Socio-Cognitive Engineering

Whether you are playing a game in virtual reality, driving a semi-autonomous car, educating yourself with a mobile app, creating music with generative AI or harmonizing your health and lifestyle with a robot assistant; nowadays intelligent networked information and communication technology is omnipresent. This course centers on the design of human-aware intelligence into such environments, to support joint human-technology performances that bring about positive human experiences. The focus is on social robots, with a hands-on assignment to design and test a robot that supports meaningful daily activities for people with dementia.

Socio-Cognitive Engineering (SCE) is a transdisciplinary methodology for designing, developing, and evaluating hybrid human–artificial intelligence systems, emphasizing the integration of cognitive science, systems engineering, human factors, ethics, and practical expertise from domain experts. SCE facilitates human-centered design innovation through iterative, evidence-based, and value sensitive design processes involving diverse stakeholders. It aims at interactive intelligent systems that are not only functionally effective but also intelligible, adaptive, and aligned with human needs and societal values. In the SCE course (MSc level), you will become acquainted with the application of a coherent set of methods for the design and evaluation of human­-agent collaboration. Based on the SCE-methodology, we will elaborate on the state of the art of intelligent user interfaces (ePartners), such as virtual assistants, robotic teammates, eCoaches, and companion agents.

The main topics of study are:

• Design methods: Participatory, value sensitive & scenario-based design, Claims analysis; Design patterns.

• Design for collective intelligence: Cognition models & embodiment; Mutual adaptation, complementarity and co-learning.

• Design Evaluation: Prototyping, Mixed methods, Ethics.

• Human Factors Theories: Memory, Learning, Emotion, Trust, Work Load, Situation Awareness, Behavior Change.

The final grade of the course consists of the following components:

• Presentations (weighting 15%)

• Prototype (weighting 15%)

• Project report according to the prescribed format (weighting 60%)

• Individual reflection (weighting 10%)

Final grade calculation = 0.15 * Presentations + 0.15 * Prototype + 0.6 * Project report + 0.1 * Individual reflection

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A; Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Presentations: resit opportunity

• Prototype: re-submit deliverable

• Project report according to the prescribed format: re-submit deliverable

• Individual reflection: re-submit deliverable

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).