Artificially Intelligent (AI) agent systems such as robots are increasingly integrated into the workplace and gaining more power in collaborating with humans. Yet studies on robot power and compliance report mixed findings. To address these inconsistencies, we introduced legitimacy as people’s psychological acceptance of power. Three preregistered experiments were conducted (N = 431). In Experiment 1 and 2, we manipulated power assignment (robot power vs. human power), and legitimacy of power (legitimate, illegitimate, no explanation) through competence and procedural fairness. The results showed that participants complied more to the legitimate robot power than illegitimate one. In Experiment 3, we examined whether perceptions of legitimacy would emerge naturally in more ecologically valid collaboration. Results of multigroup mediation model showed that the robot leader was perceived as less legitimate than the human leader, which accounted for the reduced compliance to the robot’s decisions. In all three experiments, people’s perceived social attributes of robots with power and their affective responses after the interaction were negatively affected. This study underscores the importance of legitimacy in understanding power and compliance in human-robot collaboration.

Large language model tools such as ChatGPT are increasingly used for information seeking in place of traditional search engines. However, limited research is available regarding how lay users search for and evaluate information using ChatGPT. By using scenario-based interviews, this study explores how lay users employ ChatGPT for information seeking in-situ, addressing a significant research gap. Sixteen participants completed a set of search tasks and reflected how they sought and evaluated information. The findings demonstrate that although the information seeking process and techniques resembled those commonly used in other information seeking systems, many techniques were missing. While ChatGPT's responses may reduce cognitive barriers in the information seeking process, lay users face challenges in effectively framing prompts and have concerns over transparency of information source and information quality.

Nowadays, the spread of misinformation is a prominent problem in society. Our research focuses on aiding the automatic identification of misinformation by analyzing the persuasive strategies employed in textual documents. We introduce a novel annotation scheme encompassing common persuasive writing tactics to achieve our objective. Additionally, we provide a dataset on health misinformation, thoroughly annotated by experts utilizing our proposed scheme. Our contribution includes proposing a new task of annotating pieces of text with their persuasive writing strategy types. We evaluate fine-tuning and prompt-engineering techniques with pre-trained language models of the BERT family and the generative large language models of the GPT family using persuasive strategies as an additional source of information. We evaluate the effects of employing persuasive strategies as intermediate labels in the context of misinformation detection. Our results show that those strategies enhance accuracy and improve the explainability of misinformation detection models. The persuasive strategies can serve as valuable insights and explanations, enabling other models or even humans to make more informed decisions regarding the trustworthiness of the information.

Fear of artificial intelligence (AI) has become a predominant term in users’ perceptions of emerging AI technologies. Yet we have limited knowledge about how end users perceive different types of fear of AI (e.g., fear of artificial consciousness, fear of job replacement) and what affordances of AI technologies may induce such fears. We conducted a survey (N = 717) and found that while synchronicity generally helps reduce all types of fear of AI, perceived AI control increases all types of AI fear. We also found that perceived bandwidth was positively associated with fear of artificial consciousness, but negatively associated with fear of learning about AI, among other findings. Our study provides theoretical implications by adopting a multi-dimensional fear of AI framework and analyzing the unique effects of perceived affordances of AI applications on each type of fear. We also provide practical suggestions on how fear of AI might be reduced via user experience design.

Sleep has been studied as an individual activity, with the interconnected behaviors among family members being rarely considered. In this study, by incorporating the identified themes from the previous phase, we designed and tested two types of family-based sleep management systems: Bedtime Pal and Caring Heart. These systems redistributed sleep-relevant tasks among family members, so that they would have the chance to reflect on the difficulties and values involved in those tasks. We deployed the two prototypes and performed an in-the-wild study with 12 families in their homes. This study empirically revealed the importance of considering social dynamics as a design factor for family sleep management technologies. Design implications for sleep management technologies are discussed.

Artificial intelligence (AI) applications have become an integral part of our society. However, studying AI as one entity or studying idiosyncratic applications separately both have limitations. Thus, this study used computational methods to categorize ten different AI roles prevalent in our everyday life and compared laypeople’s perceptions of them using online survey data (N = 727). Based on theoretical factors related to the fundamental nature of AI, the principal component analysis revealed two dimensions that categorize AI: human involvement and AI autonomy. K-means clustering identified four AI role clusters: tools (low in both dimensions), servants (high human involvement and low AI autonomy), assistants (low human involvement and high AI autonomy), and mediators (high in both dimensions). Multivariate analyses of covariances revealed that people assessed AI mediators the most and AI tools the least favorably. Demographics also influenced laypeople’s assessments of AI. The implications of these results are discussed.

The emerging uptake of AI chatbots for social support entails systematic comparisons between human and non-human entities as sources of support. In a between-subject experimental study, a human and two types of ostensible chatbots (using a wizard of oz design) had supportive conversations with college students who were experiencing stressful situations during the pandemic. We found that when compared with a less ideal chatbot (i.e., low-contingent chatbot), (1) the human support provider was perceived with more warmth, which directly reduced emotional distress among participants; (2) the ideal chatbot (i.e., high-contingent chatbot) was perceived to be more competent, which activated participants' cognitive reappraisal of their stressful situations and subsequently reduced emotional distress. The human provider and the ideal chatbot did not differ in users' perceived competence or warmth, although the human provider was more effective at activating participants' cognitive reappraisal. This study integrates human communication theories into human-computer interaction work and contributes by positioning and theorizing user perceptions of chatbots in a larger process from support sources with varying communication competence to users' cognitive and emotional responses, and ultimately to the stress outcome. Theoretical and design implications are discussed.

Sleep is a vital health issue. Continued sleep deficiency can increase the chance of stroke, cardiovascular disease, obesity, and diabetes. Previous studies have investigated sleep as an individual activity performed within bedrooms at night. In this study with twenty parents of young children, we identify sleep as a complex experience entangled with social dynamics between family members. For example, children's sleep means not just time for children to rest, but time for self-care for parents. This paper's contributions are twofold. First, we show how the boundaries that define sleep in terms of time (at night), space (in bedrooms), and unit of analysis (individual-focused) limit designers' opportunities to tackle the deeper sleep issues of families. Second, we suggest "division of labor" as an important but rarely discussed design concept to enhance family sleep, and as a design theme for home technologies that address issues emerging from social dynamics between householders.

Minjin Rheua*, Ji Youn Shina, Wei Penga & Jina Huh-Yooba Media and Information, Michigan State University, East Lansing, Michigan, USAb Department of Information Science, Drexel University, Philadelphia, Pennsylvania, USAMinjin Rheu is a Ph.D. candidate in the Department of Media and Information at Michigan State University. Her research delves into how specific designs or features of communication technology influences users’ motivational and cognitive processes through which people change or adopt their health and prosocial behaviors.Ji Youn Shin is a Ph.D. student in the Department of Media and Information at Michigan State University. Her research includes human-computer interaction, human-centered design, and health communication. She designs and evaluates technologies for supporting individuals’ health and wellness in various health contexts, including family caregiving and pediatric chronic illnesses.Wei Peng is a Professor in the Department of Media and Information, Michigan State University. Her research focuses on the psychological and social mechanisms of behavior change and their application in the design of interactive media for health and wellness promotion.Jina Huh-Yoo is an Assistant Professor of Human-Computer Interaction at Drexel University's College of Computing & Informatics. Her research areas include human-computer interaction, health informatics, and research ethics of health informatics technologies. She was a PI of NIH and NSF-funded projects in promoting health and wellness through consumer-facing technologies.CONTACT Minjin Rheu [email protected] Media and Information, Michigan State University, 404 Wilson Rd, Room 414, East Lansing, MI 48823, USAABSTRACTOff-the-shelf conversational agents are permeating people’s everyday lives. In these artificial intelligence devices, trust plays a key role in users’ initial adoption and successful utilization. Factors enhancing trust toward conversational agents include appearances, voice features, and communication styles. Synthesizing such work will be useful in designing evidence-based, trustworthy conversational agents appropriate for various contexts. We conducted a systematic review of the experimental studies that investigated the effect of conversational agents’ and users’ characteristics on trust. From a full-text review of 29 articles, we identified five agent design-themes affecting trust toward conversational agents: social intelligence of the agent, voice characteristics and communication style, look of the agent, non-verbal communication, and performance quality. We also found that participants’ demographic, personality, or use context moderate the effect of these themes. We discuss implications for designing trustworthy conversational agents and responsibilities around on stereotypes and social norm building through agent design.

Diverse fields, including CSCW, Communication, and Human Development studies, have investigated how technologies can better support parent-child relationships. While these studies are scattered across literature, little effort has been made to synthesize the findings. We conducted a review of studies that examined the factors associated with parent-child relationships that are mediated by technologies. Specifically, we synthesized previous studies based on children's age groups and different family contexts, including cohabitation. From a total of 12,942 search results from two databases, and 32 results from the hand-searching process, we conducted a full-text review of 190 articles and identified 19 suitable studies. An additional search during the revision cycle resulted in 6 more full-text reviews and 1 additional study being included in the data analysis. We analyzed challenges and facilitators in designing CSCW systems supporting parent-child relationships for families living together or apart and families with children of different developmental stages. Findings showed two common challenges, which should be addressed in technology designed to support parent-child relationships: discrepancies in expected communication between parents and child(ren) and the complex emotions of parents toward parenting caused by their busy schedules. Challenges specific to families who are living apart included consequences from being physically distant and having limited access to communication resources. The following factors commonly helped facilitate parent-child relationships: (1) reciprocity norms of the family (2) reinforcement of transparency, affection, and trust, (3) a physical proxy of each other through an object or interface design, (4) accessibility, the sophistication level of technology, and communication resources, (5) enjoyable, age-appropriate shared content among parents and children, and (6) situational awareness and routine as ways to increase parent-child relationships. Media richness and synchronicity in system design and privacy preservation without interruption facilitated parent-child relationships of families living apart. Based on the findings, we discuss opportunities for technological innovation for physically co-located families and the importance of considering children's age and developmental stages in designing technology for parent-child relationships.

By learning from the existing family mealtime activities, family members can be motivated to make the positive changes towards better relationships, which are important for the physical and mental health of children. Moreover, the details of family mealtime activities provide rich information for study in sociology and culture. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing, etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family with a CRFs-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. FamilyLog can detect those events with high accuracy across different families and home environments.

Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog - a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.

In this paper, we introduce an interdisciplinary project, involving researchers from the fields of Physical Therapy, Computer Science, Psychology, Communication and Cell Neurobiology, to develop an integrated virtual reality, haptics and modern sensing technique system for post-stroke rehabilitation. The methodology to develop the system includes identification of movement pattern, development of simulated task and diagnostics. Each part of the methodology can be achieved through several sub-steps that are described in detail in this paper. The system is designed from Physical Therapy perspective that can address the motor rehabilitation needs of stroke patients. The system is implemented through stereoscopic displays, force feedback devices and modern sensing techniques that have game-like features and can capture accurate data for further analysis. Diagnostics and evaluation can be made through an Artificial Intelligence based model using collected data and clinical tests have been conducted.