International Journal of Human-Computer Studies最新文献

With the expansive growth of AI’s capabilities in recent years, researchers have been tasked with developing and improving human-centered AI collaborations, necessitating the creation of human–AI teams (HATs). However, the differences in communication styles between humans and AI often prevent human teammates from fully understanding the intent and needs of AI teammates. One core difference is that humans naturally leverage a positive emotional tone during communication to convey their confidence or lack thereof to convey doubt in their ability to complete a task. Yet, this communication strategy must be explicitly designed in order for an AI teammate to be human-centered. In this mixed-methods study, 45 participants completed a study examining how human teammates interpret the behaviors of their AI teammates when they express different positive emotions via specific words/phrases. Quantitative results show that, based on corresponding behaviors, AI teammates were able to use displays of emotion to increase trust in AI teammates and the positive mood of the human teammate. Additionally, our qualitative findings indicate that participants preferred their AI teammates to increase the intensity of their displayed emotions to help reduce the perceived risk of their AI teammate’s behavior. When taken in sum, these findings describe the relevance of AI teammates expressing intensities of emotion while performing various behavioral decisions as a continued means to provide social support to the wider team and better task performance.

Micro-electronics tools, coupled with card-based tools, are employed for prototyping smart devices with non-experts. Lately, researchers have started investigating what tools can actively engage people with intellectual disabilities (ID) in their prototyping. This paper posits itself in this line of work. It presents a toolkit for ID people to rapidly prototype together their own ideas of smart things, for their own shared environment. It analyses and discusses engaging or disengaging features of the toolkit in light of the results of two workshops with eight ID participants. Lessons of broad interest for the design of similar toolkits are drawn from the literature and study findings.

Fall detection cameras at home can detect emergencies of older adults and send timely life-saving alerts. However, the equilibrium between privacy protection and life safety remains a controversial issue when using cameras. In this study, we assessed the attitudes of older adults towards the privacy issue of cameras using surveys (N=389) and interviews (N=20). Furthermore, we conducted a co-design workshop (N=6) in which older adults and designers collaborated to develop a prototype of cameras. We found that for older adults, the disclosure of privacy not only involves a leakage of personal information, but also influences their dignity and control, which has rarely been expressed directly in the past. Our results expand the conceptualisation of privacy and provide novel design implications for smart product development on privacy for older adults.

Virtual Reality (VR) enables the low-cost production of realistic prototypes of buildings at early stages of architectural projects. Such prototypes may be used to gather the experiences of future users and iterate early on in the design. However, it is essential to evaluate whether what is experienced within such VR prototypes corresponds to what will be experienced in reality. Here, we use an innovative method to compare the experiences of patients in a real building and in a virtual environment that plays the role of a prototype that could have been created by architects during the design phase. We first designed and implemented a VR environment replicating an existing ambulatory pathway. Then, we used micro-phenomenological interviews to collect the experiences of real patients in the VR environment (n=8), along with VR traces and first-person point of view videos, and in the real ambulatory pathway (n=8). We modeled and normalized the experiences, and compared them systematically. Results suggest that patients live comparable experiences along various experiential dimensions such as thought, emotion, sensation, social and sensory perceptions, and that VR prototypes may be adequate to assess issues with architectural design. This work opens unique perspectives towards involving patients in User-Centered Design in architecture, though challenges lie ahead in how to design VR prototypes from early blueprints of architects.

This paper investigates the relationship between design research and humancomputer interaction (HCI) in the context of climate change communication and engagement. We discuss current practices in climate change communication and the decrease in concern and engagement caused by “crisis fatigue”. Through Research through Design (RtD), we set out to investigate data humanism and how users react to climate change data, testing approaches to improve engagement. With this purpose, we designed and evaluated Finding Arcadia, an interactive data story that uses data humanism to shift the dialogue from crisis-focused to action-focused. One study with the original IMF visualisations (N = 17) and two studies in public spaces (N = 12 and N = 64) point to the contextualization of the data and presenting actionable solutions helping in engaging users with climate change issues; help in creating solution-focused narratives and interpreting and relating with climate data. From these results, we derive insights for designing empowering interactive data visualizations for resilient climate change engagement.

Considering that a significant portion of the current pedestrian population has limited exposure to automated vehicles (AVs), it is crucial to have a reliable instrument for assessing pedestrians’ initial trust in AVs. Using a survey of 436 pedestrians, this study developed and validated a PITQA (Pedestrians’ Initial Trust Questionnaire for AVs) scale using partial least squares structural equation modeling (PLS-SEM). The proposed scale will be valuable in monitoring the progression of trust over time and considering trust-related factors during the design process. The results revealed that seven key constructs significantly contribute to predicting initial trust between pedestrians and AVs. These constructs include propensity to trust, perceived statistical reliability, dependability and competence, perceived predictability, familiarity, authority/subversion, care/harm, and sanctity/degradation. These shed light on how the trust propensity of individuals, different trust/trustworthiness attributes might constitute different aspects of initial trust in the pedestrian-AV context. The developed scale can be a potentially useful tool for future research endeavors concerning trust calibration and the design of AVs specifically tailored for vulnerable road users.

The integration of emotion recognition capabilities within musical instruments can spur the emergence of novel art formats and services for musicians. This paper proposes the concept of emotionally-aware smart musical instruments, a class of musical devices embedding an artificial intelligence agent able to recognize the emotion contained in the musical signal. This spurs the emergence of novel services for musicians. Two prototypes of emotionally-aware smart piano and smart electric guitar were created, which embedded a recognition method for happiness, sadness, relaxation, aggressiveness and combination thereof. A user study, conducted with eleven pianists and eleven electric guitarists, revealed the strengths and limitations of the developed technology. On average musicians appreciated the proposed concept, who found its value in various musical activities. Most of participants tended to justify the system with respect to erroneous or partially erroneous classifications of the emotions they expressed, reporting to understand the reasons why a given output was produced. Some participants even seemed to trust more the system than their own judgments. Conversely, other participants requested to improve the accuracy, reliability and explainability of the system in order to achieve a higher degree of partnership with it. Our results suggest that, while desirable, perfect prediction of the intended emotion is not an absolute requirement for music emotion recognition to be useful in the construction of smart musical instruments.