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.

The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices.We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so.Participants shared 936 first-hand examples of using GenAI in work tasks.Quantitatively, when considering both task-and user-specific factors, a user's task-specific self-confidence and confidence in GenAI are predictive of whether critical thinking is enacted and the effort of doing so in GenAI-assisted tasks.Specifically, higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking.Qualitatively, GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship.Our insights reveal new design challenges and opportunities for developing GenAI tools for knowledge work.

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.

Ineffective meetings due to unclear goals are major obstacles to productivity, yet support for intentionality is surprisingly scant in our meeting and allied workflow technologies. To design for intentionality, we need to understand workers’ attitudes and practices around goals. We interviewed 21 employees of a global technology company and identified contrasting mental models of meeting goals: meetings as a means to an end, and meetings as an end in themselves. We explore how these mental models impact how meeting goals arise, goal prioritization, obstacles to considering goals, and how lack of alignment around goals may create tension between organizers and attendees. We highlight the challenges in balancing preparation, constraining scope, and clear outcomes, with the need for intentional adaptability and discovery in meetings. Our findings have implications for designing systems which increase effectiveness in meetings by catalyzing intentionality and reducing tension in the organisation of meetings.

Generative AI (GenAI) systems offer unprecedented opportunities for transforming professional and personal work, yet present challenges around prompting, evaluating and relying on outputs, and optimizing workflows. We argue that metacognition—the psychological ability to monitor and control one’s thoughts and behavior—offers a valuable lens to understand and design for these usability challenges. Drawing on research in psychology and cognitive science, and recent GenAI user studies, we illustrate how GenAI systems impose metacognitive demands on users, requiring a high degree of metacognitive monitoring and control. We propose these demands could be addressed by integrating metacognitive support strategies into GenAI systems, and by designing GenAI systems to reduce their metacognitive demand by targeting explainability and customizability. Metacognition offers a coherent framework for understanding the usability challenges posed by GenAI, and provides novel research and design directions to advance human-AI interaction.

Workplace meetings are vital to organizational collaboration, yet relatively little progress has been made toward measuring meeting effectiveness and inclusiveness at scale. The recent rise in remote and hybrid meetings represents an opportunity to do so via computer-mediated communication (CMC) systems. Here, we share the results of an effective and inclusive meetings survey embedded within a CMC system in a diverse set of companies and organizations. We correlate the survey results with objective metrics available from the CMC system to identify the generalizable attributes that characterize perceived effectiveness and inclusiveness in meetings. Additionally, we explore a predictive model of meeting effectiveness and inclusiveness based solely on objective meeting attributes. Lastly, we show challenges and discuss solutions around the subjective measurement of meeting experiences. To our knowledge, this is the largest data-driven study conducted after the pandemic peak to measure, understand, and predict effectiveness and inclusiveness in real-world meetings at an organizational scale.

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.

Hybrid video calls include attendees in a conference room with loudspeakers and remote attendees using headsets, each with different options for rendering sound spatially. Two studies explored the listener experience with spatial audio in video calls. One study examined the in-room experience using loudspeakers, comparing among spatialization algorithms spreading voices out horizontally. A second study compared varying degrees of horizontal separation of binaurally rendered voices for a remote participant using a headset. In-room participants preferred the widest spatialization over monophonic, stereo, and stereo-binary audio in metrics related to intelligibility and helpfulness. Remote participants preferred different widths of the audio stage depending on the number of voices. In both studies, rendering sound spatially increased performance in speech stream identification. Results indicate spatial audio benefits for in-room and remote attendees in video calls, although the in-room attendees accepted a wider audio stage than remote users.