Welcome to the area of teaching and studies

We enable our students to study in a way that is both methodologically sound and very practical.

We are proud that our courses have received several awards.

Our aim is to combine academic rigor with real-world challenges from business and society. The motto is: You have to bring something to the party. We expect and hope our students will demonstrate curiosity, commitment, and the motivation to make a difference.

Our teaching concept combines innovative didactic approaches with the close integration of our industry partners in lectures and tutorials. For many years, we have enjoyed a trusting partnership with several companies of varying sizes.

We also utilize innovative learning methods such as case studies, agile software projects (Scrum), and the collaborative development and analysis of business models.

To create optimal and flexible learning conditions—in addition to traditional classroom instruction—we offer our courses in a dual format: blended learning concepts with lecture recordings and accompanying online forums.

This course focuses on the use of artificial intelligence (AI) as a driver of digital transformation in business and society. Using selected industries—including Industry 4.0, finance, mobility, medicine, and sports—we analyze how AI-based systems fundamentally change value creation, business models, and decision-making processes.

A particular focus is placed on data-driven decision-making and the development of new digital and AI-based business models. Students learn how companies strategically use artificial intelligence and machine learning to optimize processes, drive innovation, and achieve sustainable competitive advantages.

In addition to concrete application examples, we also address technical, organizational, and ethical issues associated with the use of AI—such as transparency, accountability, acceptance, and the impact on work and organization.

The course is closely integrated with industry practice. Guest lectures from our cooperation partners, such as Lufthansa Cargo, Anacision, Eckelmann AG, Knauf, Samson AG, Stifel, and Campana & Schott, provide insights into real-world AI projects and the practical application of artificial intelligence.

In the accompanying exercises, the lecture content is reinforced through current case studies, with a particular focus on the use of AI in real-world decision-making situations.

This lecture introduces students to the core economic principles of the internet, software, and platform economy – with a particular focus on artificial intelligence, data-driven business models, and AI agents. The aim is to develop a solid understanding of how digital platforms, cloud providers, and AI providers organize value creation, structure markets, and create new competitive dynamics

Starting with the fundamentals of digital network economics, we analyze the unique characteristics of digital goods, economies of scale, and direct and indirect network effects. Building on this foundation, we examine the platform economy as the central organizational and business model of digital markets.

Practical examples demonstrate how platforms use data, algorithms, and AI to manage user interactions, build ecosystems, and achieve sustainable competitive advantages. We also discuss regulatory and competition policy issues in the context of large platform and AI providers.

Furthermore, we focus on the economic principles of the software, cloud, and AI industries. The focus is on pricing strategies—such as usage-based, performance-based, and data-based models—as well as cooperation, ecosystem, and acquisition strategies. Particular attention is paid to the business models of cloud and AI providers with their high economies of scale, lock-in effects, and data-driven learning curves.

AI agents are increasingly coming into focus as a new paradigm of digital value creation. In this lecture, we examine how autonomous or semi-autonomous AI systems automate processes, prepare decisions, or make decisions independently—and thus transform existing business models, organizational structures, and competitive logics.

Another key topic is data and models as strategic resources for digital business models. Here, we address theoretical approaches to privacy and the economic evaluation of disclosing personal data. We also explore the central role that data, models, and continuous learning processes play in building scalable platforms, AI systems, and agent-based applications.

Guest lectures from industry professionals complement the course. In the accompanying exercise, we will deepen our understanding of the content using current case studies, particularly with regard to strategic decisions in platform, AI and AI agent ecosystems.

For over ten years, we have partnered with Reichman University (formerly IDC Herzliya) in Israel, and in particular with Yossi Maaravi, Dean of the Adelson Business School in Herzliya. The goal is to provide practical training in the creation and innovation process for digital products and services – and we can learn a great deal from Israel's startup scene.

This five-day intensive course enables participants to independently develop new business ideas and systematically evaluate them. They have the opportunity to present their business ideas to our numerous industry partners and venture capital firms.

The focus is not only on developing new business models for digital products and services, but above all on learning entrepreneurial thinking and action.

This event is held in cooperation with the HIGHEST Innovation and Start-up Center at the Technical University of Darmstadt.

Sound knowledge of project management is becoming increasingly important in the course of digital transformation. In this course, we teach our students management- and technology-oriented fundamentals of project management. Our focus is on the application of agile methods. Case studies further reinforce the course content.

In the following semester, students apply their knowledge during an IT internship. Working in teams, they tackle problems from real-world business practice and primarily develop software solutions based on Scrum. The workload for each student is approximately 270 hours. It's a worthwhile investment: The solutions developed are frequently implemented successfully by our partner companies.

This two-semester course focuses on artificial intelligence (AI) and machine learning. We have been working closely with our partner Porsche in this area for many years.

Students acquire fundamental skills in machine learning and the use of software tools for developing machine learning applications. The focus is on the “After Sales” area. In so-called “Capstone Projects”—small hackathons—students develop innovative solutions for specific problems at Porsche SE.

Artificial Intelligence I

In the first module, students gain an insight into the theoretical and historical foundations of artificial intelligence. Topics include:

• the emergence of the AI research field

• agents and search methods

• knowledge representation and processing

• data and knowledge modeling

• expert systems and inference

• central algorithms and challenges of machine learning

An accompanying introduction to the Python programming language serves the practical implementation and discussion of the teaching concepts.

Artificial Intelligence II

In the second module, students deepen their knowledge using current and practical issues. These include, among others, big data, parallelization, and transparent and fair AI. Furthermore, we explore the functionality of advanced algorithms and methods such as deep neural networks and generative models.

Another focus is on the development process of AI solutions. Our goal is to empower students to realistically assess and utilize the potential of AI and to successfully overcome implementation challenges. The Python programming language is also used here.

In this course, we introduce industrial engineering students to the economic framework of the software industry as well as to the management and execution of software development projects.

Building on this foundation, students learn fundamental concepts of data processing and the representation of algorithms. They acquire skills in object-oriented programming languages and learn to independently develop software solutions for specific problems.

Finally, we examine the differences between traditional software development and software development with machine learning and discuss the opportunities and specific challenges that arise.

Practical exercises with Eclipse and Java enable students to develop and test their own programs.

This lecture is dedicated to the economics of digital transformation. A particular focus is placed on the platform economy – a key business model in the digital age.

Furthermore, we will analyze various phases of digitalization, including the use of artificial intelligence, and their impact on businesses and the labor market.

We are delighted to have secured network economist and honorary professorDr. Holger Schmidt as a lecturer for this event!