The Challenge Addressed
This practice addresses the urgent challenge of helping secondary school students understand and respond to local climate impacts, such as sea level rise, heatwaves, droughts, biodiversity loss, and risks to agriculture and human health.
Young people often encounter climate change as an abstract, global problem, making it difficult for them to connect scientific data with their own environment. This initiative bridges that gap by combining authentic local challenges with advanced AI tools, enabling students to transform complex climate science into accessible, community-focused insights.
Why the Practice is Innovative
The practice is innovative because it integrates multiple AI tools—IPCC Atlas, ChatGPT (IPCC Climate Science Helper), Perplexity, NotebookLM, and Google AI Studio—to support inquiry-based learning.
Students use AI for data visualization, scientific explanation, hypothesis testing, and collaborative knowledge building. Instead of consuming information passively, they interact with dynamic datasets, test ideas, critique AI outputs for accuracy and bias, and develop responsible AI literacy.
This multimodal use of AI elevates traditional climate education by merging scientific inquiry, technological fluency, and creative communication.
Type of Integration and Key Competences
The practice promotes strong curricular integration by linking geography, biology, ICT, and languages within a single interdisciplinary project.
Students develop key competences such as data literacy, media literacy, AI literacy, scientific reasoning, critical thinking, creativity, problem-solving, communication, and civic responsibility.
Through group roles and collaborative tasks, they also strengthen teamwork, leadership, and organizational skills aligned with 21st-century learning objectives and UN SDG 4.
Role of the Teacher/Trainer
The teacher acts as a facilitator, mentor, and guide. The role includes scaffolding research questions, modelling how to evaluate AI outputs, helping students organize findings, supporting collaboration, and ensuring ethical and responsible use of technology.
Instead of delivering information, the teacher structures milestones, provides feedback, adapts materials for diverse learners, and creates a safe space for experimentation and reflection.
Type of Learning Activities
The learning activities are student-centered and inquiry-based. Students observe their local environment, collect preliminary data, and develop research questions.
Small groups analyze climate datasets using IPCC Atlas and AI-assisted tools, explore scenarios, and identify local vulnerabilities. They design realistic solutions such as coastal protection plans, community awareness campaigns, or proposals for climate-resilient agriculture.
Their findings are communicated creatively through posters, presentations, podcasts, and digital stories. NotebookLM enables interactive testing of hypotheses, while Google AI Studio supports collaborative visualization.
Impact of the Practice
The practice has strong impact: students gain a deep understanding of climate science, become active problem-solvers, and learn to communicate complex information clearly.
Their confidence, engagement, and sense of agency increase as they propose solutions for their own community. Teachers report improved collaboration, motivation, and critical thinking across student groups.
Potential for Replication
The model is highly replicable. Schools can adopt the framework with minimal resources by adapting themes to local climate issues, using freely available AI tools, and documenting methods and student outputs.
The approach can scale across subjects, regions, and international networks, empowering youth to lead meaningful climate action.
Innovator: Dajana Jelavić - Jure Kaštelan high school