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This research investigates fast and precise Virtual Reality (VR) sketching methods with different tool-based asymmetric interfaces. In traditional real-world drawing, artists commonly employ an asymmetric interaction system where each hand holds different tools, facilitating diverse and nuanced artistic expressions. However, in virtual reality (VR), users are typically limited to using identical tools in both hands for drawing. To bridge this gap, we aim to introduce specifically designed tools in VR that replicate the varied tool configurations found in the real world. Hence, we developed a VR sketching system supporting three hybrid input techniques using a standard VR controller, a VR stylus, or a data glove. We conducted a formal user study consisting of an internal comparative experiment with four conditions and three tasks to compare three asymmetric input methods with each other and with a traditional symmetric controller-based solution based on questionnaires and performance evaluations. The results showed that in contrast to symmetric dual VR controller interfaces, the asymmetric input with gestures significantly reduced task completion times while maintaining good usability and input accuracy with a low task workload. This shows the value of asymmetric input methods for VR sketching. We also found that the overall user experience could be further improved by optimizing the tracking stability of the data glove and the VR stylus. [ABSTRACT FROM AUTHOR]

Extended reality (XR) such as Virtual Reality (VR) and Augmented Reality (AR) has been heralded as a particularly promising technology for autistic people. However, prior studies have centered around curing or ameliorating deficits and impairments and are typically conducted by non-disabled and non-autistic researchers. Using co-design and a variety of learning experience design methods and processes, Project PHoENIX (Participatory, Human-centered, Equitable, Neurodiverse, and Inclusive XR) speaks to the need for research that applies a social-ecological perspective to the design and evaluation of VR experiences for autistic users, with the goal of decreasing environmental barriers and promoting a more inclusive society. In this study, we describe a multi-cycle process of educational design research (EDR), consisting of iterative human-centered formative design, development, implementation, and evaluation of Project PHoENIX from Spring 2021 to Spring 2022. A framework for conducting co-design and collaborative educational design research with autistic individuals in a VR environment is provided along with design principles that support this framework. Findings from 3 meso-cycles illustrate the dual outcomes of educational design research, namely, (1) a consistently maturing intervention and (2) improving theoretical understanding. Findings underscore the feasibility of our approach, and demonstrate potential to scale. [ABSTRACT FROM AUTHOR]

With the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, including their language proficiencies. In particular, there is now a significant number of polyglot Web users, i.e., users who are proficient in more than one language. However, even such users with potential access to a broad range of information from multiple languages often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages. To overcome language barriers, the majority of research has focused primarily on improving retrieval and translation accuracy, while paying comparably less attention to multilingual user interaction aspects. In particular, relatively little human-centered research has been conducted to better understand and support multilingual user abilities and preferences, and even less so regarding news search and different access modalities (such as desktop and mobile interfaces). The research presented in this article provides the first comparative analyses of polyglot users' preferences and behaviors with respect to different multilingual news search interfaces on both desktop and mobile platforms. Specifically, through a set of task-based user studies in laboratory experiments, the key contribution of this article is the presentation of the first human-centered studies in multilingual news search result interfaces, aiming to drive the development of human-centered multilingual news access systems for both desktop and mobile platforms. This contribution includes a detailed analysis of different interface design paradigms, as well as a series of implications for design. [ABSTRACT FROM AUTHOR]

Extended reality (XR) such as Virtual Reality (VR) and Augmented Reality (AR) has been heralded as a particularly promising technology for autistic people. However, prior studies have centered around curing or ameliorating deficits and impairments and are typically conducted by non-disabled and non-autistic researchers. Using co-design and a variety of learning experience design methods and processes, Project PHoENIX (Participatory, Human-centered, Equitable, Neurodiverse, and Inclusive XR) speaks to the need for research that applies a social-ecological perspective to the design and evaluation of VR experiences for autistic users, with the goal of decreasing environmental barriers and promoting a more inclusive society. In this study, we describe a multi-cycle process of educational design research (EDR), consisting of iterative human-centered formative design, development, implementation, and evaluation of Project PHoENIX from Spring 2021 to Spring 2022. A framework for conducting co-design and collaborative educational design research with autistic individuals in a VR environment is provided along with design principles that support this framework. Findings from 3 meso-cycles illustrate the dual outcomes of educational design research, namely, (1) a consistently maturing intervention and (2) improving theoretical understanding. Findings underscore the feasibility of our approach, and demonstrate potential to scale.

Differential privacy is an essential approach for privacy preservation in data queries. However, users face a significant challenge in selecting an appropriate privacy scheme, as they struggle to balance the utility of query results with the preservation of diverse individual privacy. Customizing a privacy scheme becomes even more complex in dealing with queries that involve multiple data attributes. When adversaries attempt to breach privacy firewalls by conducting multiple regular data queries with various attribute values, data owners must arduously discern unpredictable disclosure risks and construct suitable privacy schemes. In this paper, we propose a visual analysis approach for formulating privacy schemes of differential privacy. Our approach supports the identification and simulation of potential privacy attacks in querying statistical results of multi-dimensional databases. We also developed a prototype system, called DPKnob, which integrates multiple coordinated views. DPKnob not only allows users to interactively assess and explore privacy exposure risks by browsing high-risk attacks, but also facilitates an iterative process for formulating and optimizing privacy schemes based on differential privacy. This iterative process allows users to compare different schemes, refine their expectations of privacy and utility, and ultimately establish a well-balanced privacy scheme. The effectiveness of this study is verified by a user study and two case studies with real-world datasets.

The application and use cases for conversational agents (CAs) are versatile. Smart speakers such as Alexa and Google Home are used in smart home environments, digital agents are integrated into car systems and chatbots are increasingly used in customer service processes. However, human–computer interaction researchers identify and investigate a wide-ranging variety of aspects impeding the usage of CAs by end-users. In general, impediments differ depending on use case contexts, user group characteristics and the CA's technological infrastructure. Hence, it is difficult and often ambiguous for designers and developers to generate an appropriate awareness about aspects impeding CA usage. We address this problem, by conducting a systematic review of 65 publications surveying impeding aspects of the usage of CAs. [ABSTRACT FROM AUTHOR]

Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle these issues in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on extra equipment in the conference venues, such as the infrared camera or special microphone, which are not practical. In this article, we design and implement Meetor, a human-centered automatic video editing system for meeting recordings. The Meetor mainly contains three parts: an audio-based video synchronization algorithm, human-centered video content flaw detection algorithms, and an automatic video editing algorithm. Two main experiments are conducted from both objective and subjective aspects to evaluate the performance of the Meetor. The experimental results on a testbed illustrate that the proposed algorithms could achieve state-of-the-art (SOTA) performance in video content flaw detection. However, the conducted user study demonstrates that Meetor could generate meeting recordings with a satisfactory quality compared with professional video editors. Moreover, we also present a practical application of the Meetor in a university campus prototype, in which the Meetor is applied in the automatic editing of lecture recordings. All in all, the proposed Meetor can be utilized in practical applications to release the workload of professional video editors. [ABSTRACT FROM AUTHOR]

Office well-being aims to explore and support a healthy, balanced and active work style in office environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces as active interventions to explore how to tackle problems such as inactive work and lifestyles, and increasingly sedentary behaviours. We identify a fragmented research landscape on tangible Office well-being interventions, missing the relationship between interventions, data, design strategies, and outcomes, and behaviour change techniques. Based on the analysis of 40 papers, we identify 7 classifications in tangible Office well-being interventions and analyse the intervention based on their role and foundation in behaviour change. Based on the analysis, we present design considerations for the development of future tangible Office well-being design interventions and present an overview of the current field and future research into tangible Office well-being interventions to design for a healthier and active office environment. [ABSTRACT FROM AUTHOR]

Achieving fluency in a foreign language is challenging, often because of limited opportunities for speaking practice. Speaking in a different language can be difficult and sometimes embarrassing. This study introduces a tool designed to prioritize language-speaking practice by integrating the "Taboo" game with speech recognition technology and a large language model (LLM). This game involves players conveying a target word to an AI-based agent without using specific "Taboo" words. Our contribution focuses on proposing a meticulously designed taboo game for speaking practice that incorporates engaging interactions with the LLM. This methodology offers a novel and beneficial alternative for English as a Second Language (ESL) learners. Initial experimental trials were conducted, and user feedback highlighted a positive experience with reduced anxiety compared to conventional, human-based speaking practice. However, challenges have emerged, particularly in terms of pronunciation and the expression of certain words. In response to this valuable feedback, we presented a design proposal aimed at addressing these challenges to improve the overall learning experience and ensure effective language practice. [ABSTRACT FROM AUTHOR]

Introduction: In this work we explore a potential approach to improve humanrobot collaboration experience by adapting cobot behavior based on natural cues from the operator. Methods: Inspired by the literature on human-human interactions, we conducted a wizard-of-oz study to examine whether a gaze towards the cobot can serve as a trigger for initiating joint activities in collaborative sessions. In this study, 37 participants engaged in an assembly task while their gaze behavior was analyzed. We employed a gaze-based attention recognition model to identify when the participants look at the cobot. Results: Our results indicate that in most cases (83.74%), the joint activity is preceded by a gaze towards the cobot. Furthermore, during the entire assembly cycle, the participants tend to look at the cobot mostly around the time of the joint activity. Given the above results, a fully integrated system triggering joint action only when the gaze is directed towards the cobot was piloted with 10 volunteers, of which one characterized by high-functioning Autism Spectrum Disorder. Even though they had never interacted with the robot and did not know about the gaze-based triggering system, most of them successfully collaborated with the cobot and reported a smooth and natural interaction experience. Discussion: To the best of our knowledge, this is the first study to analyze the natural gaze behavior of participants working on a joint activity with a robot during a collaborative assembly task and to attempt the full integration of an automated gaze-based triggering system. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices have become increasingly important within the smart home domain, making the security of the devices a critical aspect. The majority of IoT devices are black-box systems running closed and pre-installed firmware. This raises concerns about the trustworthiness of these devices, especially considering that some of them are shipped with a microphone or a camera. Remote attestation aims at validating the trustworthiness of these devices by verifying the integrity of the software. However, users cannot validate whether the attestation has actually taken place and has not been manipulated by an attacker, raising the need for HCI research on trust and understandability. We conducted a qualitative study with 35 participants, investigating trust in the attestation process and whether this trust can be improved by additional explanations in the application. We developed an application that allows users to attest a smart speaker using their smartphone over an audio channel to identify the attested device and observe the attestation process. In order to observe the differences between the applications with and without explanations, we performed A/B testing. We discovered that trust increases when additional explanations of the technical process are provided, improving the understanding of the attestation process. [ABSTRACT FROM AUTHOR]

AbstractDance learning through online videos has gained popularity, but it presents challenges in providing comprehensive information and personalized feedback. This paper introduces DanceSculpt, a system that utilizes 3D human reconstruction and tracking technology to enhance the dance learning experience. DanceSculpt consists of a dancer viewer that reconstructs dancers in video into 3D avatars and a dance feedback tool that analyzes and compares the user’s performance with that of the reference dancer. We conducted a comparative study to investigate the effectiveness of DanceSculpt against conventional video-based learning. Participants’ dance performances were evaluated using a motion comparison algorithm that measured the temporal and spatial deviation between the users’ and reference dancers’ movements in terms of pose, trajectory, formation, and timing accuracy. Additionally, user experience was assessed through questionnaires and interviews, focusing on aspects such as effectiveness, usefulness, and satisfaction with the system. The results showed that participants using DanceSculpt achieved significant improvements in dance performance compared to those using conventional methods. Furthermore, the participants rated DanceSculpt highly in terms of effectiveness (avg. 4.27) and usefulness (avg. 4.17) for learning dance. The DanceSculpt system demonstrates the potential of leveraging 3D human reconstruction and tracking technology to provide a more informative and interactive dance learning experience. By offering detailed visual information, multiple viewpoints, and quantitative performance feedback, DanceSculpt addresses the limitations of traditional video-based learning and supports learners in effectively analyzing and improving their dance skills. [ABSTRACT FROM AUTHOR]

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications. [ABSTRACT FROM AUTHOR]

AbstractHumans encounter a vast array of sensory stimuli in their everyday lives. However, many visualization techniques primarily utilize visual feedback, which may disregard certain intricate details. Relying on a single visual channel may overlook complex layouts. However, how haptic force feedback can be used to assist visualization remained under-explored. In this work, we initially conducted a literature review to identify potential problems in the visualization of large datasets and engaged in discussions with domain experts to explore the potential of haptic force feedback and visual collision representation. Subsequently, we designed an innovative haptic force feedback knob, which included 3 primary modules and 29 elements. To evaluate the clarity and usefulness of this design space, we conducted a workshop and devised “recommended solutions” for the identified visualization problems. Finally, we implemented a prototype of the haptic force feedback knob and assessed its performance on scatterplot and parallel coordinate plot tasks using large datasets. The results indicated that the knob prototype could reduce visual strain and enhance the efficiency of visualization tasks. [ABSTRACT FROM AUTHOR]

AbstractAutonomous 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. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) has recently seen significant development in interaction with computers and the visualization of information. More and more people are using virtual and immersive technologies in their daily lives, especially for entertainment, fitness, and socializing purposes. This paper presents a qualitative evaluation of a large sample of users using a VR platform for dancing ( N = 292 ); we study the users' motivations, experiences, and requirements for using VR as an inclusive platform for dancing, mainly as a social or physical activity. We used an artificial intelligence platform (OpenAI) to extract categories or clusters of responses automatically. We organized the data into six user motivation categories: fun, fitness, social activity, pandemic, escape from reality, and professional activities. Our results indicate that dancing in virtual reality is a different experience than in the real world, and there is a clear distinction in the user's motivations for using VR platforms for dancing. Our survey results suggest that VR is a tool that can positively impact physical and mental well-being through dancing. These findings complement the related work, help in identifying the use cases, and can be used to assist future improvements of VR dance applications. [ABSTRACT FROM AUTHOR]

Face reenactment technology is widely applied in various applications. However, the reconstruction effects of existing methods are often not quite realistic enough. Thus, this paper proposes a progressive face reenactment method. First, to make full use of the key information, we propose adaptive convolution and instance normalization to encode the key information into all learnable parameters in the network, including the weights of the convolution kernels and the means and variances in the normalization layer. Second, we present continuous transitive facial expression generation according to all the weights of the network generated by the key points, resulting in the continuous change of the image generated by the network. Third, in contrast to classical convolution, we apply the combination of depth‐ and point‐wise convolutions, which can greatly reduce the number of weights and improve the efficiency of training. Finally, we extend the proposed face reenactment method to the face editing application. Comprehensive experiments demonstrate the effectiveness of the proposed method, which can generate a clearer and more realistic face from any person and is more generic and applicable than other methods. [ABSTRACT FROM AUTHOR]

Inspired by the rich tapestry of nature, biomimicry encourages designers, engineers, and researchers to draw inspiration from the natural world to create technological solutions. However, little is known about the usage of biomimicry in participatory research involving local people without technical expertise. In this paper, we present two case studies to explore how performative biomimicry is when applied in a participatory research process with local people of different ages. To this end, we conducted two different case studies and asked our participants to analyze the characteristics of different animal species to design an interactive prototype that addresses a specific task. Both case studies demonstrated promising potential for biomimicry, as the participants drew inspiration from animal species to develop functional prototypes and narratives that resonate on an emotional level. Finally, we discuss the benefits of integrating biomimicry in participatory research and how this method could be used to promote social change and transformation in the society, for example, in STEM education, digital literacy, and environmental awareness. [ABSTRACT FROM AUTHOR]

Introduction: In this work we explore a potential approach to improve human-robot collaboration experience by adapting cobot behavior based on natural cues from the operator.Methods: Inspired by the literature on human-human interactions, we conducted a wizard-of-oz study to examine whether a gaze towards the cobot can serve as a trigger for initiating joint activities in collaborative sessions. In this study, 37 participants engaged in an assembly task while their gaze behavior was analyzed. We employed a gaze-based attention recognition model to identify when the participants look at the cobot.Results: Our results indicate that in most cases (83.74%), the joint activity is preceded by a gaze towards the cobot. Furthermore, during the entire assembly cycle, the participants tend to look at the cobot mostly around the time of the joint activity. Given the above results, a fully integrated system triggering joint action only when the gaze is directed towards the cobot was piloted with 10 volunteers, of which one characterized by high-functioning Autism Spectrum Disorder. Even though they had never interacted with the robot and did not know about the gaze-based triggering system, most of them successfully collaborated with the cobot and reported a smooth and natural interaction experience.Discussion: To the best of our knowledge, this is the first study to analyze the natural gaze behavior of participants working on a joint activity with a robot during a collaborative assembly task and to attempt the full integration of an automated gaze-based triggering system.

Virtual reality(VR) applications are typically developed within their own immersive digital worlds; therefore, virtual spaces are usually treated as discrete from the physical space where augmented and mixed reality users exist, which makes it difficult to combine these heterogeneous realities into an integrated extended reality(XR) environment. Along these lines, we propose a method that enables a user to geometrically register the virtual space within a VR application to a real space using a commodity camera in a workspace as an anchor point. We first investigate the mathematical aspect of the computational model for connecting the virtual space to the physical world. Then, we present a computational procedure that implements our proposed method with numerical accuracy and stability. As an application, we demonstrate that users of two VR systems from different vendors may collaborate within a shared real workspace while interacting with each other physically. The presented method provides a key mechanism for enabling XR users to leverage these immersive technologies by effectively using different realities within an integrated environment.

Transactive memory system (TMS) is a team emergent state representing the knowledge of each member about "who knows what" in a team performing a joint task. We present a study to show how the three TMS dimensions Credibility, Specialisation, Coordination, can be modelled as a linear combination of the nonverbal multimodal features displayed by the team performing the joint task. Results indicate that, to some extent, the three dimensions of TMS can be expressed as a linear combination of nonverbal multimodal features. Moreover, the higher the number of modalities (audio, movement, spatial), the better the modelling. Results could be used in future work to design human-centered computing applications able to automatically estimate TMS from teams' behavioural patterns, to provide feedback and help teams' interactions. [ABSTRACT FROM AUTHOR]

Trust is important for collaboration. In hybrid teams of humans and robots, trust enables smooth collaboration and reduces risks. Just as collaboration between humans and robots differs from interpersonal collaboration, so does the nature of trust in human-robot interaction (HRI). Therefore, further investigations on trust formation and dissolution in HRI, factors affecting it, and means for keeping trust on an appropriate level are needed. However, our knowledge of interpersonal trust and trust in autonomous agents cannot be transferred directly to HRI. In this paper, we present a study with 32 participants on trust formation and dissolution as well as forecasting to influence trust in an industry robot. Results show differences in dynamics and factors of trust formation and dissolution. Additionally, we find that the effect of forecasting on trust depends on task success. These findings support the design of trustful human-robot interaction and corresponding robotic team members. [ABSTRACT FROM AUTHOR]

In this article, we reconsider elements of Agre's critical technical practice approach (Agre, 1997) for critical technical practice approach for reflexive artificial intelligence (AI) research and explore ways and expansions to make it productive for an operationalization in contemporary data science. Drawing on Jörg Niewöhner's co-laboration approach, we show how frictions within interdisciplinary work can be made productive for reflection. We then show how software development environments can be repurposed to infrastructure reflexivities and to make co-laborative engagement with AI-related technology possible and productive. We document our own co-laborative engagement with machine learning and highlight three exemplary critical technical practices that emerged out of the co-laboration: negotiating comparabilities, shifting contextual attention and challenging similarity and difference. We finally wrap up the conceptual and empirical elements and propose Reflexive Data Science (RDS) as a methodology for co-laborative engagement and infrastructured reflexivities in contemporary AI-related research. We come back to Agre's ways of operationalizing reflexivity and introduce the building blocks of RDS: (1) organizing encounters of social contestation, (2) infrastructuring a network of anchoring devices enabling reflection, (3) negotiating timely matters of concern and (4) designing for reflection. With our research, we aim at contributing to the methodological underpinnings of epistemological and social reflection in contemporary AI research. [ABSTRACT FROM AUTHOR]

As deep reinforcement learning (RL)'s capabilities surpass traditional reinforcement learning, the community is working to make these black boxes less opaque. Explanations about algorithms' choices and strategies serve this purpose. However, information about RL algorithms' operations is not easily accessible. Our research aimed to extract such information, use it to build explanations, and test those explanations with users. For our user study, eight RL agents were trained using OpenAI baselines. Then, the HIGHLIGHTS-VIS algorithm was created by altering previous HIGHLIGHTS algorithms to collect data about the agents' interactions with the environment. The data were used to create explanations that were compared to previous works' video-based summaries in a user study. The between-subjects user study had participants answer questions about different agents. Participants were measured using both self-reported trust and performance on downstream tasks. Downstream tasks are tasks that a participant is more likely to do correctly with the information contained in the explanation. Collecting data about both trust and the utility of explanations allowed comparison and analysis of the explanations' effectiveness. Results showed that the alternative explanations built from the collected data led to more correct answers about the agents and their strategies. Additionally, explanations' utility depended on the context. Finally, users' reported trust in an explanation did not directly correlate to performance. These results suggest trust and effectiveness may need to be measured and calibrated separately in future examinations of explanations. [ABSTRACT FROM AUTHOR]

Immersive visualization utilizes virtual reality, mixed reality devices, and other interactive devices to create a novel visual environment that integrates multimodal perception and interaction. This technology has been maturing in recent years and has found broad applications in various fields. Based on the latest research advancements in visualization, this paper summarizes the state-of-the-art work in immersive visualization from the perspectives of multimodal perception and interaction in immersive environments, additionally discusses the current hardware foundations of immersive setups. By examining the design patterns and research approaches of previous immersive methods, the paper reveals the design factors for multimodal perception and interaction in current immersive environments. Furthermore, the challenges and development trends of immersive multimodal perception and interaction techniques are discussed, and potential areas of growth in immersive visualization design directions are explored.

Virtual tourism or VR tourism has attracted great attention for its ability to simulate real-world experiences at a much lower cost and safer than actual travel. However, previous studies have mainly targeted young people or studies on older adults did not compare different age groups with both quantitative and qualitative methods. This study analyzed empirical user experiences of VR tourism via head-mounted displays (HMDs) by age group, with particular focus on experiential values, presence, and cybersickness. It was found that older adults perceived higher experiential values and presence than younger people and experienced less cybersickness, which is in contrast to previous research suggesting that susceptibility to VR sickness may increase with age. Furthermore, many older adults considered that virtual tourism can be an alternative to actual travel. This study addresses the effectiveness and applicability of VR tourism in older demographics and its potential contribution to their welfare. [ABSTRACT FROM AUTHOR]

Transitioning to and through University is a delicate period for students' well-being. Moreover, the recent COVID-19 pandemic added a further toll through the various challenges related to studying, socializing, community-building, and safety. These challenges inspired the design of a mobile application, called Tecnico GO!, to support university students' well-being and academic performance. This paper presents the design rationale and evaluation of the app conducted during the academic year 2021-2022. Findings cluster around three themes: i) students studying needs; ii) building a sense of community; iii) gamification strategies. The discussion elaborates on the student's perceptions of well-being during pandemics. Students' perception of the app is positive, appreciative of the crowdsensing features, supporting learning goals, community building, and safety. On the other hand, the gamification features, as currently deployed, do not achieve the expected goals. [ABSTRACT FROM AUTHOR]