Implementing and Adapting to Artificial Intelligence in Organizations
The faculty of Industrial Engineering and Innovation Sciences offers a course on the impact of AI implementation on organizations. It emphasizes effective collaboration between humans and AI systems while prioritizing employee wellbeing, using a multidisciplinary approach.
- Teachers: A. S. Ulfert-Bank, A. G. Nicolau, P. M. Le Blanc.
- Academic level: Master.
- Stakeholders: Lecturer and guest speakers.
- Disciplines: Industrial Engineering and Innovation Sciences – Human Performance Management.
- Resources: There are different knowledge clips selected theoretical and empirical papers, as well as mandatory and recommended additional (guest) lectures provided to students.
SETUP
This course combines perspectives from organizational psychology with theories from human-technology interaction and computer science to address AI in organizational settings at multiple levels:
Individual level:
- How do individuals collaborate with AI at work?
- How are individuals affected by AI-related changes at work?
- How can system design be inspired by organizational psychology?
Team level:
- How do teams interact with AI, as artificial agents are becoming team members?
Organizational level:
- How can organizations assist implementation and help employees to adapt to AI at work to foster long-term collaboration?
To better understand the role that AI will play in organizations and the changes that are associated with it, current examples of real-world AI applications in organizations, such as AI teammates, decision support systems, or robotics across diverse organizational domains (e.g., personnel selection, management) are used.
INTENDED LEARNING OUTCOMES (ILOS)
The primary objective is to provide students with knowledge in organizational psychology, integrating theories from human-technology interaction and computer science, to address the topic of AI implementation in organizational settings at multiple levels. Students are able to answer the following questions:
- How do individuals collaborate with AI at work?
- How are individuals affected by AI-related changes at work?
- How can system design be inspired by organizational psychology?
- How do teams interact with AI, as artificial agents are becoming team members?
- How can organizations assist implementation and help employees to adapt to AI at work to foster long-term collaboration?
LEARNING ACTIVITIES
- Self-studying;
- Hybrid learning with knowledge clips;
- Attending (guest-) lectures;
- Challenge-based learning;
- Group assignment.
ASSESSMENT
The total assessment of the course is composed of a multiple-choice mid-term exam (30%, no resit), and a group assignment (70%, min grade 5.5, resit). Group assignment and presentation take place at the end of the course. Resit of group assignment: Adjustment of the original work (group assignment) based on expert feedback, including an additional reflection section.
EVALUATION
The course is routinely evaluated through feedback forms handed out to the students.
