AI technologies that sense student attention and emotions to enable more personalised teaching interventions are increasingly promoted, but raise pressing questions about student learning, wellbeing, and ethics. In particular, students’ perspectives about AI sensing-intervention in learning are often overlooked. We conducted an online mixed-method experiment with Australian university students (N=132), presenting video scenarios varying by whether sensing was used (in-use vs. not-in-use), sensing modality (gaze-based attention detection vs. facial-based emotion detection), and intervention (by digital device vs. teacher). Participants also completed pairwise ranking tasks to prioritise six core ethical concerns. Findings revealed that students valued targeted intervention but responded negatively to AI monitoring, regardless of sensing methods. Students preferred system-generated hints over teacher-initiated assistance, citing learning agency and social embarrassment concerns. Students’ ethical considerations prioritised autonomy and privacy, followed by transparency, accuracy, fairness, and learning beneficence. We advocate designing customisable, social-sensitive, non-intrusive systems that preserve student control, agency, and well-being.

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.

In this paper, we critically examine the current "humanoid hype" in robotics, questioning its alignment with responsible robotics principles. While technical challenges drive internal fascination, the pervasive public image of humanoids demands deeper HRI engagement. We explore how responsible robotics concepts, such as privacy, dignity, and trust, are uniquely challenged or overlooked in the pursuit of anthropomorphic robot forms. By dissecting this hype, and mapping the main findings of the recently-published Roadmap for Responsible Robotics to the humanoids field, we aim to move beyond technical form-factor obsessions to understand the true societal implications and identify potential blind spots for the HRI community.

Adopting AI in multi-stakeholder contexts often involves complex ethical challenges, particularly when decision-makers are not directly impacted by the technology. Ethical challenges arise not only from the design of the AI technology but also from social factors in its development and deployment which may harm disempowered stakeholders. This paper examines how stakeholder dynamics in AI adoption may help to create or mitigate such harm, using a case study of AI-based student monitoring in K-12 education. These systems analyse students’ emotions, concentration, and classroom performance to inform teaching. We conducted 30 semi-structured interviews with key stakeholders: parents, school representatives, and developers. Our findings identify three key social factors that may create ethical problems in decision-making: limited representation of students’ perspectives during design and adoption; developers’ compromises and pragmatism under organisational pressure; and school governors’ limited technological literacy and failure of ‘due diligence’. These factors arise from power imbalances related to knowledge and authority that may weaken accountability mechanisms for governing AI in Education (AIEd) adoption. We propose addressing these imbalances through improving the AI ethics literacy of key stakeholders to ensure effective oversight and informed decision-making. This work contributes an empirical understanding of how real-world power relations shape ethical vulnerabilities in AIEd adoption. It extends HCI and AI ethics research by revealing how stakeholder asymmetries can marginalise affected users and constrain the practical application of ethical design principles. These insights offer guidance for more inclusive and accountable AI governance and provide insights relevant to other multi-stakeholder AI contexts. CCS CONCEPTS • Human-centred computing -> Empirical studies in HCI; Empirical studies in collaborative and social computing.

Social activities in long-term care homes help promote residents’ wellbeing, but their effectiveness depends on residents’ engagement. To identify design opportunities for promoting meaningful engagement, we conducted an ethnographic study on organised activities in an Australian aged care home. We observed staff fostered engagement by initiating conversations, weaving residents’ backgrounds into interactions, and adapting activities to residents’ varying abilities. However, challenges included new staff members’ unfamiliarity with residents, multi-tasking, and insufficient support to engage excluded residents. Using a care ethics lens that includes relational, situated and empathetic features of care, we show that meaningful engagement is shaped by the ethical care practices embedded in staff-resident interactions and highlight opportunities for technologies to mitigate barriers hindering staff from providing ethical care in existing activities. These opportunities include: collecting and recording residents’ interests, providing conversation prompts, enhancing activity inclusiveness, and reducing language and cultural barriers.

AI's integration into education promises to equip teachers with data-driven insights and intervene in student learning. Despite the intended advancements, there is a lack of understanding of interactions and emerging dynamics in classrooms where various stakeholders including teachers, students, and AI, collaborate. This paper aims to understand how students perceive the implications of AI in Education (AIEd) in terms of classroom collaborative dynamics, especially AI used to observe students and notify teachers to provide targeted help. Using the story completion method, we analyzed narratives from 65 participants, highlighting three challenges: AI decontextualizing of the educational context; AI-teacher cooperation with bias concerns and power disparities; AI's impact on student behavior that further challenges AI's effectiveness. We argue that for effective and ethical AI-facilitated cooperative education, future AIEd design must factor in the situated nature of implementation. Designers must consider the broader nuances of the education context, impacts on multiple stakeholders, dynamics involving these stakeholders, and the interplay among potential consequences for AI systems and stakeholders. It is crucial to understand the values in the situated context, the capacity and limitations of both AI and human for effective cooperation, and any implications to the relevant ecosystem.

This perspective is part of an international effort to improve epidemiological models with the goal of reducing the unintended consequences of infectious disease interventions. The scenarios in which models are applied often involve difficult trade-offs that are well recognised in public health ethics. Unless these trade-offs are explicitly accounted for, models risk overlooking contested ethical choices and values, leading to an increased risk of unintended consequences. We argue that such risks could be reduced if modellers were more aware of ethical frameworks and had the capacity to explicitly account for the relevant values in their models. We propose that public health ethics can provide a conceptual foundation for developing this capacity. After reviewing relevant concepts in public health and clinical ethics, we discuss examples from the COVID-19 pandemic to illustrate the current separation between public health ethics and infectious disease modelling. We conclude by describing practical steps to build the capacity for ethically aware modelling. Developing this capacity constitutes a critical step towards ethical practice in computational modelling of public health interventions, which will require collaboration with experts on public health ethics, decision support, behavioural interventions, and social determinants of health, as well as direct consultation with communities and policy makers.

Encouraging and supporting diversity and inclusion in computer science research communities is a critical issue for many reasons, including the ethical and robust design, delivery and publication of research that addresses real-world situations ranging from the use of digital tools in health to predictive policing to workplace hiring practices, just to name a few. One way to measure diversity is to apply analytical research methods to data sourced from the public domain for use in research. However, attempts to measure diversity using public data may themselves raise legal and ethical questions about the provenance of the data, research methods adopted, and treatment of diversity in the publication of results. This article interrogates the challenges of measuring diversity using public data, examining an illustrative case study framed around an academic research project at an Australian university using a public data set to identify gender representation in computer science communities. Employing a critical data governance perspective, we point to a range of ethical and legal concerns and recommend greater regulatory guardrails to better balance public interests in research and the privacy, data protection and other ethical interests of research subjects.

Social robots have the potential to augment the care provided to older adults in residential aged care homes. However, social robots can only be valuable in aged care if care staff successfully incorporate them into their ongoing care practices beyond a limited research period. This study examines the benefits and challenges of using different types of social robots in real-world practices from care staff perspectives. We conducted semi-structured interviews with eleven staff members who have first-hand experience of employing robots in their work. Our findings highlight the entangled relationships among the actors in the older adult/carer/robot triad. We discuss the role of robots in supporting a mutually beneficial relationship between care staff and older adults, and how robopets and humanoid robots impact care staff in different ways. Finally, we offer recommendations for the future deployment of robots. We argue that sustainable deployment of robots in care practice might involve recognizing and promoting positive impacts for both human parties in the triad, and that the practice of using robots needs to align with the needs and interests of both caregivers and care recipients.

Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes 2 contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution that hinges on the education and accreditation of new expert groups who specialize in the development, verification, and operation of medical AI technologies. These groups will be required to maintain trust in our healthcare institutions.

Research into companion robots for older adults, including those who are socially isolated and lonely, continues to grow. Although some insight into older adults' preferences for various robotic types and functionality is emerging, we lack research examining how these robots fulfil or challenge a range of values and aspirations individuals have in later life. This study examines the attitudes and perspectives of 16 older adults (aged 65+) living independently but alone in their own homes, who were interviewed and shown videos depicting three distinctive companion robots: a talking assistant; a roving toylike vehicle; and a robotic dog. This approach illuminated values, preferences, and needs amongst older people that are vital for understanding the potential of companion robots. In comparing the robots, participants expressed concerns about the impact of different companion robots on their abilities and skills, their sense of autonomy and control over their lives, and the maintenance of several kinds of dignity. These results inform user-centered design and use of companion robots for older people living alone and independently.

As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in health care. These concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments. While there has been extensive discussion about the ethics of AI in health care, there has been little dialogue or recommendations as to how to practically address these concerns in health care. In this article, we propose a governance model that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance of AI in health care.