Human-AI Interaction & Digital Transformation
Niveau
Master
Learning outcomes of the courses/module
The participants:
• understand the fundamentals and functioning of human–AI interaction and digital transformation.
• analyze human behavior in interaction with AI systems (e.g., trust, acceptance, bias).
• apply key methods for evaluating and designing AI interactions.
• transfer technological developments to organizational and business psychology contexts.
• develop digital transformation approaches (e.g., roadmaps, capability building, change concepts).
• critically reflect on the ethical, social, and organizational implications of AI.
• use AI tools in practice and evaluate their potential applications in organizational contexts.
• understand the fundamentals and functioning of human–AI interaction and digital transformation.
• analyze human behavior in interaction with AI systems (e.g., trust, acceptance, bias).
• apply key methods for evaluating and designing AI interactions.
• transfer technological developments to organizational and business psychology contexts.
• develop digital transformation approaches (e.g., roadmaps, capability building, change concepts).
• critically reflect on the ethical, social, and organizational implications of AI.
• use AI tools in practice and evaluate their potential applications in organizational contexts.
Prerequisites for the course
None
Course content
• Foundations of Human–AI Interaction (interdisciplinarity; perception & trust; anthropomorphism; human–machine collaboration)
• Technological Foundations (AI/ML, generative AI; how they work, limitations, and bias)
• Forms of Interaction (verbal & nonverbal interaction; UX/UI; collaborative systems; human–AI teamwork)
• Emotion & Behavior (acceptance; trust; cognitive load; decision support through AI)
• Research Methods in HAI (experimental designs; usability testing; mixed methods; evaluation of AI systems)
• Digital Transformation (business models; organizational development; digital maturity; roadmaps)
• Organizational Perspective (capabilities; change management; leadership; cultural transformation)
• Fields of Application (HR, marketing, finance, production, logistics; AR/VR; blockchain; platform economy)
• Ethics & Governance (data protection; fairness; transparency; responsibility; AI governance)
• Lab & Practice (hands-on with AI tools; prototyping; analysis of real use cases; reflection on application experiences)
• Technological Foundations (AI/ML, generative AI; how they work, limitations, and bias)
• Forms of Interaction (verbal & nonverbal interaction; UX/UI; collaborative systems; human–AI teamwork)
• Emotion & Behavior (acceptance; trust; cognitive load; decision support through AI)
• Research Methods in HAI (experimental designs; usability testing; mixed methods; evaluation of AI systems)
• Digital Transformation (business models; organizational development; digital maturity; roadmaps)
• Organizational Perspective (capabilities; change management; leadership; cultural transformation)
• Fields of Application (HR, marketing, finance, production, logistics; AR/VR; blockchain; platform economy)
• Ethics & Governance (data protection; fairness; transparency; responsibility; AI governance)
• Lab & Practice (hands-on with AI tools; prototyping; analysis of real use cases; reflection on application experiences)
Recommended specialist literature
• Appelfeller, W., & Feldmann, C. (2023). Die digitale Transformation des Unternehmens: Systematischer Leitfaden mit zehn Elementen zur Strukturierung und Reifegradmessung. Berlin: Springer.
• Bartneck, C., Belpaeme, T., Eyssel, F., Kanada, T., Ksijsers, M., & Šabanovič, S. (2024). Human-robot interaction: An introduction. Cambridge, UK: Cambridge University Press.
• Behl, A., & Sampat, B. (2025) (Hrsg.). Digital transformation: A business imperative. Abingdon, OX: Routledge.
• Daugherty, P. R., & Wilson, J. H. (2024). Human + machine: Reimagining work in the age of AI. Boston, MA: Harvard Business Review Press.
• Wu, D., & Liang, S. (2026) (Hrsg.). Human-AI interaction and collaboration. Cambridge, UK: Cambridge University Press.
• Bartneck, C., Belpaeme, T., Eyssel, F., Kanada, T., Ksijsers, M., & Šabanovič, S. (2024). Human-robot interaction: An introduction. Cambridge, UK: Cambridge University Press.
• Behl, A., & Sampat, B. (2025) (Hrsg.). Digital transformation: A business imperative. Abingdon, OX: Routledge.
• Daugherty, P. R., & Wilson, J. H. (2024). Human + machine: Reimagining work in the age of AI. Boston, MA: Harvard Business Review Press.
• Wu, D., & Liang, S. (2026) (Hrsg.). Human-AI interaction and collaboration. Cambridge, UK: Cambridge University Press.
Assessment methods and criteria
Exam
Portfolio
Portfolio
Language
German
Number of ECTS credits awarded
6
Semester hours per week
Planned teaching and learning method
The course is based on an e-learning concept that incorporates a didactically aligned combination of online and in-person phases. During the online phases, an inductive learning approach is applied, fostering the independent acquisition of knowledge and the completion of tasks. The in-person phases follow a deductive approach, in which learning processes are guided and deepened, and content is systematically delivered through lectures as well as practical examples. In addition, students complete selected exercises in the lab to deepen the content taught in the course.
Semester/trimester in which the course/module is offered
3