ML2MT – From Machine Learning to Machine Teaching – Making Machines AND Humans Smarter

The 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.

Project Description

Inspired by the success of learning machines, as exemplified by the board game Go (in the computer version "AlphaGo Zero"), the project is concerned with developments in human-machine interaction in education.

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 are being developed. In the second project unit, the practical applications and long-term effects of machine learning are 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.

Project Objectives

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.




Goethe University Frankfurt (GU)
Technical University of Darmstadt

Project Management

Prof. Dr. Hendrik Drachsler

Project Team

Sebastian Wollny

Project Details

Current project
Area of Focus Education in the Digital World
Department: Information Centre for Education
Unit: Educational Technologies
Education Sector: Science
04/2022 - 03/2026
External funding
Contact: Dr. Daniele Di Mitri, Post-doc Researcher