Ineffective meetings are pervasive. Thinking ahead explicitly about meeting goals may improve effectiveness, but current collaboration platforms lack integrated support. We tested a lightweight goal-reflection intervention in a preregistered field experiment in a global technology company (361 employees, 7196 meetings). Over two weeks, workers in the treatment group completed brief pre-meeting surveys in their collaboration platform, nudging attention to goals for upcoming meetings. To measure impact, both treatment and control groups completed post-meeting surveys about meeting effectiveness. While the intervention impact on meeting effectiveness was not statistically significant, mixed‑methods findings revealed improvements in self‑reported awareness and behaviour across both groups, with post‑meeting surveys unintentionally functioning as an intervention. We highlight the promise of supporting goal reflection, while noting challenges of evaluating and supporting workplace reflection for meetings, including workflow and collaboration norms, and attitudes and behaviours around meeting preparation. We conclude with implications for designing technological support for meeting intentionality.

Peer Reviewed

Meetings often suffer from a lack of intentionality, such as unclear goals and straying off-topic. Identifying goals and maintaining their clarity throughout a meeting is challenging, as discussions and uncertainties evolve. Yet meeting technologies predominantly fail to support meeting intentionality. AI-assisted reflection is a promising approach. To explore this, we conducted a technology probe study with 15 knowledge workers, integrating their real meeting data into two AI-assisted reflection probes: a passive and active design. Participants identified goal clarification as a foundational aspect of reflection. Goal clarity enabled people to assess when their meetings were off-track and reprioritize accordingly. Passive AI intervention helped participants maintain focus through non-intrusive feedback, while active AI intervention, though effective at triggering immediate reflection and action, risked disrupting the conversation flow. We identify three key design dimensions for AI-assisted reflection systems, and provide insights into design trade-offs, emphasizing the need to adapt intervention intensity and timing, balance democratic input with efficiency, and offer user control to foster intentional, goal-oriented behavior during meetings and beyond.

Avatars are edging into mainstream videoconferencing, but evaluation of how avatar animation modalities contribute to work meeting outcomes has been limited. We report a within-group videoconferencing experiment in which 68 employees of a global technology company, in 16 groups, used the same stylized avatars in three modalities (static picture, audio-animation, and webcam-animation) to complete collaborative decision-making tasks. Quantitatively, for meeting outcomes, webcam-animated avatars improved meeting effectiveness over the picture modality and were also reported to be more comfortable and inclusive than both other modalities. In terms of avatar satisfaction, there was a similar preference for webcam animation as compared to both other modalities. Our qualitative analysis shows participants expressing a preference for the holistic motion of webcam animation, and that meaningful movement outweighs realism for meeting outcomes, as evidenced through a systematic overview of ten thematic factors. We discuss implications for research and commercial deployment and conclude that webcam-animated avatars are a plausible alternative to video in work meetings.

Effective meetings are effortful, but traditional videoconferencing systems offer little support for reducing this effort across the meeting lifecycle. Generative AI (GenAI) has the potential to radically redefine meetings by augmenting intentional meeting behaviors. CoExplorer, our novel adaptive meeting prototype, preemptively generates likely phases that meetings would undergo, tools that allow capturing attendees’ thoughts before the meeting, and for each phase, window layouts, and appropriate applications and files. Using CoExplorer as a technology probe in a guided walkthrough, we studied its potential in a sample of participants from a global technology company. Our findings suggest that GenAI has the potential to help meetings stay on track and reduce workload, although concerns were raised about users’ agency, trust, and possible disruption to traditional meeting norms. We discuss these concerns and their design implications for the development of GenAI meeting technology.

Hybrid meetings limit inclusion for remote participants. The Hybridge experimental system provides different interfaces for remote and room endpoints, focusing on improving inclusion via shared spatiality and remote agency. In-room participants see remotes on displays around a table, and remotes see video integrated into a digital twin. Remotes can choose where to appear and from where they view the room. We tested Hybridge in a within-subjects study of group survival tasks. An in-person condition was followed by a counterbalanced order of hybrid traditional videoconferencing ("Gallery") and Hybridge. We found that co-presence and agency differences between in-room and remotes were alleviated in Hybridge but remained in Gallery. Physical presence for remotes was higher in Hybridge than Gallery. Conversation flow was better in Hybridge than Gallery, but ease of awareness was not different. We argue that asymmetry should be embraced when designing hybrid meeting systems, with inclusivity achieved by tailoring features for the needs of different endpoints.

Via Research through Design (RtD), we explore the potential of headphones as a general-purpose input device for both foreground motion-gestures as well as background sensing of user activity. As a familiar wearable device, headphones offer a compelling site for head-situated interaction and sensing. Using emerging sensing modalities such as inertial motion, capacitive touch sensing, and depth cameras, our implemented prototypes explore sensing and interaction techniques that offer a range of compelling capabilities.

User studies play a critical role in human subject research, including human-computer interaction. Virtual reality (VR) researchers tend to conduct user studies in-person at their laboratory, where participants experiment with novel equipment to complete tasks in a simulated environment, which is often new to many. However, due to social distancing requirements in recent years, VR research has been disrupted by preventing participants from attending in-person laboratory studies. On the other hand, affordable head-mounted displays are becoming common, enabling access to VR experiences and interactions outside traditional research settings. Recent research has shown that unsupervised remote user studies can yield reliable results, however, the setup of experiment software designed for remote studies can be technically complex and convoluted. We present a novel open-source Unity toolkit, RemoteLab, designed to facilitate the preparation of remote experiments by providing a set of tools that synchronize experiment state across multiple computers, record and collect data from various multimedia sources, and replay the accumulated data for analysis. This toolkit facilitates VR researchers to conduct remote experiments when in-person experiments are not feasible or increase the sampling variety of a target population and reach participants that otherwise would not be able to attend in-person.

Researchers have explored using VR and 3D data visualizations for analyzing and presenting data for several decades. Surveys of the literature in the field usually adopt a technical or systemic lens. We propose a survey of the Immersive Analytics literature from the user’s perspective that relates the purpose of the visualization to its technical qualities. We present our preliminary review to describe how device technologies, kinds of representation, collaborative features, and research design have been utilized to accomplish the purpose of the visualization. This poster demonstrates our preliminary investigation, inviting feedback from the VRST community. Our hope is the final version of our review will benefit designers, developers, and practitioners who want to implement immersive visualizations from a Human-Centered Design perspective, and help Immersive Analytics researchers get a better understanding of the gaps in current literature.