Editorial 2 : AI by doing
Introduction: History shows us that true learning happens by doing, not just reading. Whether it’s mastering a craft or reaching enlightenment, hands-on engagement builds both skills and understanding. The same holds true for AI.
- The Principle of Learning by Doing:
- Rooted in cognitive science and championed by John Dewey as experiential learning.
- Focuses on applying concepts to internalise and evolve thinking.
- The Role of AI Maker Labs:
- AI maker labs are the modern equivalent of computer labs for the AI era.
- Enable students to interact with AI tools, build models, and experiment hands-on.
- Transforms abstract AI concepts into tangible experiences.
- Benefits of AI Maker Labs in K-12 Education:
- Teach how AI works in practice.
- Foster curiosity, problem-solving, and creative thinking.
- Prepare students to shape AI applications in the future.
- A case-study: Environmental Science Project:
- Traditional approach: Explain recyclable vs. non-recyclable materials in a few hours.
- Maker lab approach:
- Students train an AI model to classify objects for recycling.
- Build a working conveyor belt with object recognition and sorting.
- Learn about AI accuracy, bias, ethics, and real-world applications.
- Outcome: Students engage in deeper inquiry:
- Why isn’t an object recyclable?
- Can AI improve national recycling systems?
- Can we create universally recyclable materials?
- Key Takeaways from the Maker Lab Experience:
- Shift from passive learning to active problem-solving.
- Makes AI tangible — exposing its mechanics, strengths, and flaws.
- Prepares students for AI-integrated careers through hands-on familiarity.
- India’s Existing Infrastructure: Atal Tinkering Labs (ATLs):
- Over 10,000 ATLs operational; thousands more planned under the Atal Innovation Mission (AIM).
- Solid foundation to introduce AI literacy through making.
- Current Challenges:
- Many labs face issues:
- Unused equipment, under-trained facilitators.
- Difficulty in nurturing a culture of experimentation.
- A true makerspace success requires:
- Long-term mentorship.
- Strong teacher support.
- Ongoing capacity building.
- What is needed beyond Hardware:
- Go beyond top-down hardware deployment or one-off training.
- Invest in:
- Teacher capacity.
- School leadership.
- Local ecosystems that support inquiry-based learning.
- Focus on Equity:
- Must reach beyond elite private schools.
- 60% of current and all 50,000 newly announced ATLs are in government schools.
- Requires:
- Partnerships with state governments.
- Teacher training institutes.
- Community organisations.
- The Big Opportunity:
- AI maker labs can make AI a lived experience for young Indians.
- Create a generation that:
- Questions AI.
- Builds with AI.
- Applies AI to solve local and national challenges.
Conclusion: Progress won’t happen overnight, it will require steady effort, funding, and trial-and-error. But the payoff, equipping every child to engage with and drive the technology of the future, is undeniably worth it.