Much research in human–artificial intelligence (AI) interaction treats cooperation as a design problem of alignment, asking how system behavior can be coordinated with human intentions, task goals or expectations in order to support effective collaborative action. In this debate contribution, we shift the analytic focus from alignment to sequential organisation and ask how cooperation is interactionally accomplished when one source of action does not demonstrably orient to a shared sense of reality, yet its outputs are nevertheless incorporated into an unfolding sequence of action. Drawing on a sequential comic-making practice involving a generative AI system, we analyse three examples to examine how creative work can progress through misrecognition, breakdown and material effects, not necessarily requiring consensus or control. We argue that such misalignment is not a failure to be repaired but can function as a recurring interactional condition in human–AI cooperation, sustained through the sequential uptake, reinterpretation and transformation of AI-generated outputs. Based on this analysis, we suggest that human–AI interaction and design research would benefit from more explicitly interactional accounts of cooperation that treat misalignment as a productive condition rather than something to be minimised. We position artistic creative practice as a methodologically revealing site for examining how agency, authorship and coordination are practically accomplished in cooperative work with AI and we discuss implications for human-centered design.
A motion-based virtual reality (VR) bicycle simulator allows for steering and pedaling as input, while visual information and platform movements are provided as output. The simulator offers a potential means of safely experiencing mountain biking (MTB); however, the impact of complex multidirectional tilting of the bicycle on user experience remains unclear. Therefore, the aim of this study was to investigate the effects of integrating tilt and pitch on users’ psychological experience during downhill cycling in a simulator. Twenty-one participants rode a simulator course designed to replicate a real MTB course and were instructed to pass through balls placed at ten turns (i.e., banks) along the course. Measurements were taken under two conditions: the nonmotion (NM) condition, in which the platform remained stationary, and the motion-based (M) condition, in which the platform moved in tilt and pitch according to the visual environment. After each condition, participants completed the Simulator Sickness Questionnaire (SSQ), the Igroup Presence Questionnaire (IPQ), and the short version of the User Experience Questionnaire (UEQ-S). Eighteen participants completed both conditions. There was no significant difference in the SSQ and the UEQ-S between the two conditions. In the IPQ, only the subscale spatial presence was significantly higher in the M condition than in the NM condition. The platform’s tilt and pitch movements during downhill cycling in a VR bicycle simulator had only a minimal impact on simulator sickness, presence, and user experience. These results indicate that a platform without motion may be sufficient for rehearsing MTB downhill courses for individuals with no prior MTB experience.
Augmented reality (AR) introduces new dynamics for personal information sharing by embedding digital content into shared physical spaces. While prior work has examined comfort and privacy concerns in AR, less is known about why people choose to disclose personal information and how social context shapes these decisions. We conducted a mixed-methods study with 20 participants across three fictional scenarios that varied in formality and social norms. Participants disclosed information to clarify identity, build relationships, and strategically manage impressions, yet patterns varied by setting: intimate details were sometimes shared in casual contexts despite acknowledged risks, while professional contexts raised concerns about appropriateness. Based on these findings, we present an AR-adapted disclosure decision model that treats context as an active moderator of perceived risks and benefits and highlights the roles of control and reciprocity in co-present environments. We conclude with design recommendations for context-sensitive privacy mechanisms that support selective and reciprocal disclosure in AR.
Depending on the degree of disability, simple tasks of daily living can be challenging for people with physical disabilities, such as picking up and placing objects, eating, or reaching for a cup to drink independently. Pervasive technologies such as robotic arms can be used to assist with these daily tasks, allowing patients to regain independence while reducing the need for care. Specialized devices, such as assistive forks or spoons, can facilitate these tasks. Image datasets of everyday objects such as MS COCO do not contain assistive devices, which tend to look different from their non-assistive counterparts. We present the dataset WLRI-AD (Work-Life Robotics Institute–Assistive Devices) to enable a robot to interact with devices in assisted living homes. The benefits of including assistive devices are demonstrated by comparing versions of the dataset with each other and to a baseline. Initial results show an improvement in the detection of assistive devices by training a YOLOv8 model on the assistive devices.
In Ambient Intelligence (AmI), seamless interaction between humans and AI systems is crucial for the effective utilization of smart environments. This paper investigates human–AI interaction in AmI, focusing on the management of uncertainty—situations where the AI is unsure how to choose between multiple possible outputs—within the context of Opportunistic Software Composition. The Opportunistic Composition Engine (OCE) dynamically constructs applications or assemblies from available software components, learning from human feedback to tailor applications to user preferences and context. We design, implement, and evaluate three approaches to uncertainty handling: explicitly asking the user to resolve uncertainty, offering a choice between multiple assemblies, and using a visual gradient to indicate the AI’s confidence. Each approach is implemented in a distinct version of OCE and assessed through a user study (N = 121) measuring usability and learning performance in a 2D online simulated ambient environment. Our results show that providing clear visual or informational cues about uncertainty improves user satisfaction, while forcing users to resolve uncertainty themselves reduces usability and can hinder learning. Importantly, the other interaction strategies do not impair the system’s learning, suggesting that lightweight, system-guided handling of uncertainty can effectively support both user experience and AI adaptation. Beyond these conclusions, this work demonstrates the utility and general applicability of simulation-based technologies for prototyping and evaluating AmI applications.
