This interdisciplinary seminar teaches students from all disciplines fundamental and practical skills in dealing with artificial intelligence in a university context in German and English.
The focus is on:
o AI literacy (how AI works, its application, evaluation)
o Critical Thinking and reflection on AI
o Academic Writing and research with AI
o ethical and responsible use
o Development of own AI tools (no-code/low-code)
The seminar comprises four blocks (30 teaching units):
Block 1 - Basics
Introduction to AI, LLMs, SLMs; opportunities, risks, and ethics; university guidelines; personalizing ChatGPT; free writing; AI tools.
Block 2 - AI workshop with development team
Testing a beta version of an AI tool for promoting critical thinking; two test runs; World Café; evaluation; meta-reflection.
Block 3 - Asynchronous AI project
Developing your own AI prototype for study or work; documentation; reflection.
Block 4 - Presentations
25-minute presentations of the projects according to ABC criteria (quality, creativity, learning & innovation).

Lectures, workshops, group work, AI-supported exercises, asynchronous work, presentations, discussion, reflection.

Knowledge & Understanding
o Explain how large and specialized, small language models work
o Assess opportunities, risks, and limitations
o Apply university guidelines on AI responsibly
Application
o Use AI tools for research, analysis, writing, and creative work
o Employ effective prompting techniques
o Apply scientific AI tools (Consensus, Elicit, Semantic Scholar, NotebookLM)
Design
o Develop your own AI prototypes or workflows (no-code/low-code)
o Document configurations transparently
Reflect
o Analyze bias, data protection, ethics, hallucinations
o Critically examine AI outputs
o Reflect on your own AI-related learning

Key Competences
o Critical Thinking
o AI Literacy
o Digital Literacy
o Ethical Reasoning
o Creativity & Problem Solving
o Research Skills
o Academic Writing
o Interdisciplinary Collaboration

Provided through Moodle

Project & Presentation (70%)
Grading according to ABC criteria:
A - Quality of the solution:
o Accuracy & reliability of AI outputs
o Consistency
o Relevance to the use case
o Technical comprehensibility
o Target group & problem definition
B - Creativity:
o Originality
o Human-AI collaboration
o New perspectives
o Practical value
o Comparison to traditional methods
C - Learning & innovation:
o Insights into AI potential
o New ways of working
o Ethics, data protection, bias
o Practical relevance
Collaboration (30%)
o Pre-tasks
o Workshop reflection
o Project documentation
o Final reflection

Attendance min. 80%

Grading