Dates
| every 14 days | Monday | 10:15 - 13:45 | 06.04.2026 - 10.07.2026 | C 14.103 Seminarraum |
Curriculum context
Written assignment (30%)
Resit date: No resit date will be offered to this assessment, because it is didactically inseparably connected with one of the associated courses. A resit will only be possible, if the module is available again.
Organizational information
Registration
Registration ends 07.4.2026 at 23:59 h
Persons
Content
In recent years, technology has rapidly changed the way we work. Digitalization, automation and novel ways of communication have altered the way that employees engage with the workplace. Career pathways have also changed. Today, graduates have many jobs, craft multiple careers and work in many industries in their lifetime. Rapid advances in technology, i.e., the use of AI, are enabling firms to automate processes, which is displacing or changing routine work. Simultaneously, the power of knowledge workers is rising. In parallel, new platform industries and new forms of collaborative and distance-based work are emerging. All of this is offering new challenges and opportunities. In this course you will explore how rapid technological advances are creating digital disruption that will impact your work in the future. It will enable you to identify opportunities to craft your own career pathway in a rapidly globalising, knowledge and service-intense economy. In particular, we will discuss
(1) Digitalization and datafication in the workplace
(2) Constant connectivity and shifting (work-life) boundaries
(3) Digitalization and job crafting
(4) Digitalization, control and resistance
(5) Platform economy and platform work
(6) AI and the changing nature of (routine) work
Throughout the course, students will be encouraged to actively engage in debates to explore the challenges and opportunities of digital disruption on their careers. Through a combination of theory and practical application, students will be well-equipped to navigate the complexities of the modern work landscape. Students will also participate in and learn about team work. Finally, students will acquire the skills to effectively utilize generative AI in their projects, understanding how to avoid common pitfalls and generate useful outcomes.
Evaluation
Further information on teaching evaluation: https://www.leuphana.de/en/teaching/quality-management/feedback-instruments.html