How can an 11-year-old with no coding experience build an interactive wildlife discovery website in just 10 days? According to RocketHour CEO and co-founder John Naiker, the answer lies in taking the right approach to AI in education — as shown by the company’s recent CodeQuest programme.
Rethinking How Kids Learn With AI
“Today’s AI tools offer unprecedented creative potential, but they also risk turning a generation into passive consumers,” says Naiker. “This is the context in which RocketHour designed CodeQuest as an educational intervention.”
Instead of teaching theory first, students dived straight into problem-solving, using tools like VS Code, GitHub Copilot and Netlify. AI became a learning partner — not a shortcut.
“We showed them what was possible and how to explore using the tools, but we didn’t tell them what or how to do their projects,” explains Holly Armstrong, a RocketHour tutor.
Because AI gives instant feedback, students saw results quickly — which kept them engaged and taught resilience. Group collaboration also created peer-to-peer teaching moments, where beginners and more advanced learners learned from each other.
Building Skills That Outlast Technology
RocketHour’s tutors taught students how to communicate clearly, evaluate critically, and direct AI creatively — skills that remain relevant regardless of how technology evolves.
“The most effective way to teach creative thinking is by building and making,” Naiker says. “CodeQuest completely transformed what students were able to achieve.”
RocketHour’s Three Key Discoveries
1. Age and Ability Assumptions Are Wrong
“The learning gap wasn’t in the children’s ability — it was in the tutors’ expectations,” says Naiker.
Students produced polished, interactive websites with JavaScript, CSS animations, and HTML structure. Even without grasping every concept, they demonstrated creativity, focus and problem-solving that exceeded expectations.
2. Intentional Design Prevents Over-Reliance on AI
Some learners started by asking AI to “build me a website” and expecting perfect results. Tutors taught them to break problems down and give precise prompts.
“When the AI’s output didn’t match their vision, we’d ask: ‘What’s different from what you wanted? How can you adjust your request?’” Naiker explains.
Through a plan–prompt–evaluate–refine cycle, students learned to direct AI with intention rather than depend on it blindly.
3. Foundations Matter More Than Age
Naiker notes that success depended less on age and more on family technology habits and critical thinking skills.
“Kids from families where technology was used for creating, not just consuming, had a huge advantage,” he says. “The students who thrived weren’t the most tech-savvy — they were the ones who naturally asked ‘why?’ and ‘what if?’”
By focusing on curiosity and problem ownership, tutors helped most students find the spark to persist.
Lessons for the Future of Learning
Naiker says the results show that implementation matters as much as policy.
“Kids who learn to collaborate effectively with AI today will have a huge head start in almost any future career,” he says. “This approach doesn’t need expensive tech or a full curriculum overhaul — it needs trust. Trust in young people’s ability to learn by building and in educators’ willingness to guide, not dictate.”
He adds, “Students may sometimes teach us more than we teach them.”
