Large Language Models (LLMs) aim to mimic a natural form of human conversation, likely contributing to an anthropomorphic perception of AI in contrast to conventional human-computer interfaces. Our study explores human-AI conversations and humans’ perception of their counterpart in a collaborative mystery solving task with Anthropic’s Claude 3.5 Sonnet v2 model. We collected self-report data on participants’ perception of the interaction, measured task performance, and analyzed conversational dynamics using LLM-based emotion coding. We found that humans’ perception of AI, ranging from that of a teammate or colleague to a tool, did not necessarily impact performance in mystery solving, but correlated with aspects of the interaction itself. When participants perceived the AI as a teammate or colleague, they felt a stronger sense of team cohesion and their conversations were more collaborative, with more positive emotions. These findings may help practitioners design human-AI interfaces that foster positive interactions without endangering performance.

In recent years, hybrid work has surged globally, making Hybrid Collaboration Spaces—the physical and virtual environments facilitating hybrid collaboration—increasingly relevant. However, this aspect is often not addressed in research and its scientific discourse is not always obviously apparent. In the existing body of literature, there are terminological ambiguities as well as temporal trends which make it hard to quickly identify relevant research. We thus present a systematic literature review on Hybrid Collaboration Spaces based on seven academic data sources (90 publications included, 996 screened). Our review encompasses several scientific fields and domains, contributing a detailed bibliometric report and a thorough thematic analysis highlighting challenges in hybrid collaboration and requirements for related spaces. We aim to 1) provide a concise overview of the research landscape, 2) advance the cross-domain dialog on hybrid collaboration, and 3) contribute to the disambiguation of terminology and establishment of a shared vocabulary.

Since 2020, worldwide COVID-19-related lockdowns have led to a rapid increase of remote collaboration, particularly in the domain of knowledge work. This has undoubtedly brought challenges (e.g., work-life boundary management, social isolation), but also opportunities. Practices that have proven successful (e.g., through increased task performance, efficiency or satisfaction) are worth retaining in future. In this qualitative empirical study, we analyzed four teams’ (14 participants in total) mandatory remote collaboration over a period of several days to several months during a nationally imposed lockdown. We report results derived from questionnaires, logbooks, group interviews, and meeting recordings. We identify possible factors influencing quality of task outcome as well as subjective aspects like satisfaction, motivation, and team atmosphere. As a basis for our conclusions, we provide a scheme for categorizing effects of remote collaboration based on an exhaustive literature review on pandemic-induced mandatory remote work and collaboration.

Following the vision of Industry 4.0, manufacturers started to break down products into subsystems, each of which is developed and supplied by different suppliers to tackle the increasing complexity of product development (e.g., in vehicle, machine or plant construction). This process needs to be supported by intuitive Computer-Aided Design (CAD) tools and virtual modeling and simulation environments to effectively meet the challenges of increasingly complex product development. Therefore, in this paper, we present some outcoming of a research project called DisMoSim (Distributed Modeling and Simulation), which proposes an intuitive virtual environment for modeling and simulation that embeds a Multi User Inverse Kinematic Solver (MUKS). The MUKS allows (i) individual subsystems to be tested intuitively, i.e., Rapid Testing (RT), and (ii) people not yet familiar with a subsystem to understand it easily and quickly through simple kinematic actions, i.e., Rapid Understanding (RU), without unnaturally interrupting the modeling process. Finally, (iii) the MUKS enables subsystems to be easily assembled into complete systems, i.e., Rapid Assembling (RA), by means of a so-called snapping-point mechanism which supports the complex process of aligning and assembling different subsystems. To evaluate these capabilities, we present the results of a user study conducted with 20 participants. Special attention has been given to the embedded MUKS in contrast to existing approaches known from common CAD and simulation software. Thus, we demonstrate the practicability, usability, and versatility of our approach.