stAI - Act before it happens: Preventing and fighting Early Leaving from Education and Training with Artificial Intelligence

Founded by Erasmus+, this European project aims to equip educational institutions and private and public bodies in the field of Early Leaving from Education and Training (ELET) with strategies and concrete measures to help deal with by: (1) mapping the main risk factors that lead to ELET, thus recommending the most effective intervention strategies; (2) outlining a complete picture of what systems based on knowledge-driven AI are, and how they can support decision w.r.t. ELET in education; (3) improving decision support in education; and (4) advising policymakers to support the implementation of the education ecosystems at a local level.

Aim & Partners

Our ambition is to contribute to lowering the average percentage of early school leavers. We will start with the country-specific risk maps, the benefits of applying AI-powered tools to support decision-making in education, and boldly go beyond to design and develop a knowledge-based AI prompt-system prototype to automate the extraction of actionable insight on the local factors influencing ELET and identify early school leavers. We will demonstrate to policymakers, stakeholders, and teachers that this system can result in a responsible use of AI to identify school leavers.

Know more about us!

Partners

Haskoli Islands, Iceland

Vrije Universiteit Brussel, Belgium

CRES Centro di Ricerche e Studi Europei – future business, Italy

Scuolattiva Cooperativa Sociale Onlus, Italy

Colegiul National de Arta “Octav Bancila”, Romania

Escola Secundária Dr. José Afonso, Portugal

A & A Emphasys Interactive Solutions Ltd, Cyprus

The project’s main intended impact is to monitor ELET by supporting schools in creating tools for them based on proven AI technology with three main purposes:

Descriptive: to better understand the factors leading to ELET in a specific, local context, to improve ELET-related policies.

Predictive: to provide teachers with an actionable and interpretable tool to identify at-risk students early on, adapted to the specific school context.

Prescriptive: supporting different stakeholders by suggesting interventions with the highest impact

Main Topics

We will also look into the following main topics:

Data analysis, classification, responsible AI, knowledge-driven systems
Preventing early school leaving and failure in education
Inclusion, promoting equality and non-discrimination

Project Info

Start 01/01/2024

End 31/12/2026

Funding European Commission, Erasmus+

Members Prof. Dr. Johan Loeckx, Dr. Leticia Arco García, MSc. Laetitia D’Ornano