Speculative and systemic design are both used by HCI researchers to engage in complex sociotechnical change. However, they are rarely integrated in ways that make their complementary strengths explicit. This paper introduces Systemic Futures Dialogue, a design approach that interleaves speculative and systemic design methods across micro-macro and present-future dimensions. We report on an 18-month case study with the Architecture, Engineering, and Construction (AEC) industry, that applied a mixture of methods used in both design disciplines. These included semi-structured interviews, systems mapping, future-based scenarios, speculative probes, and participatory reflection. The resulting design approach generates grounded futures by connecting macro-level system dynamics with micro-level speculative critique, identifying tensions between present-day solutions and desired futures. The final Systemic Futures Dialogue contributes methodological guidance for conducting critical, participatory design work within a sociotechnical system.

As multi-agent Large Language Models (LLMs) gain traction, designers must consider how to surface their internal reasoning in ways that foster appropriate trust. We present a design-led, qualitative, comparative structured observation study, exploring how users interpret and evaluate transparency in multi-agent LLMs. Participants interacted with five interface variants, each instantiating different combinations of transparency-related design dimensions, across two task types: information-seeking and logical reasoning. We surface participants’ mental models, the cues they interpret as signals of transparency and trustworthiness, and how they weigh the costs and benefits of increasing process visibility. Transparency needs were dynamic and context-sensitive, with the ideal “Goldilocks” (i.e., “just right” transparency) level shaped jointly by task demands, interface affordances, and user characteristics such as task expertise and dispositional AI trust. We highlight tensions between process visibility, information sufficiency, and cognitive effort, and synthesise these insights into design considerations for aligning transparency with user needs in future multi-agent LLM interfaces.

When distributed mixed reality (MR) systems map physical spaces to enable co-presence for local and remote collaborators, they can unintentionally disrupt the sociocultural norms that give actions their meaning. For instance, a participant sitting at their own desk may be rendered as occupying their collaborator’s desk, inadvertently signalling an invasion of personal space. This paper examines the design tension between spatial information and sociocultural norms through a qualitative counterfactual cards activity with 20 participants, probing how they navigate these trade-offs across different collaborative contexts. Our findings show similarities between Expectancy Violations Theory (EVT) and the factors participants assess when they decide to uphold accurate spatial information or sociocultural norms during collaboration in MR. However, there were some departures from EVT, which can be used to inform the development of MR-specific theories in the future.

Collaborative Mixed Reality (MR) enables embodied meetings for distributed collaborators working across a variety of locations. However, providing a coherent experience for all users regardless of the spatial configurations of their respective physical environments is a central challenge. We present the Spatial Heterogeneity Framework, which breaks the problem into four core components: the activity zones, heterogeneity ladder, blended proxemics, and MR solutions matrix. We explain the interplay between these components, demonstrating their interconnectivity via a case study. Our framework enables researchers to navigate differences and trade-offs between solutions for distributed MR collaboration. It also supports designers to think about the role of space, technology, and social behaviours in MR collaboration. Ultimately, our contributions advance the field by conceptualising the challenges of spatial heterogeneity and strategies to overcome them.

Collaboration in distributed mixed reality (MR) creates co-presence illusions by bringing a digital representation of the remote collaborator into the local user’s physical space. However, constraints in each physical environment, such as different spatial layouts and furniture, can hinder collaboration and momentarily break the illusion of co-location. Since the remote user’s physical space is invisible to the local user, the source of the interaction problem is often imperceptible, leading to familiar real-world interactions suddenly feeling unfamiliar—a concept we term the Jamais Vu Effect. In this paper, we conceptualise and demonstrate this effect through a five-part user study that elicits the user experience of the Jamais Vu Effect in MR. We contribute a shared vocabulary and understanding of spatial interface design challenges to help designers and researchers discuss and improve distributed collaboration in MR.

Collaborating across dissimilar, distributed spaces presents numerous challenges for computer-aided spatial communication. Mixed reality (MR) can blend selected surfaces, allowing collaborators to work in blended f-formations (facing formations), even when their workstations are physically misaligned. Since collaboration often involves more than just participant pairs, this research examines how we might scale MR experiences for large-group collaboration. To do so, this study recruited collaboration designers (CDs) to evaluate and reimagine MR for large-scale collaboration. These CDs were engaged in a four-part user study that involved a technology probe, a semi-structured interview, a speculative low-fidelity prototyping activity and a validation session. The outcomes of this paper contribute (1) a set of collaboration design principles to inspire future computer-supported collaborative work, (2) eight collaboration patterns for blended f-formations and collaboration at scale and (3) theoretical implications for f-formations and space-place relationships. As a result, this work creates a blueprint for scaling collaboration across distributed spaces.