10.00–10.10 am

Welcome

Welcome address by Prof. Dr Pedro José Marrón, Vice-Rector for Transfer, Innovation and Digitalisation
and Prof. Dr Stefan Rumann, Vice-Rector for Studies, Teaching & Education

10.10–10.40 am

Intro:
Introduction to the basics of text generators

Matthias Kramer, Faculty of Computer Science

“Never gonna give you...” – do you know how the song goes on? Perhaps you’ve already gained enough experience with pop music or with the “Rickrolling” meme to be able to continue that opening line in a meaningful way. Text generators such as Gemini, DeepSeek or ChatGPT are generative machine-learning systems and, in principle, do nothing else. They enable an input text to be “coherently” expanded upon. These machine-generated texts are now indistinguishable from those produced by humans. But what concepts underpin these systems? And what does this imply for the way we interact with them? This short presentation will explain the basic functioning of text-generating tools.

10.40–10.50

Break

10.50–11.35

‘Keep on Prompting’
Tips and methods for using Large Language Models (LLMs)

Lea Grey, Centre for Teacher Training (ZLB)
Marianne Wefelnberg, Centre for Information and Media Services (ZIM)

Good results do not come automatically, even with generative AI – just as in real life, the key here is: don’t give up. In this session, we would like to show you what to bear in mind when prompting and what methods are available to optimise the output. Interacting with Large Language Models (LLMs) opens up new possibilities, but limitations also become apparent. Using engaging examples, we’ll look at the results with you and discuss possible use cases.

11.35–12.20

“I still haven’t found what I’m looking for”
Literature research with AI

Katharina Cyra & Leana Neuber, University Library (UB)

You’ll be familiar with this: spending hours searching for the right source, only to be left with the feeling that something’s still missing. Literature research can feel like a U2 song: lots of hope, lots of effort – but not always a clear hit. But what happens when you bring artificial intelligence into the mix?

In this input session with interactive elements, we’ll show you how AI tools can support you in your literature search: from identifying a topic and getting your bearings to conducting more targeted searches in databases. Using practical examples and short exercises, we’ll explore together what works and how, and where AI doesn’t (yet) help.

12.20–12.35

Break

12.35–13.20

‘Running up that hill’
Academic writing with AI

Claudia Spanier, Institute for Key Academic Skills (IwiS)

During university studies, academic writing often resembles a laborious, sometimes solitary process that requires perseverance, determination and constant deliberation – just as Kate Bush sings about in “Running up that Hill”. Can this hill be “flattened” through the use of AI? Using examples of how various LLMs can be applied to typical stages of the writing process, we will examine the differences in the tools’ outputs and discuss what is needed to ensure that the use of these tools genuinely aids the writing process.

1.20–1.50 pm

Artificially generated, genuinely problematic?
On generative AI and the reliability of knowledge

Dr Matthias Begenat, Centre for Advanced Internet Studies (CAIS)

AI-generated content poses a challenge to societal communication. The ability to generate unlimited amounts of synthetic content and distribute it via social media also increases the risk of encountering misinformation. At the same time, the importance of generative AI as a source of scientific and political information is growing. In this talk and discussion, we will explore what this means for the reliability of knowledge and how we can deal with these new uncertainties.

1.50–2.00 pm

Conclusion & Outlook