They are not finished products yet, neither their prototypes nor their journeys. As part of Samsung Solve for Tomorrow, challenges have not ceased for the Top 40 innovators. These young changemakers are still building, modifying, expanding, testing, and sometimes discarding ideas altogether. What they are discovering, however, may be as relevant and important as the innovations themselves: that design thinking is not just a toolkit but a mindset that demands empathy, patience, and an openness to failure.
Over the past week, participants were on a frenetic pursuit for perfection in ideas guided by mentors, workshops, and their first exposure to the FITT labs.
In an AI-driven world where speed and automation dominate the public discourse, these students are being reminded that the true test of technology is whether it can connect to the human heart and the human behaviour.
Sitting With the Problem
In this context, it will be pertinent to speak about the story of the Pink Brigadiers. A team comprising of Vivek Sawant from Maharashtra and Shriya Aditya Dalai from Odisha, both NIT Rourkela engineering students. What are they doing this year? They are working on what they call Bharat’s first AI-driven breast care app. At first glance, it’s a technical marvel: convolutional neural networks with edge deployment that can detect anomalies and connect women with doctors. But the breakthrough, they admit, has not been in the code.
“Our product requires immense sensitivity. The design thinking training encouraged us to sit with the problem longer, understand users more deeply, and keep adapting to their needs. UX/UI and trust are as important as the AI itself,” they explain.
For them, design thinking is a reminder that how an app makes someone feel may be as critical as what it does. Building technology for a deeply private health concern means that tone, colour palettes, language, and interface all become questions of empathy. This insight resonates with recent Stanford research showing that building fair and trustworthy AI systems requires attention not only to algorithms but also to transparency, edge-case behaviour, and user comfort.
Humanising AI
Elsewhere, inside the FITT lab there is a duo trying to grasp the lesson on AI from their product – How can AI provide intelligence, and how can design thinking make it intelligible.
Take Mindsnap, a personalised education platform created by Devayanee Gupta and Sayan Adhikary from Kolkata, both engineering students. Powered by large language models (LLMs), the platform adapts to neurodiverse learners, whether they are dyslexic, on the spectrum, or simply learn better through games.
“We realised no algorithm works if the interface doesn’t speak to the learner,” they explain. “Design thinking made us focus on UX/UI, accessibility, and the lived experience of students.”
Aditya Verma from Chennai is making a similar discovery with Mama Saheli AI, a holistic pregnancy app inspired by his mother’s experience in remote areas where medical access was limited.
“My app had to feel like a friend, not just a tool. Design thinking pushed me to see it through the user’s emotions, behaviour, and even cultural context. That’s what makes it scalable and trustworthy,” he says.
His app synthesises information, filters out misinformation, and integrates with wearables to provide hyperpersonalized insights, but its soul lies in the idea of companionship. His approach aligns with the PADTHAI-MM framework, which shows that transparent, human-centred design, combining explainability with user context, produces far more trust than opaque “black box” AI.
Design as a Strategy for Scale
The Prithvirakshak team from Ludhiana: 12th graders Abhishek Dhanda, Prabhkirat Singh, and Rachita Chandok are fighting India’s colossal waste management problem with what they call the nation’s first modular automated vermicomposting centre.
The idea began as a classroom experiment, it has now become a three-year journey of prototyping, testing, and learning how to collapse a 90-day composting process into just 30 days.
“Traditionally, vermicomposting has been labour-intensive and hard to scale,” they explain. “Design thinking helped us imagine modular models that can work in a garden, a housing society, or even at city level.”
For them, scalability is not about size but about adaptability, the ability to shape the same core idea to serve farmers, urban families, or municipalities.
The Journey, Not the Destination
None of these teams know if they will eventually win the Solve for Tomorrow challenge. Their prototypes remain imperfect; their pitch decks are still being rewritten. Yet what binds them together is a recognition that design thinking has already amended their approach.
While global conversations around AI often spiral into questions of ethics, bias, and speed, these young problem-solvers are grounding their innovations in something older and steadier: human-centred design.
AI, they are discovering, may be the brain. But design thinking, in all its humility and discipline, is the heart. And as these students continue to fight for their place in the Top 20, that may turn out to be the most important lesson of all.
