Despite the importance of the Information Technology (IT) sector for sustainability, there is a lack of guidance for including Education for Sustainable Development (ESD) in IT degree programs. This research investigates perspectives of staff, students and industry, and contextual factors, to develop strategies for embedding sustainability in IT curricula. We conducted a case study at an IT department in a major Australian university, through semi-structured interviews and workshops with academic staff (n=10), students (n=28), and industry professionals (n=5), and by analysing documentation such as university plans, handbooks and company reports. A thematic analysis was used to synthesise perspectives, and sustainability topics were mapped to IT knowledge areas. While IT professionals have increasingly important roles in addressing sustainability challenges, many IT staff and students are unaware of, or uncertain about, sustainability’s relevance to their field. Additional barriers to integrating sustainability into IT programs include lack of educator expertise, overcrowded curricula, and the lack of tailored resources and support. We provide an empirical account of stakeholder perspectives on integrating sustainability into IT curricula in Australia. We offer a framework and recommendations to address these issues through loose, program-level coordination, training and resources to empower educators, paving the way for more profound change. This case study offers valuable insights for integrating sustainability into IT curricula, along with a framework and strategies that can serve as useful starting points for universities to take practical, educator-led steps towards ESD.

Human–robot collaboration faces a speed–accuracy trade-off (SAT): higher speed lowers latency but increases errors; lower speed improves accuracy but extends waiting time. Both pathways can frustrate users in research and real-world deployments. Despite this importance, the impact of SAT on frustration and how to mitigate it remains underexplored. We conducted a user study (N=24) in which participants collaborated with a robot in an assembly task. We investigate three levels of SAT (conservative, moderate, risky), and examine how uncertainty communication and offering decision autonomy affect user frustration. Our results show that user frustration is highest for risky robot behavior. User involvement decreased frustration for risky behaviors, but increased it for conservative ones, while verbal uncertainty communication had no effects. We further found that perceived transparency, agency, intelligence, and utility of the robot increase with conservative SAT, with user workload decreasing. We propose that user involvement is advisable in higher-risk settings to mitigate user frustration, whereas autonomous operation is preferable in lower-risk scenarios.

Figure 1: Exemplified experimental setting for air pollution: participants were given a GUI to manipulate the different levels of particulate matter by moving a slider.The respective information was conveyed through the physical humanlike agent placed next to them.

Virtual AI-generated Influencers (VAIIs) are increasingly being used by corporations and public agencies, raising questions about how their visual design and communication strategies impact end-users’ propensity to accept the messages they deliver. We examined the impact of human-likeness (how closely a VAII resembles a human) and self-disclosure (whether the message contains personal information) on message acceptance, alongside dispositional factors like empathy and anthropomorphising tendencies. In a mixed-methods experiment, participants (N=120) watched short-form videos featuring VAIIs of varying human-likeness (High/Moderate-High/Moderate-Low/Low) and self-disclosure (present/absent). We observed the strongest message acceptance from the VAIIs with the lowest human-likeness, and message rejection for VAIIs with moderate to low human-likeness. Additionally, participants’ message acceptance was influenced by their empathy tendencies. Our qualitative analysis revealed further insights into participants’ perceptions of the human-likeness of VAIIs, their discomfort with self-disclosure, and their tendency to anthropomorphise VAIIs. These findings provide important implications for the design of VAIIs.

Humans naturally seek to identify causes behind outcomes through causal attribution, yet Human-AI research often overlooks how users perceive causality behind AI decisions. We examine how this perceived locus of causality—internal or external to the AI—influences trust, and how decision stakes and outcome favourability moderate this relationship. Participants (N=192) engaged with AI-based decision-making scenarios operationalising varying loci of causality, stakes, and favourability, evaluating their trust in each AI. We find that internal attributions foster lower trust as participants perceive the AI to have high autonomy and decision-making responsibility. Conversely, external attributions portray the AI as merely “a tool” processing data, reducing its perceived agency and distributing responsibility, thereby boosting trust. Moreover, stakes moderate this relationship—external attributions foster even more trust in lower-risk, low-stakes scenarios. Our findings establish causal attribution as a crucial yet underexplored determinant of trust in AI, highlighting the importance of accounting for it when researching trust dynamics.

Recent advances in multi-agentic systems (e.g., AutoGen, OpenAI Agents) allow users to interact with a group of specialised AI agents rather than a single general-purpose agent. Despite the promise of this new paradigm, the HCI community has yet to fully examine the opportunities, risks, and user-centred challenges it introduces. We contribute to research on multi-agentic systems by exploring their architectures and key features through a human-centred lens. While literature and use cases are still emerging, we build on existing tools and frameworks available to developers to identify a set of overarching challenges, e.g., orchestration and conflict resolution, that can guide future research in HCI. We illustrate these challenges through examples, present potential design considerations, and provide research opportunities to spark interdisciplinary conversation. Our work lays the groundwork for future exploration and offers a research agenda focused on user-centred design in multi-agentic systems.

“Sustainability −4- HRI, HRI −4-Sustainability” offers hands-on engagement with the HRI 2025 conference theme, “Robots for a Sustainable World”. This workshop will explore the relationship between HRI and sustainable development and stimulate discussion on how we can make our own research practices more sustainable. We propose a full-day workshop, featuring morning discussions with sustainability experts and activists, and an afternoon hands-on activity aimed at understanding how robotics research can help in creating sustainable futures. We will broadly answer the following questions: How can we, as robotic researchers, help address sustainable development responsibly? Equally, how can we ensure that our HRI research practices minimises ecological footprints and operates within ethical and sustainable frameworks? We envision two practical outcomes of this workshop: a “sustainability statement” that can be submitted together with future HRI research papers, and a paper gathering insights and reflections from the workshop. We welcome researchers and students at any career stage and from any subfield of HRI to attend and contribute.

Office Assistant Robots (OARs) offer a promising solution to proactively provide in-situ support to enhance employee well-being and productivity in office spaces. We introduce OfficeMate, a social OAR designed to assist with practical tasks, foster social interaction, and promote health and well-being. Through a pilot evaluation with seven participants in an office environment, we found that users see potential in OARs for reducing stress and promoting healthy habits and value the robot's ability to provide companionship and physical activity reminders in the office space. However, concerns regarding privacy, communication, and the robot's interaction timing were also raised. The feedback highlights the need to carefully consider the robot's appearance and behaviour to ensure it enhances user experience and aligns with office social norms. We believe these insights will better inform the development of adaptive, intelligent OAR systems for future office space integration.

Robots are expected to play an increasing role in users' lives, assisting with everyday tasks and complex decision-making, which creates the need to communicate risks -including probabilities and consequences of hazards-and to introduce robots as effective risk communicators to ensure safety. My research investigates how robots can convey two types of risks: external risks to inform users of relevant hazards, presenting the robot as a conduit for risk communication, and internal risks to transparently convey risks related to the robot itself. By leveraging the robot's embodiment and agency, my work addresses important and novel ways to communicate risk through robot affordances and their effects on user perception, decision-making, and affective responses. This dissertation offers empirical insights, implications, and design guidelines for creating robotic systems that support intuitive, human-centred risk communication.

Robots are embodied agents that act under several sources of uncertainty. When assisting humans in a collaborative task, robots need to communicate their uncertainty to help inform decisions. In this study, we examine the use of visualising a robot’s uncertainty in a high-stakes assisted decision-making task. In particular, we explore how different modalities of uncertainty visualisations (graphical display vs. the robot’s embodied behaviour) and confidence levels (low, high, 100%) conveyed by a robot affect the human decision-making and perception during a collaborative task. Our results show that these visualisations significantly impact how participants arrive to their decision as well as how they perceive the robot’s transparency across the different confidence levels. We highlight potential trade-offs and offer implications for robot-assisted decision-making. Our work contributes empirical insights on how humans make use of uncertainty visualisations conveyed by a robot in a critical robot-assisted decision-making scenario.