GEN-VET

GENerative AI in VET (Vocational Education and Training)

Our goal

With the support of AI, we want to design learning processes in vocational education in such a way that vocational school students recognize and internalize the personal benefits of the learning content and the value of learning, regardless of the subject or occupational field.

The challenge

Without motivation, learning processes are not initiated and little effort is invested. Despite the proven effectiveness of utility interventions, there are currently no scalable, adaptive solutions that specifically support learners in building motivation.

Our approach

First, we will gain an overview of the current state of motivation among vocational school students. We will then pilot an AI assistant designed to promote motivation and optimize it iteratively at selected vocational schools.

This will be followed by a large-scale effectiveness study involving several hundred vocational school students. Finally, we will optimize the application and make it available as a free open-source tool for all vocational schools in Germany.

Background

Why motivation is so important
Motivation is the driving force behind successful learning. Those who do not see the point in learning content often do not even start or invest very little energy. This is particularly relevant in vocational education: many students ask themselves in class, “Why will I need this later on?” If this connection is missing, interest and learning performance decline.

Self-regulated learning – what does that mean?
Self-regulated learning means that learners actively control their learning process – they set goals, monitor their progress, and adjust their strategies. For this to succeed, it takes not only knowledge, but also motivation and the feeling that the effort is worthwhile. This is precisely where the challenge lies: without perceived benefits, it is difficult to stick with it.

The gap in everyday practice
Teachers know how important motivation is. But in heterogeneous classes with different career goals, it is hardly possible to establish a personal connection with each individual. Previous approaches are often complex and not scalable. That is why new methods are needed that are simple, flexible, and usable for all vocational schools.

Timetable

01.03.2026

Project launch

Year 1

Functional dialogue prototype with learner feedback

Year 2

Effectiveness and acceptance data, adaptive AI prototype

Year 3

Open source version, guide

28.02.2029

Closing symposium

Project participants

Project management: Dr. Tim Fütterer (Hector Research Institute of Education Sciences and Psychology, University of Tübingen)

Expertise self-regulation: Prof. Dr. Peter Gerjets (Leibniz-Institut für Wissensmedien) & Prof. Dr. Ulrich Trautwein (Hector Research Institute of Education Sciences and Psychology, University of Tübingen)

Expertise motivation: Prof. Dr. Hanna Gaspard (University of Konstanz)

Expertise teaching methods: Prof. Dr. Jochen Kuhn (Ludwig-Maximilians-Universität München)

Expertise learning analytics: Mihwa Lee supported by Dr. Björn Rudzewitz & Dr. Hannah Deininger (Hector Research Institute of Education Sciences and Psychology, University of Tübingen)

Expertise software development: Medientechnik (Leibniz-Institut für Wissensmedien)

Coordination office: Christina Michels (Hector Research Institute of Education Sciences and Psychology, University of Tübingen)

Field access: Berufsfachschulen Heimerer GmbH (represented by Andrian Heimerer and Vinzenz Benz, in particular) and other vocational schools in Hamburg and/or Schleswig-Holstein.

This project is funded by third-party funds from the Joachim Herz Foundation.