Dates
| weekly | Monday | 12:15 - 13:45 | 12.10.2026 - 29.01.2027 | C 40.108 Seminarraum |
Curriculum context
Written Assignment (50%)
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 does not begin until 02.10.2026 at 08:00. It ends on 12.10.2026 at 23:59.
This course belongs to the cluster "M & Data Science: Data Science Seminar".
In this cluster, you have one choice during the lottery phases.
Persons
Content
How do AI technologies shape new business opportunities and how can entrepreneurs harness them strategically? This seminar combines entrepreneurial thinking with hands-on exploration of AI-driven business model development. Working in teams, students develop, challenge, and refine business model ideas for AI-driven ventures. The seminar is accompanied by a game-based learning format that enables students to experience business model decisions in a dynamic, simulated environment, reflect on entrepreneurial challenges, and explore strategies for adaptive business model development. Over the course of the semester, students iteratively refine their business models, applying both disciplinary knowledge and cross-functional perspectives. The course bridges data science competencies with entrepreneurial practice, making it particularly relevant for students at the intersection of technology and business.
• Students understand key concepts and frameworks for AI-driven business model development and can apply them critically to real-world contexts.
• Students experience entrepreneurial decision-making through game-based learning and reflect on the boundaries and potentials of disciplinary knowledge in a team setting.
• Students develop, challenge, and iteratively refine a business model idea for an AI venture over the course of the semester.
• Students critically reflect on the societal, ethical, and competitive implications of AI-based business models.
Evaluation
Further information on teaching evaluation: https://www.leuphana.de/en/teaching/quality-management/evaluation/course-evaluation.html