AI Literacy

Artificial intelligence (AI) is reshaping how employees and managers make decisions, solve problems, and learn. While AI technologies facilitate and broaden access to information, the resulting information density raises demands on information selection, integration, and reflection. This redefines the skills required to use AI in ways that enhance productivity, innovativeness, and job satisfaction. This course equips management students with the knowledge, competencies, and hands-on experience needed to successfully navigate an AI-transformed business world. It is intentionally designed for management students with a business rather than a technical background. We will address the following topics:
1)    an assessment of students’ current AI literacy;
2)    an introduction to the function, capabilities, and limitations of AI technologies;
3)    cognitive mechanisms through which AI use reshapes strategic decision-making;
4)    an overview of future-relevant skills;
5)    the implications of AI use for job design, professional identity, and job satisfaction;
6)    best practices in managing firms’ AI transformation;
7)    ethical considerations of AI use.

• Synthesize and critically reflect on the key implications of AI for human cognition, strategic decision-making, and work life.
• Analyze and critically evaluate AI-related decisions in real-world cases to make informed strategic choices about AI use, automation, and augmentation.
• Formulate research questions and carry out an empirical project, linking theoretical concepts on AI literacy to real-world empirical insights.
• Effectively apply AI tools for innovative problem-solving and strategic decision-making.

In presence

Introductory lectures grounded in current research equip students with knowledge on the functions, mechanisms, and implications of AI use. Joint reflections, case discussions, and a hands-on group project deepen learning and help students develop AI literacy. Students are encouraged to use AI tools throughout their work process, including for brainstorming, outlining, and analysis. However, they are fully accountable for what they submit.

Given the condensed nature of the course, a minimum of 80 % attendance is required. Missing a session significantly impacts the learning experience of students and their peers.

Group project (70 %)
Students will conduct a small empirical research project within one of the course's thematic clusters. Grading is based on a group presentation and individual Q&A. Assessment criteria include: clarity of the research question, connection to course concepts, quality of the data collection process, depth and communication of findings, effectiveness of AI use, and critical reflection on the findings and AI's role.

Individual video reflection (15 %)
Each student submits a personal video reflection (max. 3 minutes) including a video transcript within one week after the end of the second course day. The video should address their main learning from the course, how their view on AI has changed, and what this means for their own career. Videos are assessed on depth of reflection, integration of course concepts, and clarity of communication.

Class participation (15 %)
Students are expected to contribute actively, constructively build on their peers’ ideas, and critically engage with course concepts.