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AbstractAlthough an increasing number of studies explore the factors influencing users’ privacy concerns regarding social robots, the existing understanding of this issue remains largely fragmented. Previous studies have mainly focused on the "net effect" between variables, leaving the complexity of causal configurations, and the holistic impact of design characteristics of social robots on user privacy concerns remains unclear. Based on the Stimuli-Organism-Response (S-O-R) framework and Communication Privacy Management Theory (CPMT), this study integrates social robot design characteristics such as Anthropomorphism, Warmth, Competence, and Transparency into causal configurations, and uses Perceived Privacy Risk and Perceived Privacy Control as mediating variables to propose a Comprehensive conceptual model. Based on valid data from a sample of 198 Chinese social robot users, this study conducted empirical analyses of the conceptual model using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (FsQCA). PLS-SEM results show that anthropomorphism, warmth, competence, and transparency are key factors influencing privacy concerns, and perceived privacy risk mediates the relationship between warmth, information transparency, and privacy concerns. The FsQCA results further validated the findings of PLS-SEM and identified five configurations of factor combinations that led to higher levels of user privacy concerns. Among them, the combination of high anthropomorphism design, high competence, and low warmth of social robots is the core configuration that leads to users’ privacy concerns. Overall, this study broadens our understanding of social robot users’ privacy concerns and reveals the causal complexity behind social robot users’ privacy concerns. It provides some theoretical and practical insights for subsequent scholars and designers. [ABSTRACT FROM AUTHOR]

Non-verbal communication, especially eye contact, plays a crucial role in human interaction. Integrating eye contact capabilities into robotic behavior presents challenges that require comprehensive interdisciplinary collaboration from the fields of vision science and robotics. This study introduces an innovative methodology to improve the user's perception of the tabletop social robot Haru's direct gaze by explaining how eye and head movements can be managed through simulating visual perception processing. By considering the mechanisms of eye and head movements during visual perception processing, the robot's eye and head gaze behaviors are intended to replicate human gaze movements with Vestibulo-Ocular Movement (VOM) during face-to-face interaction, ultimately enhancing the user's perception of the robot's direct gaze. [ABSTRACT FROM AUTHOR]

The presence of social robots in children's daily environments has steadily increased. With the advancement of artificial intelligence (AI), social robots have influenced children's learning and development. This study innovatively utilized the Web of Science database and conducted a bibliometric analysis of 517 publications on social robots supporting children's learning and development before September 2022. Unlike most existing reviews, this study employed a synergistic combination of two complementary visualization tools, VOSviewer and CiteSpace, to map the intellectual structure and analyze the knowledge evolution path in this emerging interdisciplinary field. Specifically, VOSviewer generated visualizations depicting collaboration networks, research hotspots, and trends based on co-occurrences. CiteSpace enabled quantitative measurements of node centrality and burstness to reveal pivotal entities and emerging topics. Combining visual mapping and quantitative analysis by VOSviewer and CiteSpace allowed comprehensive landscape mapping for an in-depth investigation into the development of this field. This study proposes future research directions, including children's perceptions of social robots, social robots enhancing children's learning, social robots supporting children's social and emotional development, and social robots for children with special needs. The findings also inform the design and application of child-friendly social robots equipped with generative AI techniques. [ABSTRACT FROM AUTHOR]

In a diverse society, a crucial aspect of developing social robots is their ability to adapt to each individual's characteristics. There are two types of adaptation: physical and social. In pHRI, attention is paid to physical adaptation, but by investigating the social meaning of physical adaptation, we can extend it to social adaptation. Although it is a physical interaction, a handshake is a social action with complex meanings. We realized a handshaking robot with a Rowat-Selverston CPG controller that can synchronize with human movements. This study aims to clarify the relationship between physical and social adaptation by examining individual characteristics inferred from the internal state of the robot and human-robotic social attributes. Our finding is that there is a correlation between the internal state of the robot and some robotic social attributes. It was also found that the length of the handshake had little effect on these social perceptions. This study highlights the complex interplay between robot physical adaptability and social attribution, providing a foundation for developing robots capable of personalized and socially meaningful interactions. [ABSTRACT FROM AUTHOR]

Low-stakes assessment has gained attention in recent years due to its link to enhancing learning effects and its essential role in learning evaluation. Unlike high-stakes assessments, low-stakes assessments have little or no consequences for learners' academic performance, and are designed to support the feedback-oriented learning process. Providing multiple low-stakes assessments to students yields significantly greater long-term retention of knowledge and skills. However, learners may not give their best efforts when taking low-stakes assessments, which could lead to poorer learning outcomes. Using emerging technologies such as social robots in the learning environment could foster interactive learning, engagement, and motivation for learning assessments. Therefore, integrating low-stakes assessments and robots might encourage students to exert greater effort while performing learning tasks. This study aimed to discover the impacts of robot-based multiple low-stakes assessments on students' oral presentation performance, collective efficacy, and learning attitude. A quasi-experiment was conducted in two sixth-grade classes of elementary students. The Robot-based Multiple Low-Stakes Assessment (Robot-MLSA) was randomly assigned to one class, while the Computer-based Multiple Low-Stakes Assessment (C-MLSA) was assigned to another class. The findings showed that the Robot-MLSA could enhance students' oral presentation performance, support their collective efficacy, and improve their learning attitude toward robots. Furthermore, an in-depth discussion of students' learning perceptions and experience is provided to explore the effectiveness of the Robot-MLSA. [ABSTRACT FROM AUTHOR]

The corporeality of social robots can be broken down into anthropomorphic (humanoid), zoomorphic (animal-like), mechanoid (machine-like), and functional (use-based). The effects of these corporeal forms and their functions have been investigated within prior research; however, the benefits of each form and how they may relate to social presence still need investigation. 95 participants were randomly assigned to interact with either Lynx (humanoid), Vector (mechanoid), or Alexa Echo (functional) and then answered questionnaires related to the robot's embodiment and perceived social presence. There is supporting evidence that social presence is explained by a few key factors of embodiment, but not all of them. (shared) perceptions and interpretations were found to be a requirement for social presence in robots. Once met, the robot's motion seems to be the most important factor for improving and predicting emotional and behavioral interdependence. Based on these findings, robot development for social purposes would benefit by implementing different movements that can help build rapport. [ABSTRACT FROM AUTHOR]

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social communication, social interaction, and restricted behaviors. The importance of early intervention has been widely demonstrated, and developmental trajectories in ASD emphasize the importance of nonverbal communication, such as intransitive gesture production, as a possible positive prognostic factor for language development. The use of technological tools in the therapy of individuals with ASD has also become increasingly important due to their higher engagement and responsiveness to technological objects, such as robots. Materials and methods: We developed a training protocol using the humanoid robot NAO, called IOGIOCO (Interactive mirroring Games wIth sOCial rObot), based on the use of intransitive gestures embedded in naturalistic dialogues, stimulating a triadic interaction between child, robot and therapist. The training was divided into six levels; the first 2 levels were called "familiarization levels," and the other 4 were "training levels". The technological setup includes different complexity levels, from mirroring tasks to building spontaneous interactions. We tested the protocol on 10 preschool children with ASD (aged 2-6 years) for 14 weeks. We assessed them at recruitment (T0), at the end of training (T1), and after 6 months (T2). Results: We demonstrated the tolerability of the protocol. We found that one group (n=4, males and 2 females) reached the training level, while another and group (n=6 males) remained at a familiarization level (mirroring), we analyzed the results for the two groups. In the group that reached the training levels, we found promising results, such as an improvement in the Social Adaptive Domain of the ABAS-II questionnaire between T0 and T2. Conclusion: While current results will need a Randomized Controlled Trial to be confirmed, the present work sets an important milestone in using social robots for ASD treatment, aimed at impacting social and communication skills in everyday life. [ABSTRACT FROM AUTHOR]

As a technical application in artificial intelligence, a social robot is one of the branches of robotic studies that emphasizes socially communicating and interacting with human beings. Although both robot and behavior research have realized the significance of social robot design for its market success and related emotional benefit to users, the specific design of the eye and mouth shape of a social robot in eliciting trustworthiness has received only limited attention. In order to address this research gap, our study conducted a 2 (eye shape) × 3 (mouth shape) full factorial between-subject experiment. A total of 211 participants were recruited and randomly assigned to the six scenarios in the study. After exposure to the stimuli, perceived trustworthiness and robot attitude were measured accordingly. The results showed that round eyes (vs. narrow eyes) and an upturned-shape mouth or neutral mouth (vs. downturned-shape mouth) for social robots could significantly improve people's trustworthiness and attitude towards social robots. The effect of eye and mouth shape on robot attitude are all mediated by the perceived trustworthiness. Trustworthy human facial features could be applied to the robot's face, eliciting a similar trustworthiness perception and attitude. In addition to empirical contributions to HRI, this finding could shed light on the design practice for a trustworthy-looking social robot. [ABSTRACT FROM AUTHOR]

By converging 3A—artificial intelligence (AI), augmented reality (AR), and anime culture, a new type of non-naturalistic and stylized anthropomorphism has been exercised on the latest hologram social robot—"Hupo." By combining scopes of human–machine communication and cultural studies, this study investigated how this 3A convergence constructed the user experience of Hupo. Through in-depth interviews with 25 users, we revealed their expected experience related to the dual identity of anime otaku and technology geeks alongside social segregation and loneliness. We found their actual experience highlighted how anime elements offset their dissatisfactions caused by AI weakness and the uncanny valley. Three specific anime-enabled strategies were illustrated, namely anime characterization, gamification, and idolization. Our analysis suggested that pop culture offers not technical solutions but temporary compensation for the current AI limitations. Nevertheless, such fusions between anime otakuism and instinct-served technologies also raise concerns about a further animalized human society. [ABSTRACT FROM AUTHOR]

The article deals with the development of human-robot interaction, where the robot measures the user's emotional state as well as environmental factors by different devices, and it responds to the user with colorful patterns and controls the smart home through the guidance of the Internet of Things system. Before the design process, robots and their functions currently on the market, the role of emotions in communication, and technologies for measuring emotion (such as face recognition, measurement of heart rate, breath, and physical changes) are presented in detail. The designed robot (Em-Patty) uses a previously developed emotion-based automatic pattern generation system based on a fuzzy system, which is a suitable tool for handling emotions. A main and three fuzzy subsystems are fusioned in order to create the most efficient control system for Em-Patty. The nature-inspired design of the robot is also presented, as well as a description of its behavior with the help of a specific case study. [ABSTRACT FROM AUTHOR]

Social robots require the ability to understand human emotions and provide affective and behavioral responses during human-robot interactions. However, current social robots lack empathy capabilities. In this work, we propose a novel Multi-modal Hierarchical Empathetic (MHE) framework for generating empathetic responses for social robots. MHE is composed of a multi-modal fusion and emotion recognition module, an empathetic dialogue generation module, and an expression generation module. By fusing the sensor signals of different modalities, the robot can recognize human emotions and generate affective responses. Multiple experiments are conducted on a real robot, Pepper, to evaluate the proposed framework. The experiments are conducted to discriminate between MHE-generated text and human responses in complete ignorance, and most experimenters agree that MHE can effectively generate human-like and empathetic responses. To better evaluate the similarity between human-robot and human-human interactions, a period eye movement map (PEM) captured by an eye tracker is proposed. The experimental results demonstrate the improvement in the MHE in human-robot interactions by comparing different PEMs. [ABSTRACT FROM AUTHOR]

The social integration of older adults has been shown to be vital for successful aging. Innovative communication technologies, such as telepresence robots, can protect older adults against loneliness and social isolation by helping them stay connected to their social networks. This human-centered qualitative study aims to identify the attitudes (research question 1, RQ1), intentions to use (RQ2), and requirements (RQ3) of older adults for robot-mediated communication (RMC) via a telepresence robot. Semi-structured individual interviews were conducted with N = 30 older adults from Germany, who evaluated storyboard illustrations depicting a fictional RMC scenario between a grandparent and their adult grandchild. The study identified 3 attitude groups towards telepresence robots among participants: positive, negative, and conflicting attitudes (RQ1). Furthermore, based on their intentions of use, participants were classified into reluctant future users (n = 12), future non-users (n = 10), and enthusiastic future users (n = 8) (RQ2). Finally, the study identified technological, social, and age-related requirements of older adults for a telepresence robot (RQ3). Practical recommendations are provided based on these findings, such as leveraging older adults' current technology-related skills, adapting technologies to older adults' lifestyles and social networks, and designing technologies that contribute to older adults aging in place. This study contributes to the human-centered design of telepresence robots that support the social integration of older adults. [ABSTRACT FROM AUTHOR]

The need for safe, predictable, and reliable robot navigation is fundamental for mobile robots to move around in home and office environments. Shortest-path navigation is a popular robot navigation method that uses the most efficient path to get to the desired goal. This behaviour is not always easy to interpret, understand, and avoid in a congested hallway. Instead, more predictable navigation methods, such as a robot following a wall, can help increase social acceptance and help avoid the robot crossing the pedestrian path. If a robot follows along a wall, a key variable to consider is the preferred driving side of the robot (left or right) in areas such as in narrow passages, and its perceived impact on social acceptance. This international user study (n = 143) involved an online video-based test to compare robot evaluation and social acceptance for two types of mobile navigation (Wall-Following and Shortest Path), including the preferred driving side for Wall-Following. A Fetch robot navigated from start to goal position in a series of indoor scenarios with a pedestrian. Select scenarios included a hallway, doorway, and intersection. Independent Sample T-Tests results found that Wall-Following was rated significantly higher than Shortest Path for being perceived as more comfortable and predictable, regardless of robot driving side. The preference for the driver side of the robot did not match the country of residence, nor did it have a significant impact on robot ratings. There were significant interaction effects for comfort, safety and predictable scores across two timepoints. Given the popularity of Shortest Path navigation, the findings indicate that this approach might not be the most appropriate for human settings. Additional investigation into Wall-Following behaviours is recommended for social acceptance, even if the method compromises the efficiency of the robot to acheive its objective. [ABSTRACT FROM AUTHOR]

One of the most important aspects in the design of a social robot is its visual appeal, with the design of its head playing a particularly important role in this regard. The head design for social robots has been developed using a variety of ways; one that has become popular today is the use of an in-head projector to create a 3D face for the robot. In this research, we review the design specifications and development stages of the Taban 1 and Taban 2 social robots, which were developed for communication with children in educational sessions. One notable feature of these robots is the presence of a projector located inside the back of the head, which displays the image of different characters on various 3D masks, enhancing the robot's appeal and preventing children from getting bored with the interaction. Due to the low attractiveness of the Taban 1, the Taban 2 robot was developed to increase its desirability. The study explores the conceptual and detailed design of the robots, including their hardware and software components. As children prefer a more cartoon-like horizontal face, this study also highlights the advantages of a horizontal face design, allowing for more cartoon-like characters. To evaluate the effectiveness of child–robot interaction and to study whether the Taban 2 robot is more attractive to children than the Taban 1 or not, acceptance sessions were conducted. The participants expressed high satisfaction and positive reception towards Taban 2, considering it a likable, intelligent, and safe technological teaching aid. [ABSTRACT FROM AUTHOR]

Remote, online learning provides opportunities for flexible, accessible, and personalised education, regardless of geographical boundaries. This study mode also promises to democratise education, making it more adaptable to individual learning styles. However, transitioning to this digital paradigm also brings challenges, including issues related to students' mental health and motivation and communication barriers. Integrating social robots into this evolving educational landscape presents an effective approach to enhancing student support and engagement. In this article, we focus on the potential of social robots in higher education, identifying a significant gap in the educational technology landscape that could be filled by open-source learning robots tailored to university students' needs. To bridge this gap, we introduce the Robotic Study Companion (RSC), a customisable, open-source social robot developed with cost-effective off-the-shelf parts. Designed to provide an interactive and multimodal learning experience, the RSC aims to enhance student engagement and success in their studies. This paper documents the development of the RSC, from establishing literature-based requirements to detailing the design process and build instructions. As an open development platform, the RSC offers a solution to current educational challenges and lays the groundwork for personalised, interactive, and affordable AI-enabled robotic companions. [ABSTRACT FROM AUTHOR]

Social robots that are fabricated to interact with humans and to help them in education, healthcare, etc., are required to have an interactive behavior similar to humans. One of the important interactive behaviors of humans is social eye gaze. Eye gaze is significantly more important than other nonverbal signals; it is shown that eyes are special cognitive stimuli with unique hardwired pathways in the brain dedicated to their interpretation. Studying the literature, we found out that in previous research conducted to control the social robots’ gaze behavior, human gaze behavior was investigated in some limited situations, such as two- or three-way conversation, in order to extract the pattern of this behavior. Therefore, increasing the variety of studied social situations is a way to fill this gap. In order to design a gaze control system for a social robot, details about human gaze behavior must be found. The purpose of this research is to propose an empirical motion-time pattern for human gaze behavior in a number of different social situations; these situations include scenes with 2 to 4 people in a prepared video where the people in the scene show the social behaviors of "talking", "waving", "pointing", "entering the scene" and "exiting the scene" in a structured way. Fifteen normal adults (mean age: 24 and std: 3.3 years) watched this movie, and their gaze positions were recorded using an eye tracker system (SR-Research EyeLink 1000 plus). Next, by using the genetic algorithm (which is an optimization process), we were able to extract the relative coefficient of each of the mentioned social behaviors in our proposed model. The results of reconstructing the participants' gaze on the test data are very similar to the real performance of the subjects. Finally, the ability to implement this model was successfully tested by implementing it on a Nao robot, and its positive performance was confirmed using a survey. The model showed significant differences between the two studied situations in 3 questions out of the whole survey’s 10 questions.

In recent years, in response to the effects of Covid-19, there has been an increase in the use of social robots in service organisations, as well as in the number of interactions between consumers and robots. However, it is not clear how consumers are valuing these experiences or what the main drivers that shape them are. Furthermore, it is an open research question whether these experiences undergone by consumers can be affected by their own personality. This study attempts to shed some light on these questions and, to do so, an experiment is proposed in which a sample of 378 participants evaluate a simulated front-office service experience delivered by a social robot. The authors investigate the underlying process that explains the experience and find that cognitive-functional factors, emphasising efficiency, have practically the same relevance as emotional factors, emphasising stimulation. In addition, this research identifies the personality traits of the participants and explores their moderating role in the evaluation of the experience. The results reveal that each personality trait, estimated between high and low poles, generates different responses in the evaluation of the experience. [ABSTRACT FROM AUTHOR]

Recent, rapid advancements in technology have enabled the introduction and integration of robots into everyday life. Different from the traditional image of robots as cold and mechanical, social robots are designed to emulate human interaction patterns, improving the user experience and facilitating social interactivity. Thus, social robots represent a promising new care intervention. In this article, after defining social robots and explaining the factors influencing "human-robot interaction", the authors discuss the effectiveness of social robots in the context of providing care to patients with dementia and autism as well as to pediatric patients. Finally, current cases in which PARO, a social robot, has been used in nursing are described, and key challenges and suggestions for future social robot applications are given. Current evidence indicates social robots must be developed and designed to adhere to a people-centered approach to achieve better robot-assisted care outcomes, be better accepted by patients, and better enable patients to open up emotionally and maintain good physical, mental, and social well-being. [ABSTRACT FROM AUTHOR]

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Mental health and psychological distress are rising in adults, showing the importance of wellbeing promotion, support, and technique practice that is effective and accessible. Interactive social robots have been tested to deliver health programs but have not been explored to deliver wellbeing technique training in detail. A pilot randomised controlled trial was conducted to explore the feasibility of an autonomous humanoid social robot to deliver a brief mindful breathing technique to promote information around wellbeing. It contained two conditions: brief technique training ('Technique') and control designed to represent a simple wait-list activity to represent a relationship-building discussion ('Simple Rapport'). This trial also explored willingness to discuss health-related topics with a robot. Recruitment uptake rate through convenience sampling was high (53%). A total of 230 participants took part (mean age = 29 years) with 71% being higher education students. There were moderate ratings of technique enjoyment, perceived usefulness, and likelihood to repeat the technique again. Interaction effects were found across measures with scores varying across gender and distress levels. Males with high distress and females with low distress who received the simple rapport activity reported greater comfort to discuss non-health topics than males with low distress and females with high distress. This trial marks a notable step towards the design and deployment of an autonomous wellbeing intervention to investigate the impact of a brief robot-delivered mindfulness training program for a sub-clinical population. [ABSTRACT FROM AUTHOR]

Social robots facilitate improved human–robot interactions through nonverbal behaviors such as handshakes or hugs. However, the traditional methods, which rely on precoded motions, are predictable and can detract from the perception of robots as interactive agents. To address this issue, we have introduced a Seq2Seq-based neural network model that learns social behaviors from human–human interactions in an end-to-end manner. To mitigate the risk of invalid pose sequences during long-term behavior generation, we incorporated a generative adversarial network (GAN). This proposed method was tested using the humanoid robot, Pepper, in a simulated environment. Given the challenges in assessing the success of social behavior generation, we devised novel metrics to quantify the discrepancy between the generated and ground-truth behaviors. Our analysis reveals the impact of different networks on behavior generation performance and compares the efficacy of learning multiple behaviors versus a single behavior. We anticipate that our method will find application in various sectors, including home service, guide, delivery, educational, and virtual robots, thereby enhancing user interaction and enjoyment. [ABSTRACT FROM AUTHOR]

The use of new assistive technologies in general, and Socially Assistive Robots (SARs) in particular, is becoming increasingly common for supporting people's health and well-being. However, it still faces many issues regarding long-term adherence, acceptability and utility. Most of these issues are due to design processes that insufficiently take into account the needs, preferences and values of intended users. Other issues are related to the currently very limited amount of long-term evaluations, performed in real-world settings, for SARs. This study presents the results of two regional projects that consider as a starting hypothesis that the assessment in controlled environments and/or with short exposures may not be enough in the design of an SAR deployed in a retirement home and the necessity of designing for and with users. Thus, the proposed methodology has focused on use-cases definitions that follow a human-centred and participatory design approach. The main goals have been facilitating system acceptance and attachment by involving stakeholders in the robots design and evaluation, overcoming usage barriers and considering user's needs integration. The implementation of the first use-case deployed and the two-phase pilot test performed in a retirement home are presented. In particular, a detailed description of the interface redesign process based on improving a basic prototype with users' feedback and recommendations is presented, together with the main results of a formal evaluation that has highlighted the impact of changes and improvements addressed in the first redesign loop of the system. [ABSTRACT FROM AUTHOR]

We are facing the rapid development of educational technology and social robots tested in classrooms. Research has identified teachers' caution and concerns about these robots' social skills. Pre-service education is critical for forming beliefs and preparing teachers for the future classroom and innovations in educational technology. In the present study, exploratory factor analysis is applied to examine pre-service teachers' concerns about social robots' instructional integration in the role of social agents interacting with children. We apply a concerns scale encompassing the instructional and socio-emotional concerns regarding robots' instructional integration in the classroom environment. In this study, the scale, which was developed in Slovenia, is examined in the Russian cultural context. Based on the concerns scale, exploratory factor analysis identifies a one-factor solution with five statements (of a six-item factor) shared with the Slovene sample, adding three statements focusing on the importance of the teacher's role. Russian pre-service teachers share concerns with Slovene pre-service teachers and further highlight the authenticity of unique human relationships and interactions. Slovenian pre-service teachers are more focused on children's social skills and well-being, while Russian participants give special attention to the teacher's role and value and believe that it would be wrong to place the robot in a classroom for such a purpose. They do not consider the robot's human-like interaction skills sufficient for it to be assigned the role of a social agent and interaction partner in the classroom. The inappropriateness of the robot for pedagogical interactions and relationships is the basis of all their concerns. The Kruskal–Wallis test identified the moderate magnitude of the difference between the groups (ε2 = 0.07–0.12), with Russian pre-service teachers presenting the strongest reluctance towards authenticity-imbued social robots in pedagogical roles. The authors emphasize the need to clearly state stakeholders (roboticists, teachers, children, parents) in the research design and their roles in the evaluation of robot implementation. [ABSTRACT FROM AUTHOR]

Spoken communication between social robotic devices, powered by generative AI tools such as ChatGPT, and the senior population offers great potential for researching social interaction and robot identity perceptions as well as exploring the potential opportunities and challenges when implementing this human-machine interactions in real life situations and health care. In this paper we explore people's perceptions of the social robot Furhat when administering verbal tasks similar to those used in screening for Alzheimer's disease. We describe the Slovak system mounted on the robot that includes a speech recognizer, the scenarios powered by generative large language model ChatGPT, and a speech synthesizer. We tested the functionality of the proposed approach with two groups of participants: attendees of a large science fair and a scientific conference. The observations from 87 collected questionnaires suggest good potential and applicability of such an approach for the given task and more positive attitudes of older subjects compared to younger ones. [ABSTRACT FROM AUTHOR]

The home robot-based child activity service aims to cultivate children’s social emotions. A design theme was produced by interviewing child development experts and parents. The activity service is composed of 50 plays and 70 conversations. These were developed based on activities from psychomotor therapy and the guidelines of Ministry of Early Childhood Education in South Korea. In the field test, 50 children aged five–seven years participated to experience the activity services at home for 4 days. After completing the 4 days of field testing, we conducted customer satisfaction (CSAT) surveys, Godspeed evaluations and interviews to quantitatively and qualitatively verify the evaluations by the children and parents. As a result, 92% of the children and 80% of the parents evaluated that they were satisfied with the service. In addition, our results revealed that the social robot-based service contributed to improving the relationship between children and families by functioning as a messenger. Finally, the lessons learned from the service development and field tests were discussed to aid service designers and robotics engineers.

[Purpose/Significance] The advent and emergence of social robots represent a closer development trend in human-computer interaction. However, the study of the information behavior of social robots faces many challenges that arise from the need to simulate human social behavior. This challenge includes technical hurdles such as a multi-level understanding of human emotions, extraction of multi-modal information features, situational awareness, as well as the establishment of long-term user profiling, data privacy, and ethical considerations in personalized interaction. However, existing research tends to focus narrowly on specific applications and lacks a holistic review. This paper attempts to provide a thorough review of both domestic and international studies of social robots in the area of information behavior. It aims to elucidate the theoretical evolution and technological foundations of social robots, thereby enriching our understanding of their role in the landscape of information behavior research. [Method/Process] Using a rigorous literature review methodology, we meticulously analyze the current state and prospective trajectory of research on the information behavior of social robots. First, we extract and scrutinize the theoretical foundations and salient research topics within the field. We then delineate the core tasks of social robots, which include data acquisition, language processing, emotion analysis, information retrieval, and intelligent communication. Furthermore, we synthesize research on the information behavior of social robots in various application domains such as education, healthcare, and service sectors. We delve into the intricacies of human-computer interaction in these contexts and provide comprehensive insights. Finally, we explore future directions in the field. [Results/Conclusions] Our examination of the information behavior of social robots reveals both promising potential and notable challenges. This paper provides a fundamental elucidation of the social robot concept, identifies current research foci, and addresses prevailing challenges. Regarding the construction of data resource and related technologies, we systematically delineate the task architecture of social robots, and highlight their wide-ranging applications in various domains. Furthermore, we provide an in-depth examination of human-computer interaction scenarios in critical domains such as education, healthcare, and service delivery, offering prescient guidance for future research efforts in social robotics. Nonetheless, our findings underscore the nascent stage of development of social robotics, which requires a concerted focus on advancing interaction quality assessment, enhancing social cognitive capabilities, managing user information disclosure, and refining emotional intelligence. By prioritizing these avenues, we aim to improve the quality of human-robot interaction and provide users with enriched and personalized service experiences, thereby catalyzing the continued evolution and broader integration of social robotics technology.

This study explores users’ expectations of social robots by employing Q-methodology, a technique that identifies patterns of subjective opinions. By reviewing research papers and conducting interviews, we created 37 statements about social robot issues and users ranked these statements based on perceived importance. This made an individual’s subjectivity be measured and analyzed to distinguish between features deemed important by most users and those with high degrees of disagreement. Participants showed low interest in gaming, talking, and bonding with robots. Opinions on additional service charges were neutral while personalization was favored. We identified four types of consumers: those perceiving robots as a burdensome machine, a trusted friend, an emotionally intelligent device, and an energizing gadget. This research suggests a new academic framework to evaluate social robots consists of five dimensions: physical anthropomorphism, psychological anthropomorphism, cognitive intelligence, and user’s willingness to comply with robot’s suggestions and sacrifice. Our findings offer broad applications for companies developing social robots, providing a methodology that can serve as a basis for market surveys prior to product development and help tailor designs to meet the expectations of specific consumer niches.