When the final presentations wrapped up in Bengaluru last month, some students were still tweaking slides minutes before walking on stage. Others were pacing the hallway, rehearsing explanations in plain language. They were not pitching apps just to win a prize. Most were trying to show that their ideas could actually work outside a classroom.
That was the point of a national generative AI hackathon that ran for nearly five months and drew students and developers from across India. By the time it ended, more than two lakh participants had tried their hand at solving real problems using AI tools, many for the first time.
Learning AI by building things that break
The event brought together college students, early-career developers, and working professionals. They worked on challenges linked to mental health, misinformation, local businesses, industry operations, and public services. Instead of abstract prompts, teams were given problem statements tied to how people live and work.
Some of those challenges came from state government departments. Others came from companies in travel, manufacturing, and technology. On paper, the task was simple. Build something useful. In practice, it rarely stayed that way.
For many students, this was their first exposure to applied AI. They were not just learning how to write prompts or generate text. They had to think about users, limits, data gaps, and what happens when an AI answer is wrong.
Several participants described it as uncomfortable learning. Ideas that sounded strong on day one often failed during testing. Teams had to adjust fast. Features were cut. Others were simplified. Mentors said that process mattered more than polish.
From classroom ideas to real-world problems
By the time the top teams reached the final round in Bengaluru, most projects had changed shape. One team built a conversational tool aimed at checking in on young people’s mental wellbeing. Another focused on helping local artisans create product listings without needing marketing experience.
Some teams worked on tools to simplify legal documents or flag manipulated media. Others went in quieter directions, like automating software testing or monitoring industrial operations. These projects did not look dramatic on screen, but judges said they reflected real needs companies deal with daily.
The format pushed students to think beyond marks or college deadlines. Timelines were tight. Feedback was direct. Mentors asked questions that were hard to answer. Who will use this? Why would they trust it? What happens when it fails?
At the same time, teams were allowed to fail early. Many prototypes never made it past the first review round. Even so, those students stayed involved, watching other demos and learning where their own thinking fell short.
What students took away from the hackathon
There was also a noticeable shift in how AI was discussed. Less talk about replacing jobs. More focus on support tools. Helping people read documents faster. Helping small businesses compete. Helping administrators spot problems sooner.
Faculty members said this tone made AI feel less distant, especially for students from non-technical backgrounds. Several teams included members from design, commerce, and humanities, not just computer science.
By the final day, the pressure showed. Presentations were short. Explanations had to be clear. Judges interrupted often. Some teams struggled mid-sentence. Others recovered. It felt closer to a real review meeting than a college event.
Ten teams were eventually recognized, but the larger impact was harder to measure in awards. Many students said they now understood what it means to build under constraints. Limited data. Real users. Ethical concerns. Time limits.
Also Read: Developers Use AI Every Day—but Most of Them Don’t Trust It
No one claimed the hackathon solved everything. AI tools still made mistakes. Some projects would need months of work to become usable. Organizers admitted that openly.
As the venue cleared out, a few teams were already talking about what they would fix next. Others were just relieved it was done. Either way, they left with more than a certificate. They left knowing that real-world problems rarely behave the way ideas do on paper.
Leave a Reply