The TrainDL consortium with partners in Germany, Austria and Lithuania aims to embed educational concepts for data literacy and artificial intelligence (AI) competencies into teacher training.
For this purpose, a policy experimentation approach is used in which the effectiveness and efficiency of educational interventions are tested in field trials in order to develop educational policy measures based on these results. The project is funded by the European Union through the Erasmus+ program.
A basic understanding of artificial intelligence and data will be increasingly important for all people in order to continue to participate equally in public and economic life.
Computer science teachers, as well as teachers of all STEM subjects and primary education, should therefore be enabled to integrate these topics into their lessons in an accessible and technically sound manner.
By focusing on data literacy and AI education, TrainDL recognizes the need for contemporary computer science education in schools across Europe. This takes into account, on the one hand, the varying levels of access to computer science education per se, but also the digitalisation of all areas of life and the risk of the digital divide. Access to AI and data competencies via general school education should be made possible.
It is a central goal of TrainDL to provide evidence-based recommendations for the structural implementation of data literacy and AI skills in curricula and education systems across Europe. A monitor of teacher education will give visibility to the status of data and AI education. The project activities will also develop materials for teacher training, in particular Open Educational Resources (OER) and training modules for teachers of different school levels and subjects.
Starting from the partner countries Lithuania, Austria and Germany, the project results will be designed for application in all EU member states.
In the Policy Cluster, the educational policy situation with regard to the project goals (link) is surveyed, analyzed and further developed in close cooperation with relevant ministries and educational authorities. From this, guidelines for the education interventions of the Education Cluster will be developed and continually refined. In addition, the findings will be processed in a newsletter for teacher training and presented in recommendations to education policy decision-makers.
The Education Cluster is concerned with the didactic development and implementation of interventions in teacher training at the primary and secondary levels. New modules on data and AI competencies are being developed and applied directly in teacher training courses in three project cycles. The data and experiences gathered in the process will allow the Policy Cluster to work in an evidence-based manner. In addition, findings will be translated into in-service training modules and best practice recommendations.