The workshop begins with an overview of the strengths and limitations of current large language models (LLMs) and derives from this a general recipe for using them effectively.
Building on this foundation, we examine how to integrate AI across key stages of academic research: literature research, hypothesis development, support with phrasing, feedback generation, and programming and analysis tasks.
We also address ethical questions and the use of LLMs in the context of good scientific practice. In addition, we discuss serious risks and how to manage them.
The goal is to provide researchers with a clear, well-founded, and practice-oriented basis for using AI responsibly and effectively in their day-to-day work.
No prior technical expertise required!
Contents in brief:
- LLM fundamentals (how they work, strengths, limits, effective-use principles)
- AI across the research and writing process (workflows and specialized tools)
- Technical setup: APIs and local models
- Good scientific practice
- Key risks and mitigation strategies
Counting towards the Certificate "Leadership, Management, Science Communication and Knowledge Transfer" (Module IV, 8 AE).
Dr Daniel Friedrich
Philosopher. Data scientist at eduki.com. Previously, Post-Doc at the Berlin School of Mind and Brain at the Humboldt-University in Berlin and PhD in philosophy at the Research School for Social Sciences at the Australian National University. Educated at the FU-Berlin, the University of Heidelberg and Oxford University. Publications on philosophy of mind, action theory, metaethics and applied ethics. Work area as a trainer: project- and self-management for PhD students and Post-Docs.
