Plug-in Modules
Interdisciplinary, innovative and cooperative: in plug-in modules, students work together with their peers from all schools on projects that transcend disciplinary boundaries. Thus, they develop individual talents and skills in a wide range of future-oriented fields.
Plug-in Modules in the summer semester 2026
AI & Digitization
What role do diversity and social inequality play in the development, use and consequences of AI? The module provides skills to analyze the relationship between AI, diversity and social inequality in different areas of society. Core topics include an analysis of the contexts in which AI is produced and the role diversity plays in these contexts; a range of application areas, such as medicine, human resources, predictive policing, social media, in which diversity plays a particular role; and issues of North-South relations and global justice in relation to AI.
After the completition of the module the students will be able to understand the foundations and concepts of the regulation of AI in general. Furthermore, they will be able to illustrate possible applications and risks of using AI technologies. The module covers the following topics: Introduction to European AI Law; Overview of AI legal developments in the US, UK and other countries; Legal review of General Purpose AI Technology; AI in different sectors (e.g. contracts, healthcare and medicine, HR, traffic/automotives; discrimination, equality, fairness; transparency and explainabilty of algorithms.
Detailed information and registration in TUMonline:
The seminar enables students to actively explore how AI technologies can be used to automate, optimize, and redesign organizational processes, placing a strong emphasis on hands-on workshop formats. Working in teams, students apply concepts and tools to a practical use case, bridging theory and real-world business challenges. Students are required to hold a 15-minute presentation (30% of the final grade) and submit their project results (70 % of the final grade).
Detailed information and registration in TUMonline
This module provides a comprehensive introduction to foundational and advanced methods of text analysis, enabling students to extract insights from large collections of text data. Participants learn text preprocessing, linguistic techniques such as POS tagging and named entity recognition, as well as text representation, classification, clustering, sentiment analysis, and topic modeling. Through lectures with integrated exercises, these methods are implemented and applied in Python using real-world datasets.
The seminar examines AI’s transformative potential for business and society by exploring its unique relationship with language and meaning. We’ll draw on philosophical insights concerning key concepts such as digital, data, text, information, model, digitization, and computation, examining their relationship with interpretation, understanding, emotional experience, and other dimensions of lived experience. Some of our discussion of AI’s impact on work, society, and the ethical challenges it presents will be joined by employees of Europe’s largest IT company, SAP, and others at the forefront of business AI.
The module covers the following topics:
- Data Governance Act: Data sharing; data altruism; data intermediaries
- Data Act: Access to data; cloud switching; interoperability
- GDPR & Foundations: policy, fundamental rights, history, and relevance; Scope; data subjects, controller, joint controller, processors, third parties; principles; principle of lawfulness; data subject rights; obligations of controllers und processors; third country transfers; enforcement, remedies, liability authorities, damages
- Data protection legislation for specific sectors and conflicts
Link to detailed information and registration in TUMonline coming soon.
In this module students will: (1) learn about the history, philosophy and social studies of information, and particularly some ways in which modern society can be thought of as an ‘information society’; (2) explore how society and social configurations came to be objects of scientific study and intervention; (3) examine opportunities and concerns relating to the information society, the risks associated with technologies and infrastructures used to structure and process information, and the various types of discrimination and inequity associated with the use of information.
Link to detailed informationen and registration in TUMonline coming soon.
This seminar aims to provide students with a critical understanding of the implications of machine learning technologies and the responsibilities of users in this field.
What happens to social science theories when they “go computational”? This module explores how policy process research changes as qualitative traditions are combined with computational methods, especially natural language processing (NLP). Students critically examine how theory and method co-evolve in computational policy analysis while gaining hands-on experience with policy text data, from collection and annotation to theory-informed interpretation of results.
Detailed information and registration in TUMonline:
The practical workshop is a project-based course for Bachelor’s and Master’s students in which technical solutions to legal issues are developed. First, students learn the fundamentals and framework of legal tech. Students then work in teams to design and implement their own legal tech applications. Throughout the process, they are guided and supported by the lecturers and the Legal Tech Collab.
The module teaches the theoretical and practical foundations of programming with a focus on Python. Students learn core concepts such as variables, data structures, loops, and functions, as well as how to read, write, and process text files and extract data from the internet, including social media. Through lectures and hands-on exercises, they apply these methods to political science datasets, culminating in building their own Twitter crawler.
Over the last 30 years, Science and Technology Studies (STS) have developed a specific take on research methods. The field not only applies (mostly qualitative) research methods to produce claims and cases about science, technology, and society, but methods for knowledge production (of different scientific disciplines) are also an important object of investigation for STS. This course will introduce students to a range of different qualitative research methods relevant to STS research. We will develop an understanding of the whole qualitative research process from developing a research interest and choosing adequate methods to gathering and analysing empirical material. The focus of the course lies both on developing a reflexive understanding of the theory, practice and politics of different social science methods and on acquiring practical skills in their application. To this end, we will do a lot of practical exercises in and out of class and discuss what the exact nature, societal role and the politics of methods are. E.g., are they best understood as giving insights into a (social) reality exterior to them, or are they better understood as being part of making the social and technical relations we call society? The goal of the seminar is to provide students with an overview and first hands-on experience with different qualitative research methods, with a comprehensive understanding of the research process, and with the necessary conceptual tools to critically reflect upon and interrogate the practices, politics and ethics of doing qualitative research.
Link to detailed informationen and registration in TUMonline coming soon.
This course introduces students to the field of societal computing, focusing on the intersection of data, algorithms, and societal implications, including ethics and culture. Students will explore human-centered data to uncover patterns, test theories of societal behavior, and address ethical concerns in data analysis. The course covers concepts from social sciences, AI, machine learning, and network science, preparing students to critically assess research and solve problems in a systematic and responsible way, with no prior programming experience required.
Generative AI is increasingly transforming human behavior, interaction, experience, sense-making, and thought. Its application raises numerous ethical questions. These ethical questions and the potential handling of AI depend heavily on the conception of AI. Is AI a kind of artificial being or merely a tool? Or does it alter human thought, experience, behavior, and existence in entirely new ways? We examine widespread but fundamentally different conceptions of AI and discuss their ethical consequences based on scientific articles and case studies.
The AI strategy involves analyzing business models, tech platforms, and ecosystems to scale AI solutions effectively. Key success factors include talent development, leadership structures, and responsible handling of regulatory and ethical requirements. Practical GenAI use cases highlight real-world applications, impact measurement, and lessons learned from transformation projects.