Human AI Interaction (elective)
Niveau
Second cycle, master’s degree
Learning outcomes of the courses/module
The participants:
• can explain fundamental principles of human-AI interaction and apply them to modern AI systems such as LLMs, chatbots, agents, and decision-making systems
• can analyze user needs, tasks, and contexts of use in interactions with AI
• can develop interaction models and UX patterns for AI systems
• can analyze the distribution of roles between humans and machines and design different application scenarios
• can explain fundamental principles of human-AI interaction and apply them to modern AI systems such as LLMs, chatbots, agents, and decision-making systems
• can analyze user needs, tasks, and contexts of use in interactions with AI
• can develop interaction models and UX patterns for AI systems
• can analyze the distribution of roles between humans and machines and design different application scenarios
Prerequisites for the course
not applicable
Course content
- Foundations of Human–AI Interaction
- Human cognition, mental models, decision-making behavior
- HCI principles for AI systems
- UX Design for AI
- Interaction patterns: conversational UIs, agents, multimodal interfaces
- Prompt design, system messages, feedback loops
- Trust and user acceptance
- Human-in-the-Loop and Human-on-the-Loop
- Alignment of human–machine roles
- Task allocation and the limits of automation
- Error prevention and recovery design
- Empathic and Social Dimensions of AI Interaction
- Social responses and trust
- Persuasive and adaptive systems
- Methods and 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 UIs, agents, multimodal interfaces
- Prompt design, system messages, feedback loops
- Trust and user acceptance
- Human-in-the-Loop and Human-on-the-Loop
- Alignment of human–machine roles
- Task allocation and the limits of automation
- Error prevention and recovery design
- Empathic and Social Dimensions of AI Interaction
- Social responses and trust
- Persuasive and adaptive systems
- Methods and 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
Project
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 assignments
Semester/trimester in which the course/module is offered
3