Project Profile AI for Open Science: Rethinking Replication and Methods Teaching in the Age of Artificial Intelligence

The project aims to develop and test a two-semester teaching format that systematically integrates Open Science and Artificial Intelligence (AI). At its core lies a fundamental tension: while AI can lower technical barriers to replication in the social sciences, its use can also introduce new risks for transparency, traceability, and reproducibility.

The project addresses this tension directly. It seeks to equip students to use AI in empirical research in a reflective and responsible way. Students will learn to use AI as a tool that supports open, transparent, and reproducible research processes, while remaining attentive to epistemic and methodological risks.

In doing so, the project contributes to the development of AI literacy, Open Science competencies, and methodological judgment in the teaching of social science methods.

The project develops a two-semester teaching format that systematically connects Open Science and Artificial Intelligence. Through replication projects and machine learning (ML) applications, students learn to critically apply AI-supported workflows and to assess their impact on transparency and reproducibility. The goal is to strengthen competencies in AI and Open Science.

The modular teaching concept can be transferred to other quantitative methods courses and academic disciplines. In particular, AI-supported code translation and documentation workflows can be applied independently of specific programming languages. All materials will be made openly available, enabling sustainable reuse beyond the course itself.