360-degree virtual tours provide an immersive and interactive experience, enabling users to explore and engage with environments remotely in a detailed and realistic manner. In this work, we investigate the impact of virtual tour design on visitors' user experience, uncovering valuable insights and providing actionable design guidelines. We conducted a remote usability study on two wellknown virtual touring platforms, examining how users navigate the tours and where they experience difficulties. We identified key user-reported usability issues and pain points related to navigation, movement, control, autonomy, and information presentation. Based on our findings, we present several concrete design recommendations, aimed at designers and creators of virtual tours, to help develop tours that facilitate users' spatial cognition and orientation, enhance their interactions, and result in a positive user experience. These findings are valuable for researchers, designers, and developers, offering insights that drive the ongoing enhancement of user experiences in virtual tourism environments.

While autonomous vehicles have the potential to fundamentally reshape transportation and daily life, their current technological limitations require human intervention through teleoperation to handle complex situations. These situations can range from unexpected road obstacles and complex traffic patterns to emergency scenarios that exceed the vehicle’s autonomous decision-making capabilities. In such cases, remote operators must take control of the vehicle, making tele-driving a critical component of autonomous vehicle systems. Tele-driving requires high situational awareness (SA) to ensure safety and prevent accidents. This study investigates the effectiveness of incorporating a bird’s eye view (BEV) in tele-driving interfaces. Through two comprehensive experiments, we compared a regular frontal view with different arrangements of BEV added to it. Our findings indicate that adding a BEV does not enhance the tele-driver’s SA and may even impair the perception of critical environmental elements in certain scenarios. The findings provide valuable insights for the design of tele-driving interfaces.

Accessibility technologies for visually impaired users often convey visual scenes through discrete verbal descriptions, which can interrupt natural hearing and increase cognitive load. Other technologies that integrate spatialized audio often focus on orientation, providing spatial cues without conveying what is present. We present SoundSpace, a real-time system that represents object identity and spatial layout through structured auditory cues. SoundSpace builds on prior sensory substitution approaches to combine brief spoken object naming with continuous mappings of distance, vertical position, and horizontal location to loudness, pitch, and stereo panning. To balance spatial awareness against cognitive load, the system separates scene sensing from audio output, providing periodic spatial sweeps and immediate updates when objects move. Open-vocabulary detection and environment profiles allow users to restrict feedback to task-relevant objects, reducing auditory clutter. We describe the design and implementation of SoundSpace and discuss its implications for non-visual spatial perception and active exploration.

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.

While autonomous vehicles (AVs) continue to advance and reshape modern transportation, they remain unable to navigate all traffic conditions without human input, highlighting the need for remote human intervention in edge-case scenarios. Two major teleoperation paradigms have emerged to address this need: tele-driving and tele-assistance. In tele-driving, remote operators (ROs) continuously control the AV through direct access to its actuators. In tele-assistance, ROs provide high-level instructions through a specialized interface, with low-level maneuvers delegated to the AV. We conducted a quantitative comparison of these paradigms, examining four edge-case scenarios: one uses a steering wheel and pedals, the other employs discrete high-level commands through a Wizard-of-Oz methodology. We measured mental workload, situation awareness, time completion, and overall user experience (UX). Results indicate that the tele-assistance interface reduced operators’ mental workload and improved situation awareness, suggesting the need for further development of tele-assistance interfaces.

Interactive systems that explain data, or support decision making often emphasize what is present while overlooking what is expected but missing. This presence bias limits users' ability to form complete mental models of a dataset or situation. Detecting absence depends on expectations about what should be there, yet interfaces rarely help users form such expectations. We present an experimental study examining how reference framing and prompting influence people's ability to recognize expected but missing categories in datasets. Participants compared distributions across three domains (energy, wealth, and regime) under two reference conditions: Global, presenting a unified population baseline, and Partial, showing several concrete exemplars. Results indicate that absence detection was higher with Partial reference than with Global reference, suggesting that partial, samples-based framing can support expectation formation and absence detection. When participants were prompted to look for what was missing, absence detection rose sharply. We discuss implications for interactive user interfaces and expectation-based visualization design, while considering cognitive trade-offs of reference structures and guided attention.

Autonomous vehicles (AVs) are rapidly evolving as an innovative mode of transportation. However, the consensus in both industry and academia is that AVs cannot independently resolve all traffic scenarios. Consequently, the need for remote human assistance becomes clear. To enable the widespread integration of AVs on public roadways, it is imperative to develop novel models for remote operation. One such model is tele-assistance, which promotes delegating low-level maneuvers to automation through high-level directives. Our study investigates the design and evaluation of a new command-based tele-assistance user interface for the teleoperation of AVs. First, by integrating various control paradigms and interaction concepts, we created a simulation-based, high-fidelity interactive prototype consisting of 175 screens. Next, we conducted a comprehensive usability study with 14 expert teleoperators to assess the acceptance and usability of the system. Finally, we formulated high-level insights and guidelines for designing command-based user interfaces for the remote operation of AVs.

Virtual museum tours are increasingly used to expand access to cultural heritage, yet their experiential equivalence to physical visits remains unclear. This study compared user experience in physical and virtual tours of the same museum exhibition. Thirty-two participants explored either the physical exhibition or an photo-based 360 degrees virtual tour and completed standardized and custom questionnaires. No significant differences were observed on experiential scales between conditions. However, results revealed distinct spatial usability effects: the physical tour yielded higher ratings for navigation ease, orientation, and backtracking. These results indicate that high-quality virtual tours may replicate some experiential aspects of physical visits but still present challenges in spatial clarity and intuitive wayfinding. The findings contribute insight into where current virtual museum technologies show similarities and differences in navigation, satisfaction, and accessibility in digital heritage experiences.

By merging digital content with the physical environment. AR offers unique social interaction opportunities alongside distinct risks. This study explores perceived benefits and risks of sharing personal info in AR social contexts. Analysis of participant interviews. conducted after presenting three AR scenarios, revealed benefits like targeted communication, networking opportunities, and fostering shared values, varying by scenario. Participants also identified risks like prejudgement, harassment, and diminished emotional connection, also shown to he context -dependent. These findings demonstrate the dynamic interplay between advantages and vulnerabilities of self-disclosure in AR, highlighting the need for systems addressing these concerns.

From 21 to 24 July 2025, the Lake Como School of Advanced Studies at Villa del Grumello (Como, Italy) hosted the first edition of the Mediterranean CHI Summer School titled "Designing Human-centric Interactive and Intelligent Technology (Human-IIT)".

Augmented reality (AR) applications have been shown to improve accessibility for people with low vision by enhancing the visibility of surrounding objects. Yet, prior studies mostly examined controlled settings and often focused on developing solutions for specific functional challenges. A human remote assistant powered with AR capabilities may provide a flexible solution that addresses a variety of scenarios. To examine how AR-based remote assistance can help low-vision people in real-world settings, we examined the scenario of visiting a museum, which requires coping with a variety of tasks, from navigating between museum rooms to the accessibility of museum exhibits. We conducted a qualitative user study at an archeological museum with 11 low-vision participants who toured a predefined path while receiving real-time auditory explanations and AR annotations from a remote assistant. Our results reveal that the AR-based remote assistance improved the museum experience, assisting in mobility within the museum and the visibility of artifacts. The use of remote assistance proved to be dynamic and flexible, enabling real-time in-place annotations that helped guide low-vision participants, providing a stronger feeling of security. While acknowledging the transformative potential, participants highlighted challenges in accuracy and responsiveness, emphasizing the need to improve the design of real-time AR annotations. Our study is the first to examine the use of AR remote assistance in meeting the dynamic and diverse needs of people with low vision, illustrating both its potential and the challenges for this population.

We present an initial taxonomy for external Human-Machine Interfaces (eHMIs) for last-mile delivery robots (LMDRs). Our taxonomy spans seven core dimensions capturing both the physical characteristics and communication functions of eHMIs, with a focus on human-robot interaction. This framework supports the analysis, design, and evaluation of eHMIs for delivery robots in complex urban environments. Our analysis identifies underexplored areas that can inspire future research, particularly in tailoring eHMI strategies to LMDRs’ unique operational and social contexts.

As automated vehicles continue to advance, teleoperation has emerged as a critical support system for navigating complex and unpredictable environments that exceed the vehicles' current autonomous capabilities. A main issue in the implementation of teleoperation is latency caused by the high bandwidth required to transmit the video feed from the vehicle to the remote teleoperation station. A possible approach for addressing the latency problem is the transfer of lower-resolution or compressed videos between the vehicle and the teleoperation station. When applying semantic segmentation on the video feed, many pixels are mapped to a limited set of possible colors according to the types of objects that they represent. This concept has been commonly used in autonomous driving algorithms and has the potential to enable the transferring of smaller-sized videos thus reducing bandwidth. In this study, we examine how presenting semantically segmented driving scenes to humans affects their perception of the scene, and specifically, how it affects their hazard perception and situation awareness. We conducted two user studies comparing the effects of using different levels and types of semantic segmentation. Our results indicate that viewing partly segmented scenes, such that only a selected set of object types are colored, commonly achieves the same effect, and sometimes even outperforms a realistic view. Our study and its insights may pave the way for future research, development, and design of teleoperation systems of automated vehicles.

The market share of Last-Mile Delivery Robots (LMDRs) has grown rapidly over the past few years. These robots are mostly autonomous and supported remotely by human operators. As part of a broader shift toward sustainable urban logistics, LMDRs are seen as a promising low-emission alternative to conventional delivery vehicles. While there is a large body of literature about the technology, little is known about the real-world experiences of operating these robots. This study investigates the operational challenges faced by remote operators (ROs) of LMDRs, aiming to enhance their efficiency and safety. Through interviews with industry professionals, we explore the scenarios requiring human intervention, the strategies employed by ROs, and the unique challenges they encounter. Our findings not only identify key intervention scenarios but also provide a thorough examination of the teleoperation ecosystem, operational workflows, and how they affect the ways the ROs manage their interactions with robots. We found that ROs’ involvement varies from monitoring to active intervention to support the robots in completing their tasks when they face connectivity issues, blocked routes, and various other interruptions on their journeys. The findings highlight the importance of intuitive user interfaces (UIs) and decision-support systems to reduce cognitive load and improve situational awareness. This research contributes to the literature by offering a detailed examination of real-world teleoperation practices and focusing on the human factors influencing LMDR scalability, sustainability, and integration into future-ready logistics systems.

Autonomous vehicles (AVs) are rapidly evolving as a novel way of transportation. Nevertheless, there is a consensus that AVs cannot address all traffic scenarios independently. Consequently, there arises a need for remote human intervention. To pave the way for large-scale deployment of AVs onto public roadways, innovative models of remote operation must evolve. Such a paradigm is Tele-assistance, which posits that the low-level control of AVs should be delegated through high-level commands. Our work explores how such a command language should be constructed as a first step in designing a Tele-assistance user interface. Through interviews with 17 experienced teleoperators, we elicit a set of discrete commands that a remote operator can use to resolve various road scenarios. Subsequently, we create a scenario-command mapping and a thematic classification of the defined commands. Finally, we present an initial Tele-assistance interface design based on these commands.

Visually encoding quantitative information associated with graph links is an important problem in graph visualization. A conventional approach is to vary the thickness of lines to encode the strength of connections in node-link diagrams. In this paper, we present Sticky Links, a novel visual encoding method that draws graph links with stickiness. Taking the metaphor of links with glues, sticky links represent connection strength using spiky shapes, ranging from two broken spikes for weak connections to connected lines for strong connections. We conducted a controlled user study to compare the efficiency and aesthetic appeal of stickiness with conventional thickness encoding. Our results show that stickiness enables more effective and expressive quantitative encoding while maintaining the perception of node connectivity. Participants also found sticky links to be more aesthetic and less visually cluttering than conventional thickness encoding. Overall, our findings suggest that sticky links offer a promising alternative to conventional methods for encoding quantitative information in graphs.