automated grading. Smarter administration. These applications have value. They reduce friction. They make systems run more smoothly. But they also reveal a deeper issue. AI is often used to optimise an educational model that most people agree no longer fits the world students are entering.
Schools are still organised around a fixed curriculum, standardised assessments, and predictable outcomes. AI, when introduced without questioning these foundations, simply makes an outdated system more efficient.
That is not a transformation. That is maintenance.
For me, working closely with students, educators, and institutions across contexts, it becomes clear that AI’s real significance is not what it can do faster. It is what forces humans to reconsider doing at all.
Projects like Our Journey to a Sustainable School, students studied energy consumption within their own campuses. They identified inefficiencies, implemented practical solutions such as LED lighting and smart thermostats, and launched awareness campaigns. The outcome was measurable. Energy use dropped by 25%.
This is often where the conversation stops. Now add AI to that equation.
AI can analyse energy-use patterns humans cannot easily see. It can optimise lighting, heating, and cooling based on occupancy, weather, and usage trends. Buildings become responsive systems rather than static structures. All of that is true. And also incomplete.
Because this is where we need to pause and ask a harder question. If AI can already solve problems that are obvious to us, what exactly should humans be doing?
Too often, AI is introduced as a way to plug into problems we already understand. Energy inefficiency exists. Air pollution exists. Administrative overload exists. We point AI at the problem, wait for optimisation, and call it progress. That is not ambition. That is convenience.
If an application can be built in seconds with a prompt, the human role cannot remain limited to execution. The human role must shift upstream. From solving problems to imagining better ones. From optimising systems to questioning whether those systems deserve to exist in their current form at all.
Every major leap in progress has come from challenging constraints, not working faster within them. The move from IC engines to electric engines is transforming transportation. The invention of the steam engine transformed society.
AI is closer to the steam engine moment than an engine upgrade.
If AI now carries much of the cognitive load, then education must train young people to push not their own limits, but AI’s limits. To question its assumptions. To stretch its outputs. To test where it breaks, biases, or simplifies reality too neatly.
Using AI only within the boundaries of human practicality is lazy thinking. It risks making us more dependent, more passive, and less imaginative. The opportunity is not to make students better at using tools. It is to help them redesign the ecosystem in which those tools operate. That shift, from optimisation to imagination, is where meaningful innovation begins.
This is why the definition of social good itself needs updating.
Social good is no longer only about addressing shortages. It is about collapsing timelines. Problems that once took generations to solve no longer have to wait for generational change.
AI gives us the ability to model faster, build faster, test faster, and iterate faster. Delay is no longer neutral. When solutions can be explored instantly, choosing incrementalism becomes a decision, not a limitation.
The danger is not that AI will move too quickly. The danger is that we will use it timidly. For convenience instead of courage.
AI can be used to make us more comfortable, or it can be used to push us toward solutions we once thought were unrealistic. The difference lies in the intent of the people guiding it.
This intent is exactly is being discussed at the India AI Summit, where innovators, policymakers, and educators are exploring how AI can be steered toward meaningful social impact.
Young people today are AI native. They do not need to be taught how to access intelligence. They need guidance on how to wield it responsibly and ambitiously.
Education’s role is no longer primarily knowledge transfer. Information is abundant. Intelligence is accessible. What remains scarce is judgment.
Learning environments that prioritise real-world problem solving, mentorship, ethical reasoning, and experimentation help students develop this judgment. They learn to work with uncertainty, challenge assumptions, and take responsibility for outcomes that extend beyond grades and exams.
This is not preparation for a specific career. It is preparation for agency in a world where intelligence is everywhere.
AI for social good is not about better tools. It is about better intent.
The next decade will not be defined by machines becoming intelligent. It will be defined by whether human imagination keeps pace. AI gives us the ability to move faster than ever before. The question is whether we will use that speed to optimize convenience or to confront the problems we have long postponed.
Young people, guided well, can do more than adapt to this future. They can shape it. Education must lead that shift. Not by fitting AI into existing systems, but by redefining what learning is meant to unlock in the first place. That work cannot be automated.
This article is authored by Rahul Ramachandran, director, Partnerships and Initiatives, KRUU.
