Our teaching profile is highly interdisciplinary and our courses emphasize transferable skills and methods. Our courses therefore attract students from diverse programs ranging from mechanical engineering to art history. Having said that, the focus of our teaching is on content and methods that are useful for experimental and computational work in linguistics, psycholinguistics, and computational linguistics.
We freely combine various forms of teaching as it suits the content, including lectures, recorded videos, free discussions, and project work. Specific skills that we aim to pass on to students are the ability to conduct a well-designed experiment, to analyze and visualize data, to present complex ideas effectively, to use computational tools, and to work in teams.
We believe that the usefulnes of ChatGPT in teaching is often wildly overstated especially by non-experts and journalists. ChatGPT does not really understand what it is talking about and it is easy to demonstrate that. However, where useful applications exist, we do teach students how to use AI chat bots productively and legitimately, for example in the creation of linguistic stimuli for experiments or to assist in annotation tasks.
Formal teaching evaluations suggest that our teaching approach is successful: Across our courses, the average “overall satisfaction” was 1.37 (1=“very satisfied” – 5=“very dissatisfied”, data from 2021 to summer 2024).
Main ares of teaching:
- Human language comprehension
- Eye-tracking methods
- Statistical methods
- AI language models
- Computational methods for linguists and the humanities more generally