Coaching has become an important didactic tool for reflecting learning processes in higher education. Digital media and AI-based technologies such as chatbots can support stimulating self-coaching processes. For the use case of student coaching on the topic of exam anxiety, the working alliance between a coaching chatbot and a human coachee is investigated. Two coachbot interaction methods are compared: A click-based chatbot (implemented in a rule-based system), where the coachee can only click on one answer, and a writing-based chatbot (implemented in a conversational AI), which allows the coachee to freely type in their answers. The focus is on which coachbot interaction method enables a stronger working alliance between coach and coachee: a click-based or a writing-based chatbot. The working alliance and the technical realization of the chatbot systems were investigated in an exploratory quantitative study with 21 engineering students. The results indicate that the working alliance in both study conditions can be classified as medium to high overall. The results further show higher values for bonding on a writing-based platform than when using a click-based system. However, click-based systems seem to be more helpful as a low-threshold entry point to coaching, as they guide coachees better through the process by providing predefined answers. An evaluation of the technical realization shows that self-reflection processes through digital self-coaching via chatbot are generally well accepted by students. For further development and research, it is therefore recommendable to develop a “mixed” coachbot that allows interaction via clicking as well as via free writing.

As the population ages, the demand for care for older adults is increasing. To maintain their independence and autonomy, even with declining health, assistive technologies such as connected medical devices or social robots can be useful. In previous work, we introduced a novel health monitoring system that combines commercially available products with apps designed specifically for older adults. The system is intended for the long-term collection of subjective and objective health data. In this work, we present an exploratory user experience (UX) and usability study we conducted with older adults as the target group of the system and with younger expert users who tested our system. All participants interacted with a social robot conducting a health assessment and tested sensing devices and an app for data visualization. The UX and usability of the individual components of the system were rated highly in questionnaires in all sessions. All participants also said they would use such a system in their everyday lives, demonstrating the potential of these systems for self-managing users’ health. Finally, we found factors such as previous experience with social robots and technological expertise to have an influence on the reported UX of the users.

Socially interactive agents are gaining prominence in domains like healthcare, education, and service contexts, particularly virtual agents due to their inherent scalability. To facilitate authentic interactions, these systems require verbal and nonverbal communication through e.g., facial expressions and gestures. While natural language processing technologies have rapidly advanced, incorporating human-like nonverbal behavior into real-world interaction contexts is crucial for enhancing the success of communication, yet this area remains underexplored. One barrier is creating autonomous systems with sophisticated conversational abilities that integrate human-like nonverbal behavior. This paper presents a distributed architecture using Epic Games' MetaHuman, combined with advanced conversational AI and camera-based user management, that supports methods like motion capture, handcrafted animation, and generative approaches for nonverbal behavior. We share insights into a system architecture designed to investigate nonverbal behavior in socially interactive agents, deployed in a three-week field study in the Deutsches Museum Bonn, showcasing its potential in realistic nonverbal behavior research.