Pioneering the integration of Artificial Intelligence — including Large Language Models and ML agents — into water emergency management and environmental education across Europe.
Ai-LEARN is a pioneering model for integrating advanced AI into water emergency management and environmental science education — deployed across three leading universities in the Netherlands, Spain, and Greece.
The project creates high-quality, internationally accessible training modules on flood forecasting, drought analysis, and water management, combining state-of-the-art machine learning with real-world EU satellite data and field case studies.
At its core, Ai-LEARN rethinks how education is delivered: AI-driven systems — including Large Language Models (LLMs) — guide learners through complex environmental models, making advanced methodologies accessible and pedagogically sound.
Four interconnected goals driving educational innovation in water and environmental science.
Embed state-of-the-art AI tools — Large Language Models and ML algorithms — into educational modules, creating adaptive, interactive learning environments that foster digital literacy.
Create modules using real-time remote sensing data and advanced environmental modelling. Provide hands-on experience simulating flood and drought scenarios for climate adaptation.
Build a scalable educational framework through active partnerships among TUC, UPV/EHU, and IHE Delft — merging digital innovation and environmental expertise across borders.
Conduct rigorous evaluation studies monitoring improvements in digital competencies and practical environmental problem-solving skills, with a target of 30% improvement in learning scores.
Three regional case studies grounding the educational content in real environmental challenges.
Groundwater data infilling and drought assessment using ANN models and geostatistical methods applied to aquifer data from Cretan islands.
Hydrological analysis integrating EU satellite data (MTG) with remote sensing techniques and ML models, focused on the Basque Country region.
Urban and rural flood forecasting using LSTM networks applied to data from the Amazon and Magdalena River Basins, with collaboration from the Magdalena River Basin Authority (Colombia).
Three complementary European universities with world-class expertise in water science, AI, and environmental education.
The world's premier postgraduate institute for water education, part of UNESCO. With 200+ staff, an alumni network in 179 countries, and leadership in hydroinformatics and eCampus digital learning.
One of Spain's largest universities with 57,000+ students. The Computational Intelligence Group (CIG) brings expertise in AI, remote sensing, hyperspectral imaging, and environmental data analytics.
Research-intensive university specialising in geostatistics, hydrogeology, and AI. Home to a Geostatistics and Hydrogeology Research Group and a pioneering MSc in Machine Learning and Data Science.
Five complementary work packages spanning the full project lifecycle from management to dissemination.
Overall coordination, financial oversight, risk management, and communication across all partners. Bi-annual General Meetings (one online + one in-person per year) and bimonthly WP operational meetings ensure continuous alignment. A dynamic risk register and standardised progress reporting keep all partners on track.
Development of the technical AI infrastructure: specialised TensorFlow/Keras models for flood forecasting (LSTM/ANN), groundwater data infilling, and drought assessment. An Educational LLM Integration Framework with prompting techniques and instructor guides enables AI-assisted learning. Three case studies — Greek islands, Spain hydrology, and Latin American watersheds — are fully implemented as structured, project-based learning modules.
Design and development of the WaterTech Learning Portal — an internationally accessible virtual learning environment building on IHE Delft's eCampus Moodle infrastructure. Integrates AI tools from WP2, supporting MSc-level education, short courses, and professional development. Designed with WCAG 2.1 AA accessibility standards and multilingual support (English, Spanish, Greek).
Comprehensive communication, dissemination, and exploitation strategy. Includes this public website, Open Educational Resources, public outreach events (minimum 15) in each partner country, policy briefings for regional environmental authorities, and engagement with environmental professionals and policymakers. MOOC development for global reach beyond the project lifecycle.
Systematic monitoring of educational impact, interdisciplinary collaboration, and research innovation. Pre/post knowledge assessments measure 30% learning improvement targets. User testing cycles at M8, M16, and M24 refine platform usability. Structured reports document outcomes at key milestones.
Key outputs and reporting dates across the 36-month project lifecycle.
Three interconnected digital environments developed during the project.
The main public-facing platform for Ai-LEARN — project information, partners, deliverables, and links to all tools and resources.
Interactive browser-based tools for remote environmental modelling — flood forecasting, groundwater infilling, and drought assessment using the project's ML models.
Advanced interactive teaching platform with visual learning cards, AI-powered gamified scenarios, and a live polling system for lectures and workshops.
This section will be updated progressively as the project produces outputs.
Ai-LEARN started in October 2025. Key results from the first reporting period will be published here as they become available. Expected first outputs include:
Are you involved in the project and have results to report? Get in touch to update this section.
Universidad del País Vasco / Euskal Herriko Unibertsitatea
Leioa, Basque Country, Spain