Content
Open AI such as Chat GPT and Dall-E has made different AI areas (AI capabilities, AI bias, AI
interventions & AI applications) relevant to a wide variety of businesses. AI is now a key aspect
which enables organizations to improve productivity. Many organizations want to increase their
engagement with AI without concrete starting points or evidence-based strategies.
This course therefore combines theory and practice to help students learn about the potential
applications of AI to become more effective leaders and team members later on. To do this, they
will learn about the latest findings from academia and practice on AI in business, AI capabilities, AI
bias, and AI interventions, as well as techniques for developing and implementing their own AI use.
You will gain insights into academic research and professional experiences provided by a diverse
group of faculty members, industry leaders, and experts. Overall, you will participate in a variety of
learning activities to enhance your knowledge and skills and put what you learn into action.
Learning outcomes
Students who have attended this seminar will have
- A comprehensive understanding of key concepts in the area of productivity enhancement through
AI, particularly in the workplace.
- Knowledge of key issues with AI use, as well as promising approaches and methods to address
these issues
And have acquired practical skills related to
- The increase in productivity and quality of one's own work with reduced effort
- Thinking critically, reflecting on and applying concepts and scientific findings to concrete
challenges
- The engaging preparation of content for application.
Examination
The examination performance consists of
1) an individual presentation (20% of the final grade)
2) an individual elaboration of the presentation in the form of a detailed set of slides (40% of the
final grade)
3) an individual elaboration of the presentation in the form of small learning lessons and matching
single-choice questions (30% of the final grade)
4) active participation in the seminar (10% of the final grade)
In the examination, students demonstrate that they have
- have understood an assigned topic in depth and have demonstrated the most important aspects
in a way that is understandable for their fellow students
- have identified and prepared practical fields of application for this topic
- have presentation and communication skills that enable them to present and discuss their
findings on this topic in a clear and structured manner.
Prerequisites
Allocation of seminar places via central application procedure
(admission of max. 25 students)
Teaching & learning methods
In the course of the seminar, students will receive input on the topics covered in various thematic
blocks, as well as working materials for self-study and review. Subsequently, the contents are
deepened in the seminar in the context of exercises, role plays, reflections, presentations and
discussions.
As part of the examination, the participants will work on a topic from one of the overarching areas
(e.g., AI in business, AI skills, AI bias, AI interventions & AI applications) in depth and in detail and
prepare this didactically so that all other course participants can also benefit from it. During the
seminar they will have the opportunity to present and discuss this topic in different ways and to
receive feedback on the developed content following the presentation as well as in a peer-review
process. Based on this, the students will further elaborate, concretize and clearly prepare their
topic in the course of the semester.
Media & reading list
Activity-based learning, interactive teaching, flipped classroom, group discussions, presentations,
practical exercises, reflection, literature, script.
• Marco Iansiti (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and
Networks Run the World
• Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American
psychologist, 34(7), 571.
• Fleck, L., Rounding, N., & Özgül, P. (2022). Artificial Intelligence in Hiring: Friend or Foe?. ROA.
• Cousins G et al. Prescription drugs with potential for misuse: protocol for a multi-indicator
analysis of supply, detection and the associated health burden in Ireland between 2010 and 2020.
BMJ Open. 2023 Mar 2;13(3):e069665. doi: 10.1136/bmjopen-2022-069665. PMID: 36863742;
PMCID: PMC9990618.
• Chugunova, Marina; Sele, Daniela (2022). We and It: An Interdisciplinary Review of the
Experimental Evidence on How Humans Interact with Machines, Journal of Behavioral and
Experimental Economics 99. DOI
• AI Tools for Research Workflow in Academia (maintained under https://buff.ly/3zXkFMs by Prof.
Niels Van Quaquebeke)
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