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EcoRewards Project Story

Inspiration

The inspiration for EcoRewards came from a clear crisis in climate education. We noticed that current climate education is often too generic and lacks local relevance, leaving many people without a clear understanding of how climate change affects their specific city. Furthermore, there is often no motivation or reward system in place to encourage learning or behavior change. With only 53% of national education curricula worldwide mentioning climate change, and fewer than 40% of teachers feeling confident teaching it, we saw an urgent need for a better solution.

What it does

EcoRewards is an AI-powered climate education platform that gamifies the learning experience.

• Personalized Learning: It builds a personalized climate learning path where AI adapts content to the user's region and lifestyle.
• Daily Engagement: Users complete daily climate quizzes to keep up streaks and earn points.
• Immediate Insight: The platform provides AI feedback after each question along with actionable tips. For example, it might teach a user in Linz that local trams are electric and a better choice than diesel buses.
• Real Rewards: Users earn points for their efforts, which can be exchanged for real-world discounts, such as bus tickets.

Disclaimer: Prototype Status

Please note that this is an early prototype serving as a proof of concept to demonstrate the core idea. Currently, all users with an account can edit the quizzes, and these changes will affect everyone immediately. If you want to restore the default quizzes, they are attached to this Google Drive. We designed it this way so you can personally try out the ease of creating and editing quizzes, utilizing our AI bot for assistance. Real-world integrations with local partners for actual rewards and dedicated admin-level accounts will be implemented in later versions.

How we built it

We built EcoRewards using a modern, scalable tech stack:

• Frontend: The client layer is a React SPA built with TypeScript and Vite.
• State Management: We utilized TanStack Query for server state and localStorage for guest progress.
• Backend: The Lovable Cloud Backend uses Edge Functions to handle core logic like generate-quiz-questions and quiz-feedback.
• Database & Auth: User profiles and data are secured using Supabase Auth and a PostgreSQL database.
• AI: The core intelligence is powered by Gemini 2.5 Flash via the Lovable AI Gateway.

Challenges we ran into

A major challenge was moving beyond generic data to provide true local relevance. We needed to ensure the platform could accurately reflect local realities—like identifying specific public transport options in a user's city—to overcome the gap where users don't understand how climate issues affect their location.

Accomplishments that we're proud of

We are proud of creating a system that successfully raises awareness of local climate issues while encouraging sustainable habits through rewards. Key accomplishments include:

• Implementation of the Adventure path learning system.
• A functional reward marketplace linked to eco-friendly partners.
• An AI quiz generator that creates new questions for endless practice.

What we learned

We learned that to drive action, education must be both rewarding and relevant. By combining "Learn, Earn, Take action" into a single loop, we can motivate users who might otherwise lack the incentive to change their behavior.

What's next for Eco Rewards

Our immediate plan is to scale the platform to all regions and age groups, with a future vision of integrating into schools and forming city partnerships.

Beyond this, we see immense potential for expanding the social and real-time aspects of the platform, including:

• Game evenings featuring group quizzes.
• A news evaluator to help users discern climate facts from diverse sources.
• A live fact checker to support climate discussions with accurate data.

Built With

• gemini
• lovable
• postgresql
• react
• superbase
• typescript
• vite