ML2MT - From Machine Learning to Machine Teaching – Making Machines AND Humans Smarter
The ML2MT project takes a look at developments in human-machine interaction in education and aims to derive generalizable socio-technological and psychological findings as well as make recommendations to further improve human-machine interaction.
With almost 10 million euros, the Volkswagen Foundation is funding research projects that explore how artificial intelligence will affect society. The Goethe University Frankfurt (GU) was successful with an application that focuses on developments in human-machine interaction in education. The success of learning machines, as exemplified by the board game Go (in the computer version "AlphaGo Zero"), inspired the scientists.
In order to make optimal use of common resources and to generate synergies, the consortium has developed a detailed methodological plan consisting of three project units: In the first project unit, the cognitive, pedagogical and technical foundations of machine learning is being developed. In the second project unit, the practical applications and long-term effects of machine learning is empirically investigated. Finally, the third project unit summarizes general principles of machine learning and thus aggregates the knowledge created in this project.
With this methodological approach, the project will contribute to a sharpened understanding of human-AI interaction and the resulting potentials for all areas of life.
The project aims at a better understanding of how humans and machines in collaborative human-AI systems can develop new knowledge in symbiotic interaction with each other. For this purpose, the analytical and technical foundations responsible for the successful transfer of new knowledge from intelligent machines to humans and vice versa will be explored. This is investigated by means of hybrid human-machine systems in case studies from medical diagnostics, economic decision-making and financial market forecasting.
Cooperation: Goethe University Frankfurt (GU)
Duration: 04/2022 - 03/2026
Project Team: Sebastian Wollny
Contact: Hendrik Drachsler