Vibe synth

Teaching must convey the technological foundations of GenAI, respond to what each student already knows, and presupposes teachers who are informed in the field. These elements are necessary for any practical use – such as artistic, experimental work and critical reflection – whether or not GenAI is used in the end. This includes knowing how the technology works, and also its social and ecological costs. GenAI literacy does not require using the tools: students and teachers should be free to choose whether they use GenAI or not.

Teaching must convey a differentiated skill set for dealing with GenAI. This includes the students’ ability to critically assess results on an aesthetic, content-related and ethical-social level. To acquire this ability, a direct and repeated engagement with the “material” must be ensured, provided in teaching and placed before the use of GenAI.

What we mean by engagement here is an educational process led and shaped substantially by, with and for people.

Teaching must recognise that GenAI is no longer a classic tool, but that we stand in a complex – dialogic as well as practical – “co-productive” relationship with it. It is precisely this relationship that must be defined, reflected on and worked out by and in teaching.

GenAI tools can be trusted only to a limited extent. It follows that a critical stance must come before their use (see § 1). Being critical does not automatically mean refusing to use them altogether.

Teaching in artistic contexts requires different – practical as well as reflective – approaches to GenAI than in academic contexts. The specifics of each discipline must be taken into account in an ongoing, critical process. Individual disciplines must be able to develop their own approaches on their own responsibility.

Teaching must not ignore the legal questions of data protection in the use of GenAI and must address them proactively. This applies in particular while there are still no guidelines binding on all disciplines. A university must take a clear position here – for instance on possible violations of personality rights or copyright. The use of locally installed open-source models should be prioritised to ensure safe use that complies with data protection.

Teaching must take account of emerging professional forms of practice against the background of a changing educational horizon. That new practices emerge using GenAI also means that classic forms of practice gain relevance (see § 1).

Teaching must focus on conveying processes of appropriation – processes become more relevant than results. Experimenting, playing and “tinkering” become central basic skills in a time of rapid technological innovation. Basic knowledge in the age of GenAI calls for a broad meta-expertise that can be applied across the most varied disciplines.

Teaching itself cannot, in its essence, be replaced or automated by GenAI. Presence, interpersonal communication and physical presence remain core parts of the teaching of the future.