Recommender systems are a ubiquitous part of the online experience, yet their interfaces often appear as black boxes, making it difficult for users to understand, explore, and influence recommendations. Prior work has shown that interactive visualizations can improve transparency, trust, and user experience, but existing solutions are highly diverse and lack a unifying structure. In this paper, we present a design space for visual interfaces in recommender systems that captures recurring interface properties across WHY, WHAT, and HOW dimensions. Derived through a multi-stage empirical process, it was applied to deconstruct 58 visual recommender interfaces into 96 distinct views. Based on this design space, we further introduce an interactive exploration and analysis tool that enables users to search, filter, and compare interface solutions and to analyze recurring combinations of building blocks across challenges, data characteristics, and visualization and interaction techniques.

The use of Artificial Intelligence (AI) in creative practice has expanded rapidly, among other fields, in User Experience (UX) and User Interface (UI) design where AI systems are increasingly explored as tools to support design activities. In this poster, we present an exploratory study on human-AI interaction in UI prototyping involving five experienced professionals and five novice UX designers, analyzing their interaction with AI throughout the prototyping process. Our preliminary results provide insights into how designers with varying level of expertise engage with AI systems in high-fidelity prototyping, and how such interaction shapes the iterative design process. Our initial findings further include suggested analytical perspectives and point to future research directions.

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.

Recent years have brought immense progress in the development of AI technology. This broadened its application fields but also led to a surge of interest in many research domains and increasing significance of human-AI relations for the development of AI technology. This rapid growth and evolvement is reflected by the establishment of a great variety of terms, potentially leading to what is known as jingle and jangle fallacies. With our scoping review of the terminology used in scientific literature to describe human-AI relations and its evolvement over time (with 803 records screened, 658 finally included), we capture the variety and development of human-AI terminology in accordance with the shift from interaction to collaboration between humans and AI. We aim to raise awareness of these developments spanning over different research communities and provide a solid basis for future researchers and practitioners conducting complementary, cross-domain research. Our review comprises terminological, bibliometric and thematic analyses, e.g., reporting on the historical development of terms and term composition patterns, but also identifying key authors and publications, geographic distribution of relevant research, and elaborating on term conception and usage, and co-occurrences throughout the literature.

Recent years have brought immense advancements around development of Artificial Intelligence (AI) technology and its application. This has also led to a boost in interest on human-AI collaboration and co-creation. Specifically in the creative field, where it is usually not sufficient for an AI to solve given problems or produce deterministic output, this next-level interaction between humans and AI comes with huge potential but also major challenges, related to aspects such as role and power distribution, trust and reliance, or efficiency and effectiveness. In this paper, we present a pilot study on human-AI collaboration in three different creative fields (programming, marketing texting, and UI design), addressing User Experience, Technology Acceptance and, specifically, Perception of Collaboration. The study is based on a theoretical framework we derived from prior research through a focused, systematic literature review, and intended to raise research questions and identify related hypotheses informing future empirical work.

Design spaces are powerful conceptual frameworks for analyzing solutions within a problem domain. We provide an exploration and analysis tool to examine the underlying data collection, compare different solutions and analyze commonly used building blocks. The web-based tool is based on javascript and vue.js and is avaibable online at: https://ives.kwmhgb.at

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.

When designing the interface of a recommender system, interactive visualizations can be used to support challenges such as transparency and controllability, and thus increasing the user’s trust. In this paper, we propose a construction kit for visual recommender systems that can be used to construct new solutions or deconstruct existing approaches to identify and analyze key aspects.

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.

Hybrid Collaboration, where remote and co-located team members work together using different devices and tools, has already been trending in recent years (e.g., through globalization and international cooperation) but experienced a further boost since the outbreak of the COVID-19 pandemic. The reason behind this surge in hybrid practices is probably that the crisis revealed aspects of remote collaboration which proved functional and which many decision makers (in industry as well as academia) plan to retain for the future. Thus, hybrid collaboration is an extremely timely topic which should be further studied in the context of CSCW. One major CSCW-anchored concept that has most intensively been researched in co-located collaboration settings where it is usually inherently related to spatial aspects and proximity, is territoriality. Already work on territoriality in fully distributed, remote settings has shown that there are significant differences due to the characteristics of the scenario. In this paper, we focus on territoriality in hybrid settings where we identified a significant research gap, and present the results of a user study with 22 teams consisting of four people each (distributed across two locations at two different universities), collaborating on a problem-solving task. Our findings reveal that more dimensions and communication channels, in addition to space, might strongly impact territoriality and territorial behavior in hybrid collaboration. Besides classical spatial territories also auditory territories frequently emerged. In addition, visibility of and accessibility to certain territories need to be rethought. We discuss these novel findings also regarding their interplay with earlier ones and derive design implications for CSCW systems supporting hybrid collaboration.

In recent years, research on collaborative interaction has relied on manual coding of rich audio/video recordings. The fine-grained analysis of such material is extremely time-consuming and labor-intensive. This is not only difficult to scale, but, as a result, might also limit the quality and completeness of coding due to fatigue, inherent human biases, (accidental or intentional), and inter-rater inconsistencies. In this paper, we explore how recent advances in machine learning may reduce manual effort and loss of information while retaining the value of human intelligence in the coding process. We present ACACIA (AI Chain for Augmented Collaborative Interaction Analysis), an AI video data analysis application which combines a range of advances in machine perception of video material for the analysis of collaborative interaction. We evaluate ACACIA's abilities, show how far we can already get, and which challenges remain. Our contribution lies in establishing a combined machine and human analysis pipeline that may be generalized to different collaborative settings and guide future research.

In the field of human–computer interaction, taxonomies are used to classify and describe interaction (i.e. input and output) modalities, methods, technologies and devices. However, so far, most of these taxonomies and classification schemes consider only a subset of modalities and related methods, often reducing them to vision, audition and touch. Additionally, they are usually either technology- or task- instead of human-centered and thus vulnerable to rapid outdating with technological advancement and the advent of novel sensor and actor technologies. To tackle both problems, we propose a novel taxonomy that has been designed around the human and the human capabilities to sense output from and provide input to computer systems. We argue that although knowledge about the human sensory system might be up to changes as well, this process is considerably slower compared to technological advancement. Further, we reduce the taxonomy to what humans can actively and consciously sense or produce which is why novel findings related to human perception might not immediately compromise its validity. This article motivates the need for a novel taxonomy in the light of how computers and humans are able to perceive each other. It discusses existing taxonomies and introduces the new one that is intended to be (i) centered around the human and (ii) as holistic and timeless as possible. Further, the new taxonomy was evaluated with six human–computer interaction experts with regards to its practical use for researchers and different application scenarios. We present and discuss a new taxonomy for input/output modalities and devices that is based on humans' capabilities to provide input to and perceive output from computer systems. The taxonomy is rooted in the physiological and cognitive characteristics of the human body. The taxonomy can be used by researchers but also practitioners As a human-centered and (nearly) holistic overview of interaction modalities and categories of devices For the classification of interaction modalities For the description of interaction devices The taxonomy has been qualitatively evaluated with six HCI experts.

We present Domino, a descriptive framework for hybrid collaboration and hybrid coupling styles in partially distributed teams. Domino enables researchers to describe, analyze, and understand real-world hybrid collaboration practices, i.e., collaborative practices that involve simultaneous co-located and remote collaboration with phases of both synchronous and asynchronous work that spans multiple groupware applications and devices. It also helps to categorize collaborative activities based on yet undocumented hybrid coupling styles between the members of multiple partially distributed or co-located subgroups. Our Domino framework was derived from initial observations of real-world practice and refined by the detailed analysis of participants' behavior and working styles during a simulation of a complex hybrid collaboration task with six partially distributed teams of four users in our lab. The resulting framework allows researchers to view collaboration through a new analytical lens, use new analytical tools, and also derive implications for the design of collaborative tools.

The urge to collaborate is deeply anchored in human nature. This is substantiated by evidence for different forms of group work already performed by early humans (e.g., when defending against threats). Over time, the behavior of working together, the motivation behind and the technological support have changed which also broadened the spectrum of collaborative activities. Especially today, on the brink of the ubiquity era that envisions a person to use thousands of computers everyday, new pre-conditions for co-located and remote, planned and spontaneous collaboration are manifested. Thus, previous models that distinguished between phases of collaboration such as starting, planning, executing and reporting might become less applicable. The relevance of the emergence of collaboration phase, which has been considered only marginally so far, is likely to increase drastically. This position paper discusses considerations on the nature of collaboration throughout the ubiquity era and suggests an accordingly extended phases of collaboration model.

The CMIS workshop proposes to bring together researchers who are interested in improving collaborative experiences through the combination of multiple interaction surfaces with diverse sizes and formats, ranging from large-scale walls, to tables, mobiles, and wearables. The opportunities for innovation exist, but the ISS, CHI, CSCW, and other HCI communities have not yet thoroughly addressed the problem of bringing effective collaboration activities together using multiple interactive surfaces, especially in complex work domains. Of particular interest is the potential synergy that one can obtain by effectively combining different-sized surfaces and sharing information between devices.

People with motor and/or cognitive impairments usually face several challenges when interacting with a computer. Often they have to rely on special and in most cases expensive hardware devices or can only use a limited range of functions provided by the device. The IAAA project aims at analyzing and modeling users' interaction capabilities in a fine-grained way in order to automatically determine the optimum interaction modalities and devices for the individual user. Later, this information will be used to adapt both user interfaces and interaction modalities to users' needs. IAAA utilizes standard hardware like common smartphones to lower the barriers for people with disabilities and subsequently extends its scope to non-impaired users. This paper focuses mainly on selected user model features that are used to represent a user's individual interaction capabilities. Further, it presents the results of a related initial study.

Since a couple of years tablets are gaining ever increasing markets share, so that desktop PCs are slowly losing their dominant role. People with disabilities are also attracted by tablets but they are sometimes challenged by the multi-touch technology itself and the natural interfaces of the provided applications.