The science and technology world is stuck in an unresolved debate: can technologies ever be conscious? While this question dominates discussions in neuropsychology and cognitive science, it is ultimately unproductive for Human-Computer Interaction (HCI). As users perceive a level of consciousness in commercially available systems, there is a need to understand the factors that lead to a perception of consciousness so that we account for consciousness in designing interactive systems. To that end, we systematically develop the Precursors of Perceived Consciousness in Interactive Systems Scale—a validated instrument for quantifying the design qualities that contribute to perceived consciousness in a technology. Through reporting on a structured scale development process, we show how the PreCoS enables studies of technologies potentially perceived as conscious. PreCoS is the first step towards the systematic study and design of systems which responsibly integrate perceived consciousness: fostering positive, engaging experiences while recognising and mitigating ethical risks such as overtrust, bias, or psychological discomfort.

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.

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.

Whether it is sleep, diet, or procrastination, changing behaviours can be challenging. Individuals could design and build their own personalised digital interventions to help them reach their goals, but little is known about this process. Building upon previous research we propose the Behaviour Change with Trigger-Action Programming (BC-TAP) model which describes how individuals could bridge the gap between their current and desired behaviour through the creation of 'Do-It-Yourself' (DIY) digital interventions. We conducted a two-day participatory workshop based on the BC-TAP model with 28 participants. Participants articulated plans to change a behaviour of their choice and represented these plans in mobile device automations. After using their interventions for up to three weeks, participants reflected on their experience. Our findings report opportunities and challenges at each stage of the process. While formulating a digital proxy for certain behaviours was challenging, both failures and successes facilitated participants' awareness of their behaviour, and their ability to change it.

The prospect of machine consciousness cultivates controversy across media, academia, and industry. Assessing whether non-experts perceive technologies as conscious, and exploring the consequences of this perception, are yet unaddressed challenges in Human Computer Interaction (HCI). To address them, we surveyed 100 people, exploring their conceptualisations of consciousness and if and how they perceive consciousness in currently available interactive technologies. We show that many people already perceive a degree of consciousness in GPT-3, a voice chat bot, and a robot vacuum cleaner. Within participant responses we identified dynamic tensions between denial and speculation, thinking and feeling, interaction and experience, control and independence, and rigidity and spontaneity. These tensions can inform future research into perceptions of machine consciousness and the challenges it represents for HCI. With both empirical and theoretical contributions, this paper emphasises the importance of HCI in an era of machine consciousness, real, perceived or denied.

Reflecting on personal challenges can be difficult. Without encouragement, the reflection process often remains superficial, thus inhibiting deeper understanding and learning from past experiences. To allow people to immerse themselves in and deeply reflect on past challenges, we developed SelVReflect, a VR experience which offers active voice-based guidance and a space to freely express oneself. SelVReflect was developed in an iterative design process (N=5) and evaluated in a user study with N=20 participants. We found that SelVReflect enabled participants to approach their challenge and its (emotional) components from different perspectives and to discover new relationships between these components. By making use of the spatial possibilities in VR, participants developed a better understanding of the situation and of themselves. We contribute empirical evidence of how a guided VR experience can support reflection. We discuss opportunities and design requirements for guided VR experiences that aim to foster deeper reflection.