International Journal of Human-Computer Studies最新文献

Recent work on augmented reality (AR) has explored the use of adaptive agents to overcome attentional issues that negatively impact task performance. However, despite positive technical evaluations, adaptive agents have shown no significant improvements to user task performance in AR. Furthermore, previous works have primarily evaluated such agents using abstract tasks. In this paper, we develop an agent that observes user behaviour and performs appropriate actions to mitigate attentional issues in a realistic sense-making task in AR. We employ mixed methods to evaluate our agent in a between-subject experiment (N=60) to understand the agent’s effect on user task performance and behaviour. While we find no significant improvements in task performance, our analysis revealed that users’ preferences and trust in the agent affected their receptiveness of the agent’s recommendations. We discuss the pitfalls of autonomous agents and highlight the need to shift from designing better Human–AI interactions to better Human–AI collaborations.

Recent developments in accessible electronic-making toolkits have opened up avenues for individuals with intellectual disabilities(ID) to actively participate in creating their own smart objects based on the Internet of Things (IoT) technology. These toolkits present a novel opportunity to foster the inclusion of this often-under-considered community in the development of personalized solutions that can impact their autonomy and well-being. However, understanding IoT encompasses comprehending the inter-object connection paradigm at the core of this technology, an aspect that is not adequately covered by the existing accessible toolkits. To fill this gap, this paper illustrates the design and evaluation of MakeNodes, a tangible toolkit to involve people with ID in making smart-thing networks in a guided, collaborative, and engaging way. The toolkit comprises a series of input and output nodes that can be paired to make any object or surface smart. The paper illustrates how MakeNodes empowers people with ID through IoT-making activities that promote collaborative work to address personal needs. Key findings are discussed in the form of lessons learned concerning the importance of physical and visual elements, hands-on exploration, and interaction robustness in improving the accessibility of IoT-making toolkits for people with ID.

Rising unemployment has led to many discouraged job seekers. While the impact of job seekers’ motivation on interview performance is acknowledged in previous research, little attention has been given to understanding the effect of training on interview motivation and performance. We present InterviewApp, an online interview training tool aiming to support interview motivation through autonomy, relatedness and competence needs derived from Self-Determination Theory and, in turn, performance. Through a four-month study (N=135), we assess its effectiveness in supporting job seekers’ interview motivation and performance. Our results demonstrate the role of autonomy in mediating the effect of training on performance. We found that the intervention significantly affected the job seekers’ perceived autonomy. Furthermore, engagement with the recording and feedback features of the tool positively impacted performance. Overall, job seekers found InterviewApp helpful for online interview training and valued the provided expert feedback. These findings have implications for the design of online interview training tools and for behaviour change interventions to support employment.

Users of Social Networking Sites (SNS) often engage in social comparison, an experience that can elicit various affective responses. While previous studies have established important links between social comparison and emotion, the granularity and ecological validity of this work remains limited. To address this issue, we conducted a field study with 40 Instagram users. We unobtrusively recorded their Instagram usage patterns (e.g., time on site and the type of content consumed, such as feed, story, or message). Additionally, we used experience sampling methods and questionnaires to capture their affective experiences, social comparisons, and depression severity. Our results elucidate the interplay between these emotions, traits, and tendencies during Instagram use. Notably, we find that while passive or prolonged Instagram use affects users’ emotions, the magnitude of this effect depends on the frequency of users’ social comparisons. These results can inform the design of systems to improve mental well-being on SNS.

As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance might encounter challenges while being applied in the medical domain. With this regard, this work employs and provides the validation of an alternative approach – majority voting – to facilitate appropriate reliance on AI in medical decision-making. This is achieved by a multi-institutional user study involving 32 medical professionals with various backgrounds, focusing on the pathology task of visually detecting a pattern, mitoses, in tumor images. Here, the majority voting process was conducted by synthesizing decisions under AI assistance from a group of pathology doctors (pathologists). Two metrics were used to evaluate the appropriateness of AI reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results showed that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR – by approximately 9% and 31%, respectively – compared to decisions made by one pathologist collaborating with AI. This increased appropriateness resulted in better precision and recall in the detection of mitoses. While our study is centered on pathology, we believe these insights can be extended to general high-stakes decision-making processes involving similar visual tasks.

When we converse, we adapt our behaviors to our interlocutors. The adaptation can serve to indicate our engagement which can also elicit enhancement of the involvement of others. Virtual agents (or socially interactive virtual agents) that play the role of interaction partners can improve the human users’ interaction experience by displaying continuous and adaptive behaviors in real time. Virtual agents have been used in multiple domains to improve user interaction and performance. The promising results of the endowment of adaptation to agents in increasing the agents’ perception and user experience were shown in previous studies. In this paper, we develop an adaptive virtual agent that renders real-time adaptive behaviors based on the behaviors shown by its human interlocutor. The ASAP model rendering reciprocally adaptive agent behavior was employed to realize the system. The system consists of four main parts: perception of social signals, agent adaptive behavior generation, agent visualization (i.e. rendering of the agent’s verbal and nonverbal behavior), and communication of signals. To showcase the usefulness of our adaptive agent, as a proof-of-concept we choose the e-health application of cognitive behavior therapy (CBT), which identifies and rectifies biased and irrational thoughts (or automatic thoughts). Through this study, we show the importance of giving the agent reciprocal adaptation capability notably in enhancing the user experience and the effectiveness of the CBT session. We validate the importance of endowing such adaptation capability by studying the difference between agents that are reciprocally adaptive, solely expressive (with mismatched behavior), and inexpressive (in a still posture) via questionnaires and measures related to the agent perception (naturalness, human-likeliness, synchrony, and engagement) for user experience and the CBT effectiveness (mood, anxiety, stress, and cognitive change). These results highlight the value of making virtual agents adapt in real time. This could lead to agents being capable of providing more personalized and interactive experiences for a wide range of applications. Also, we have collected a new human-agent interaction (HAI) database, HAI-CBT database, which is publicly available to the research community.

An intuitive way to externalize spatial memory is to sketch it. Compared to traditional paper-based sketches, virtual reality (VR) creates new opportunities to investigate the 3D aspect of spatial memory as it empowers users to express 3D information on a 3D interface directly. The goal of this study is to design a 3D sketch mapping tool for researchers and non-expert users without sketching expertise that enables externalizing memories of spatial information after some 3D-critical tasks. There exist 3D sketching tools using VR, but there are two issues with the current mid-air 3D sketching approach: (1) distortion of sketches due to depth perception errors and (2) increased cognitive and sensorimotor demands due to an increased degree of freedom and absence of physical support. To address these problems, we implemented VResin, a novel sketching interface that synergizes 3D mid-air sketching with 2D surface sketching to scaffold 3D sketching into a layer-by-layer process. An experimental study with 48 participants on multi-layer building scenarios showed that VResin supports users in creating less distorted sketches while maintaining the level of completeness and generalization compared to mid-air sketching in VR. We also demonstrate the potential applications that can benefit from 3D sketch maps and the suitability of VResin for a variety of building shapes.

Text-based chatbots are widely used to deliver personalized services by leveraging user-provided information, and anthropomorphic design is crucial for their effectiveness. However, most earlier studies investigated the effects of anthropomorphic design of chatbots while manipulating only one dimension of anthropomorphic cues. The current research investigated how different dimensions of anthropomorphic design cues affect users’ information disclosure tendency towards chatbots. That is, the present study examined the effects of visual appearance (high anthropomorphism vs. low anthropomorphism), manipulating the visual cues dimension, and conversational style (human-like vs. mechanical), manipulating the verbal cues dimension, on users’ information disclosure tendency towards chatbots. Results showed positive effects of human-like conversational style on users’ information disclosure tendency. Of particular significance, an interaction effect between visual appearance and conversational style on users’ information disclosure tendency was found. Users reported a higher information disclosure tendency when the chatbot was designed with anthropomorphic cues consistent over dimensions. This finding suggested that an expectancy violation effect occurs when a chatbot exhibits inconsistent anthropomorphic design cues on two different dimensions. Besides, perceived security was identified as a positive mediating factor in the relationship between conversational style and users’ information disclosure tendency. This study advances research on users’ information disclosure tendency towards anthropomorphic chatbots and highlights the importance of different dimensions of anthropomorphic cues in chatbot design. Additionally, practical guidance for chatbot designers was also provided.

Online peer-production communities (OPPCs) have witnessed producer conflicts that negatively impact their project quality and producer morale. In response to such issues, OPPCs have supported producers to self-manage conflict as its frequency is exceeding the capacity of volunteer moderators. In this paper, we solicit design guidelines reflecting producers’ requirements and perspectives for self-conflict management in an OPPC called OpenStreetMap (OSM). We use the OSM Changeset Discussions user interface – one of OSM’s tools designed to facilitate conflict management – as our target interface. We conduct co-design workshops to collect OSM mappers’ perspectives on the existing conflict management user interface (UI) and re-design alternatives based on mappers’ suggestions. We collect rich reflections on four design prompts covering the User Experience (UX) criteria of utility, usability, desirability, and adoptability, contrasting the current and alternative Changeset Discussion UI designs. Our results lead to two design guidelines: (1) provide gentle and non-intrusive reminders customized based on OSM’s mapper profile and (2) provide customizable mapper profile information displayed during online discussion. We discuss challenges associated with the co-designing process in an OPPC as OSM and how future studies should focus on making incremental changes based on user-participatory methods to minimize any user resistance.