Description

Problem: 

Data-driven Digital Twins are increasingly used in complex decision-making environments. Humans can significantly enhance Digital Twins operations through domain knowledge and cognitive and physical capabilities. However, in many current implementations of Digital Twins, human integration remains limited or is implemented in a non-systematic manner. While research in Human-Computer Interaction (HCI) offers numerous interaction concepts (e.g., decision support, mixed-initiative systems, explainable interfaces), it is unclear how these approaches are applied in Digital Twin contexts. This gap limits our understanding of how human involvement in Digital Twins can be conceptualized and implemented according to the current state of research and practice.

Goal: 

The goal of this thesis is to conduct a systematic literature review to identify, classify, and analyze interaction patterns and design principles for human integration in Digital Twins.
Examples for interaction patterns include best practices in UI/UX design for Digital Twins. Design principles for example are concepts such as explainability, mixed-initiative interaction, knowledge crowdsourcing, or gamification.
Based on this, the student will derive as an outcome a structured overview of interaction patterns and design principles and assess their suitability for different types of human-supported Digital Twin scenarios, considering criteria such as transparency, controllability, usability, cognitive workload, and operational support.
 

Required Skills and Knowledge: 

• Interest in Digital Twins and UI Design/Development.

• Basic Knowledge on Human-Computer Interactions.

• Basic understanding of scientific literature reviews.

• Basic knowledge of simulation systems is beneficial but not required.