Human AI Interaction (WP)* (E)
Niveau
Master`s course
Learning outcomes of the courses/module
The participants:
• are able to explain the basic principles of human–AI interaction and can apply these to modern AI systems such as LLMs, chatbots, agents, and decision-making systems
• are able to analyse user needs, tasks, and usage contexts when interacting with AI
• are able to develop interaction models and UX patterns for AI systems
• are able to analyse the distribution of roles between humans and machines and can design various application scenarios
• are able to explain the basic principles of human–AI interaction and can apply these to modern AI systems such as LLMs, chatbots, agents, and decision-making systems
• are able to analyse user needs, tasks, and usage contexts when interacting with AI
• are able to develop interaction models and UX patterns for AI systems
• are able to analyse the distribution of roles between humans and machines and can design various application scenarios
Prerequisites for the course
keine
Course content
- Fundamentals of Human–AI Interaction
- Human cognition, mental models, decision-making behavior
- HCI principles for AI systems
- UX Design for AI
- Interaction patterns (conversational UI, agents, multimodal interfaces)
- Prompt design, system messages, feedback loops
- Trust and user acceptance
- Human-in-the-Loop & Human-on-the-Loop
- Alignment of human–machine roles
- Task distribution, limits of automation
- Error prevention & recovery design
- Empathetic & Social Aspects of AI Interaction
- Social responses, trust
- Persuasive and adaptive systems
Methods & Tools
- Usability testing for AI systems
- Prototyping tools
- Evaluation methods (task success, trust metrics, cognitive load)
- Human cognition, mental models, decision-making behavior
- HCI principles for AI systems
- UX Design for AI
- Interaction patterns (conversational UI, agents, multimodal interfaces)
- Prompt design, system messages, feedback loops
- Trust and user acceptance
- Human-in-the-Loop & Human-on-the-Loop
- Alignment of human–machine roles
- Task distribution, limits of automation
- Error prevention & recovery design
- Empathetic & Social Aspects of AI Interaction
- Social responses, trust
- Persuasive and adaptive systems
Methods & Tools
- Usability testing for AI systems
- Prototyping tools
- Evaluation methods (task success, trust metrics, cognitive load)
Recommended specialist literature
- Norman, D. (2013): The Design of Everyday Things
- Rogers, Y., Sharp, H. (2023): Interaction Design: Beyond Human–Computer Interaction
- Sheiderman, B. (2013): Designing the User Interface
- Kempka, D., Nudelman, G. (2025): UX for AI
- Shneideman, B. (2022): Human-Centered AI
- Rahwan, I. (2018): Society-in-the-loop: programming the algorithmic social contract
- Rogers, Y., Sharp, H. (2023): Interaction Design: Beyond Human–Computer Interaction
- Sheiderman, B. (2013): Designing the User Interface
- Kempka, D., Nudelman, G. (2025): UX for AI
- Shneideman, B. (2022): Human-Centered AI
- Rahwan, I. (2018): Society-in-the-loop: programming the algorithmic social contract
Assessment methods and criteria
Seminar thesis
Language
English
Number of ECTS credits awarded
4
Semester hours per week
Planned teaching and learning method
Lecture, group work (project), presentation and discussion of tasks
Semester/trimester in which the course/module is offered
3