Our interdisciplinary longitudinal study investigates the evolving dynamics of human-AI interaction over six waves spanning one year. By examining individual, behavioral, and task-related variables, the project aims to uncover how users' trust in, perceptions of, self-efficacy, and willingness to engage with AI systems develop and interrelate over time. The insights gained from this research are essential for better understanding human-machine interaction, a critical foundation for fostering effective collaboration between users and AI systems. This knowledge will inform user-centered AI design and guide the ethical integration of these technologies into various aspects of everyday life.
The coronavirus crisis has once again demonstrated the importance of supporting learning in schools through digital technologies. Far beyond the use of digital platforms to distribute assignments to students, digital technologies enable the tracking of individual student learning and the provision of targeted support tailored to individual needs. This research project investigates the extent to which data derived from student interactions with digital technologies in mathematics and science classrooms can be used to 1) continuously evaluate individual student learning, 2) reconstruct learning pathways across sequences of learning activities, and 3) identify those pathways associated with the development of competencies in mathematics and science.
A glance at the news makes it clear: All over the world, people are taking to the streets for various reasons. Their passionate commitment to (or against) a cause is often the result of social influence, which today often occurs via emotionalised online communication. But what exactly motivates people to participate in demonstrations, sign petitions, and otherwise engage collectively?
News about world events, as well as a look at recent history, often show: Passionate protests are important drivers of social change and undertaken for a wide variety of causes. This type of joint action, directed toward a shared goal, is known as collective action. Emotions play a central role in this behavior. But when do which emotions emerge? And how do they foster collective action?
How do we obtain scientific information? Who do we get it from? What if artificial intelligence could provide us with complicated topics and technical information in an easily understandable way? This research project investigates how laypeople perceive and evaluate intelligent language assistants who communicate scientific information. In particular, it will explore how different textual representations of automated content affect the acceptance and reception of scientific knowledge.
Robots and artificial intelligence (AI) are increasingly serving as collaborative partners for humans. Text-based chatbots, which enable humans to communicate with a technical system through natural language, have gained popularity in a variety of application areas. So far, little is known about the processes involved in teams consisting of humans and AI. Therefore, the focus of this dissertation project is to explore relationships between team composition and team performance and dynamics.
In this research project we examine how different forms of presenting factual information influence people’s knowledge about and attitudes toward foxes. In particular, the project deals with the impact of different forms of visual and textual representations. It examines whether emotionalization through visual methods has a similar effect as emotionalization mediated by textual representations.
Sequential collaboration describes a knowledge construction process often found in online collaborative projects such as Wikipedia. In this process a contributor starts by creating an entry which is sequentially adjusted or maintained by following contributors. Based on the results of a previous project, this project examines the gathering information and making decisions based on these information in sequential collaboration compared to widely used group discussions to gain further insights into the construction process of collaborative knowledge.
According to numerous studies graduate students face difficulties in finishing their dissertations. Among the factors that make it difficult to produce the preliminary or final work are the difficulties encountered by students in reading and writing practices, and dealing with the task on their own, generally performed in isolation and without didactic support. In this project, the use of digital technologies in the preparation of dissertations will be examined in this context.
Research knowledge can be disseminated in various ways. Nowadays, the focus is often placed on digital media. However, digital media as a source of knowledge come with a number of challenges. In view of these, this project investigates within the framework of the "Metavorhaben Digitalisierung im Bildungsbereich II" the transfer of scientific findings on the use of digital media in education via the internet with a user-centered focus on the trustworthiness of knowledge sources.
The aim of the project is to develop sustainable effective curriculum-accompanying innovative teaching formats with content that can be dynamically adapted to different conditions and developments for students of medicine and medical-related life science courses, which serve to impart theoretical and practical AI knowledge at different levels (basics, in-depth studies, applications) with socially relevant questions on ethics, law, privacy, transparency etc. TüKITZ Med is intended to teach basic concepts and methods of artificial intelligence competently and effectively to students who are not familiar with AI.
With the spread of the Internet and the emerging of collaborative online projects such as Wikipedia and OpenStreetMap, collaboration in groups also changed radically. Instead of in-person groups sharing information and making decisions together, contributors in sequential collaboration form a sequential chain in which the first contributor creates an entry independently which can be edited and improved or maintained by following contributors. This project focuses whether contributors generate accurate estimates in sequential collaboration and which conditions foster or hinder this process.
Scientific findings are becoming increasingly important. However, it is often difficult for many people to interpret and understand these findings. This is also due to the fact that the scientific process of knowledge gain has received little attention so far. Therefore, the aim of the VideT project is to develop a video-based transfer tool in order to communicate the empirical scientific research process to the public and test it in student laboratories.
