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This research explored if a social robot would play a role in facilitating the development of friendship between young children while they engage in playful learning. Grounded in child–robot interaction and child development literature, we instantiated four sessions of triadic interaction activities among two children and a robot, where the robot mediated the children’s collaborative interactions. There were two types of robot-mediated activities (Conversational and Tablet assisted) with each type having two sessions and each session taking approx. 20 minutes, depending on the children. The activities were deployed with ten children (aged five to six) in an after-school program in a rural public school in the U.S. twice a week for two weeks. We video-recorded the sessions and later annotated these recordings for analysis. The friendship development between the children in a pair was observed in terms of five behavioral categories (liking, togetherness, parity, agreement, and co-construction). The results showed that both conversational and tablet-assisted robot mediation contributed complementarily to friendship development among the children. [ABSTRACT FROM AUTHOR]

Cultural influences are pervasive throughout human behaviour, and as human–robot interactions become more common, roboticists are increasingly focusing attention on how to build robots that are culturally competent and culturally sustainable. The current treatment of culture in robotics, however, is largely limited to the definition of culture as national culture. This is problematic for three reasons: it ignores subcultures, it loses specificity and hides the nuances in cultures, and it excludes refugees and stateless persons. We propose to shift the focus of cultural robotics to redefine culture as an emergent phenomenon. We make use of three research programmes in the social and cognitive sciences to justify this definition. Consequently, cultural behaviour cannot be explicitly programmed into a robot, rather, a robot must be designed with the capability to participate in the interactions that lead to the arising of cultural behaviour. In the final part of the paper, we explore which capacities and abilities are the most salient for a robot to do this. [ABSTRACT FROM AUTHOR]

Robots are increasingly entering the social sphere and taking on more sophisticated roles. One application for which robots are already being deployed is in civilian security tasks, in which robots augment security and police forces. In this domain, robots will encounter individuals in crisis who may pose a threat to themselves, others, or personal property. In such interactions with human police and security officers, a key goal is to de-escalate the situation to resolve the interaction. This paper considers the task of utilizing mobile robots in de-escalation tasks, using the mechanisms developed for de-escalation in human–human interactions. What strategies should a robot follow in order to leverage existing de-escalation approaches? Given these strategies, what sensing and interaction capabilities should a robot be capable of in order to engage in de-escalation tasks with humans? First, we discuss the current understanding of de-escalation with individuals in crisis and present a working model of the de-escalation process and strategies. Next, we review the capabilities that an autonomous agent should demonstrate to be able to apply such strategies in robot-mediated crisis de-escalation. Finally, we explore data-driven approaches to training robots in de-escalation and the next steps in moving the field forward. [ABSTRACT FROM AUTHOR]

Various technological devices, especially information communications technologies (ICTs), have become increasingly remarkable in higher education to help develop students' skills and qualifications. Considering this trend, supported by several academic theories, this paper proposes a breakthrough guidebook for universities and other scholastic environments based on reasoningfor-complexity using mainly artificial intelligence (AI) and social robotics (SR). The current research provides the instructions to follow in a real class supported by AI and SR with a precise compendium of steps. On the one hand, this is done by reviewing previous studies on educational processes with AI and SR and synthesizing their findings to draw out common themes from the literature. These topics are categorized into clusters in the form of guiding questions that professors can use to prepare their classes. On the other hand, it describes the students' steps for completing the activities. These were developed with previous forms to establish different profiles and apply custom-made assessment activities. The final part of this paper involves a set of reflections regarding these two technological resources (AI and SR) to demonstrate their utility in education. [ABSTRACT FROM AUTHOR]

Featured Application: A health monitoring system that combines commercially available products, such as sensing devices and apps, with a social robot to conduct health assessments and collect subjective and objective health data. The study conducted in this work suggests that the system has a high rating for usability and user experience and that it has the potential to be used for self-managing users' health. As the population ages, the demand for care for older adults is increasing. To maintain their independence and autonomy, even with declining health, assistive technologies such as connected medical devices or social robots can be useful. In previous work, we introduced a novel health monitoring system that combines commercially available products with apps designed specifically for older adults. The system is intended for the long-term collection of subjective and objective health data. In this work, we present an exploratory user experience (UX) and usability study we conducted with older adults as the target group of the system and with younger expert users who tested our system. All participants interacted with a social robot conducting a health assessment and tested sensing devices and an app for data visualization. The UX and usability of the individual components of the system were rated highly in questionnaires in all sessions. All participants also said they would use such a system in their everyday lives, demonstrating the potential of these systems for self-managing users' health. Finally, we found factors such as previous experience with social robots and technological expertise to have an influence on the reported UX of the users. [ABSTRACT FROM AUTHOR]

In a world with increasing numbers of robots operating in everyday human spaces, the employees at this robotics company are pioneers, with intelligent point-to-point path planning and autonomous transport operations in 150+ factory and warehouse locations in North America. At the time of research, this robotics company consisted of 250 employees. Unlike other industry models, their robots are designed to operate with people in mixed human-machine spaces, yet no HRI style evaluations had previously been run with their robots. As early observers of how factory workers and transport robot interact, across varied job roles ranging from technology design to customer relations, this work sought to leverage employee knowledge and experiences to identify opportunities for improving the communication capabilities of the robots, resulting in the addition of several robot state communications to their initial software set leveraging both employee- and social robotics literature- sourced ideas for communicating with lights. To achieve this a social robotics researcher spent a summer onsite at the robotics company, getting to know their software stack and culture. Her research activities included: (1) a company-wide survey relative to the robot's light, sound, and motion communications was sent out and analyzed, (2) the development of three new light sets (car-like, sweeping, heartbeat) and five overall states (blocked, at goal, turning, idle), and (3) a user study evaluating the developed light sets relative to the current robot default light patterns, all significantly improving the overall legibility of the targeted robot state communications: at goal, blocked, turning, and idle. Our initial findings advance knowledge in which style of light patterns is best for different communication states, showing that eye-catching lights are best for high urgency states, such as blocked, and subtle lights are best for low urgency states, such as idle. Finally, the latest software release for this robot has deployed a subset of these light patterns to all of their currently operating client sites, i.e., anyone who updates their robots to the latest release will benefit from these research results. This deployment sets the ground for future researchers exploring how end-users at different sites have responded to the new, more communicative light patterns. [ABSTRACT FROM AUTHOR]

To enable humanoid robots to share the social space,development in technology is required for natural interaction with the robots using multiple modes of communication such as speech, gestures, and share emotions with them. This research is targeted towards addressing the core issue of emotion recognition problem, which would require fewer computation resources and a much lesser number of network parameters, which will be more adaptive to compute on social robots for real-time communication. Any robots will have limited computation capability for run time actions and decisions. In the present investigation, Inception based Convolution Neural Network(CNN) Architecture is proposed to improve the emotion prediction. The proposed model has achieved improved accuracy of up to 6% improvement over the existing network architecture for emotion classification. The model was tested over seven different datasets to verify its robustness. In addition, real-time implementation capability is verified on humanoid robot NAO, which depicts its social behavior in real-time. The proposed model is reducing the trainable parameters to the extent of 94% as compared to vanilla CNN model, which indicates that its implementation ability in a real-time based application such as human-robot interaction. Rigorous experiments have been performed to validate the methodology, which is sufficiently robust and could achieve a high level of accuracy. Seven datasets are used to build a robust model. Finally, the model is integrated in a humanoid robot, NAO, in real-time. When averaged over all the emotions, the reduction in response time by 60% and 61% and improvement in prediction rate by 42% and 21% when compared in real-time environment with Vanilla CNN and state of the art model respectively. [ABSTRACT FROM AUTHOR]

Over the last years, social robots have been conceived to be used in various domains and for different purposes. This paper presents a survey on different applications and aspects of social robots in education. Different contexts are seen, ranging from children education, until higher education. A variety of applications and studies are reported, in areas like second language tutoring, teaching assistance, and storytelling. Robotic platforms used in these applications are observed, and means of evaluation of users' perception of robots are presented. [ABSTRACT FROM AUTHOR]

For older adults, regular exercises can provide both physical and mental benefits, increase their independence, and reduce the risks of diseases associated with aging. However, only a small portion of older adults regularly engage in physical activity. Therefore, it is important to promote exercise among older adults to help maintain overall health. In this paper, we present the first exploratory long-term human–robot interaction (HRI) study conducted at a local long-term care facility to investigate the benefits of one-on-one and group exercise interactions with an autonomous socially assistive robot and older adults. To provide targeted facilitation, our robot utilizes a unique emotion model that can adapt its assistive behaviors to users' affect and track their progress towards exercise goals through repeated sessions using the Goal Attainment Scale (GAS), while also monitoring heart rate to prevent overexertion. Results of the study show that users had positive valence and high engagement towards the robot and were able to maintain their exercise performance throughout the study. Questionnaire results showed high robot acceptance for both types of interactions. However, users in the one-on-one sessions perceived the robot as more sociable and intelligent, and had more positive perception of the robot's appearance and movements. [ABSTRACT FROM AUTHOR]

IntroductionThe aging of the population and the high incidence of those over 80 lead to an inevitable increase in chronic degenerative diseases, such as dementia, resulting in increased morbidity and disability. Treatment of people with dementia involves both pharmacological and non-pharmacological interventions. In particular, robot-assisted therapy is a potentially useful treatment for dementia as it has the advantage of improving mood, encouraging social interaction and communication. The overall objective of the study is to evaluate the improvement in patient-perceived quality of life following the use of the Paro robot integrated with usual care in the older people with dementia.Methods and analysisFor this study, 20 patients with dementia are recruited and divided into Experimental Group (EG) and Control Group (CG). Twenty-four session of intervention are conducted, divided into 2 sessions per week, for 12 weeks. The therapy sessions last 20 min. The Experimental Group will receive a social robotic intervention with Paro combined with usual care; the Control Group will receive only the traditional therapy, consisting of cognitive stimulation (reality orientation therapy, cognitive training) and occupational activities (painting workshops, cooking workshops, garden therapy, music therapy, etc.). Paro is a seal-shaped robot designed to have a calming effect and elicit emotional responses in patients in hospitals, nursing homes, and retirement homes. Assessment will be performed at the baseline, at the end of intervention and 3 months after the end of intervention. During these phases, several scales will be administered to the patients, such as Quality of Life—Alzheimer’s Disease, Addenbrooke’s Cognitive Examination, the Rating Anxiety In Dementia scale and the Cornell Scale for Depression in Dementia, Quebec User Evaluation of Satisfaction, Neuropsychiatric Inventory, the Technology Acceptance Model.DiscussionsThe final goals of the present study are to evaluate the improvement in patient-perceived quality of life following the use of the Paro robot integrated with usual care in the older people with dementia.Ethics and disseminationThe study was approved by the Ethic Committee of the Istituto Nazionale Ricovero e Cura Anziani, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS INRCA) during the session of 12 April 2022. It was recorded in ClinicalTrials.gov on 23 November 2022 on the number NCT05626205. The study findings will be used for publication in peer-reviewed scientific journals and presentations in scientific meetings.

IntroductionSocial robotics applied to the educational context deals with proposals that start from the present to shape the future of what training in a specific subject can be. On this occasion, the aim is to investigate the connection between the utilization of social robots and the teaching–learning processes that take place within a formal music education environment at any stage of education.MethodsTo carry out this research, the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) model served as a reference to perform a systematic review of articles published in two of the most important scientific databases, Web of Science (WoS) and Scopus, since 2015.ResultsA total of four articles fulfilled the inclusion criteria.DiscussionOur findings attest that social robotics still remains a practically unknown topic. Hardly any experiences have been developed in classrooms. In general, it is considered necessary for education, and more specifically for music, not to be left out of the developments in social robotics. This technology, which is increasingly present in various areas of our society, responds to the objective of defining the 21st century, and musical education is part of it.

During the past two decades, robots have been increasingly deployed in games. Researchers use games to better understand human-robot interaction and, in turn, the inclusion of social robots during gameplay creates new opportunities for novel game experiences. The contributions from social robotics and games communities cover a large spectrum of research questions using a wide variety of scenarios. In this article, we present the first comprehensive survey of the deployment of robots in games. We organise our findings according to four dimensions: (1) the societal impact of robots in games, (2) games as a research platform, (3) social interactions in games, and (4) game scenarios and materials. We discuss some significant research achievements and potential research avenues for the gaming and social robotics communities. This article describes the state of the art of the research on robots in games in the hope that it will assist researchers to contextualise their work in the field, to adhere to best practices and to identify future areas of research and multidisciplinary collaboration. [ABSTRACT FROM AUTHOR]

It is a well-known challenge for robot designers, developers and manufacturers that certain personalities and characteristics of people can result in resistance towards or outright refusal of human-robot interactions. With this in mind, we conducted qualitative research to collect information about what people think about some typical robots (Spot, Atlas, Lovot). Fifty-eight participants were interviewed during focus group sessions. Based on responses, we could conclude that the Spot robot was considered ugly and primarily useless, the Atlas was regarded neutrally and in Lovot's case participants blamed developers of such robots and societies that encouraged this type of robots. Our main contribution is qualitative data about opinions of these robots, which can be crucial feedback information for developers. [ABSTRACT FROM AUTHOR]

As the elderly population grows, there is a need for caregivers, which may become unsustainable for society. In this situation, the demand for automated help increases. One of the solutions is service robotics, in which robots have automation and show significant promise in working with people. In particular, household settings and aged people's homes will need these robots to perform daily activities. Clothing manipulation is a daily activity and represents a challenging area for a robot. The detection and classification are key points for the manipulation of clothes. For this reason, in this paper, we proposed to study fashion image classification with four different neural network models to improve apparel image classification accuracy on the Fashion-MNIST dataset. The network models are tested with the highest accuracy with a Fashion-Product dataset and a customized dataset. The results show that one of our models, the Multiple Convolutional Neural Network including 15 convolutional layers (MCNN15), boosted the state of art accuracy, and it obtained a classification accuracy of 94.04% on the Fashion-MNIST dataset with respect to the literature. Moreover, MCNN15, with the Fashion-Product dataset and the household dataset, obtained 60% and 40% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Facing long-term autonomy with a cognitive architecture raises several difficulties for processing symbolic and sub-symbolic information under different levels of uncertainty, and deals with complex decision-making scenarios. For reducing environment uncertainty and simplify the decision-making process, this paper establishes a method for translating robot knowledge to a conceptual graph to later extract probabilistic context information that allows to bound of the actions present at the deliberative layer. This research develops two ROS components, one for translating robot knowledge to the conceptual graphs and one for extracting context knowledge from this graph using Bayesian networks. We evaluate these components in a real-world scenario, performing a task where a robot notifies to a user a message of an event at home. Our results show an improvement in task completion when using our approach, decreasing the planning requests by 65% and doing the task in a third of the time. [ABSTRACT FROM AUTHOR]

Both designers and users of social robots tend to anthropomorphize robots. Focusing on the question how to conceptualize the relation between robots and humans, this paper first outlines two opposite philosophical views regarding this relation, which are connected to various normative responses to anthropomorphism and anthropomorphization. Then it argues for a third view: navigating between what it calls "naïve instrumentalism" and "uncritical posthumanism", it develops a hermeneutic, relational, and critical approach. Paradoxically, by unpacking the human dimension of robotics in its use and development, this view enables a critical discussion of anthropomorphizing robots. At the same time, and again somewhat paradoxically, it avoids a naïve instrumentalist position by taking robots' role as an instrument in a larger con-technology seriously. As such, the third view questions the dualism assumed in the debate. The paper then explores what this means for the field of social robotics and the education of computer scientists and engineers. It proposes a reform based on a relational understanding of the field itself and offers suggestions for the role of users-citizens. [ABSTRACT FROM AUTHOR]

Hybrid military teams, formed by human warfighters and autonomous artificial agents, represent the technological future of Defence operations. Both the potential and the inherent limitations of current technology are well-known, but the cognitive–behavioral and motivational aspects of human–robot interaction on the battlefield have yet to be systematically investigated. To lay the theoretical and methodological foundation of this scientific investigation, our position paper critically examines how the military personnel's spontaneous tendency to anthropomorphize artificial autonomous agents can affect operations of hybrid military teams in multiple ways. We will argue that the psychological impact of anthropomorphism on military personnel is neither easily avoidable nor necessarily detrimental. Correctly identifying the multi-level cognitive mechanisms that underpin implicit and explicit forms of anthropomorphism allows us to increase the efficacy of human–agent interaction. We will argue that, within hybrid teams, the capability to communicate with teammates, allies, civilians, and adversaries relies on embodied social cognition processes that are inherently geared toward anthropomorphism and leverage its effects. By updating both the design of autonomous artificial agents and the training of human troops to account for these processes, their reciprocal coordination can be augmented. [ABSTRACT FROM AUTHOR]

This paper examines science centre displays incorporating robotic technologies to provoke reflection on social robotics. Drawing on research in post-phenomenology and science and technology studies, it considers how exhibits in Born or Built? about robotic emotion undermine its framing narrative of human-robot convergence. Comparing these science centre exhibits to social robotics demonstrations draws attention to how the exhibits move away from convincing audiences about the truth or merits of these technologies to instead convey their difference and open-endedness. While there are limited popular narratives and images to invoke in exploring these ideas, relationships can be drawn between the displays and historical automata and contemporary robotic artwork, practices that frequently seek to promote reflection on robotic otherness, appearance and relationality. Representing robots with robotics points towards the relevance of style to conceptualising robotic diversity and materiality. Style in this sense is not simply an embellishment or surface addition to content, but a material expression inseparable from what is communicated and relative to technological, cultural and social milieus. [ABSTRACT FROM AUTHOR]

The incorporation of the students with autism spectrum disorder in the ordinary schools has meant an advance in the educational inclusion. We intend to analyze the possibilities that social robotics may provide in educational interventions with these students. The method used is the case study in which we try to give a qualitative description of a very particular situation in a very diverse and heterogeneous reality. The incorporation of the social robot in the educational process of an ASD student, has developed the interaction in a gaming context just as the acquisition of contents. [ABSTRACT FROM AUTHOR]

Multi-robot systems are moving into human spaces, such as working with people in factories (Bacula et al., in: Companion of the 2020 ACM/IEEE international conference on human–robot interaction, pp 119–121, 2020) or in emergency support (Wagner in Front Robot AI 8, 2021; Baxter et al., in: Autonomous robots and agents, Springer, pp 9–16, 2007) and it is crucial to consider how robots can communicate with the humans in the space. Our work evaluates a parameter framework to allow multi-robot groups of x, y, θ robots to effectively communicate using expressive motion. While expressive motion has been extensively studied in single robots (Knight et al., in: 2016 IEEE international conference on intelligent robots and systems (IROS), IEEE, 2016; Bacula and LaViers in Int J Soc Robot, 1–16, 2020; Dragan et al., in: 2013 8th ACM/IEEE international conference on human–robot interaction (HRI), IEEE, pp 301–308, 2013; Kirby et al., in: The 18th IEEE international symposium on robot and human interactive communication, 2009, RO-MAN 2009, IEEE, pp 607–612, 2009), moving to multi-robots creates new challenges as the state space expands and becomes more complex. We evaluate a hierarchical framework of six parameters to generate multi-robot expressive motion consisting of: (1) relative direction, (2) coherence, (3) relative speed, (4) relative start time, (5) proximity, and (6) geometry. We conducted six independent online studies to explore each parameter, finding that four out of six of the parameters had significant impact on people's perception of the multi-robot group. Additional takeaways of our studies clarify what humans interpret as a robot group, when the group is perceived positively versus negatively, and the critical role of architectural floor plan in interpreting robot intent. [ABSTRACT FROM AUTHOR]

In response to theorists of the social-relational turn as a framework for making sense of how humans relate with social robots, I offer an in-depth critique into underlying arguments related to differences in origin as a foundational reason for dismissing posthumanist perspectives in social robotics. In particular, I argue that critical posthumanist sensibilities for social robotics can offer generative accounts for making sense of the relation among, between, and with humans and social robots. Unpacking how arguments separating humans and robots related to origin—genesis—collapse when considering critical posthumanist insights on anthropogenesis, biogenesis, and technogenesis, I explore the value of a critical posthumanist approach in social robotics. A critical posthumanism for social robotics displaces dichotomous nature-culture or natural-artificial binaries and instead embraces an entangled view of relationality. This essay starts to work toward realizing posthumanism's critical sensibilities that take into account the scalar impact of the planetary meta-condition of the Anthropocene on humans, robots, and their relations. [ABSTRACT FROM AUTHOR]

With the development of the economy and the improvement of people's living standard, social robotics gradually enter into daily lives of individuals. Human–robot interaction is the basic function of social robotics, and how to achieve better experience of human–robot interaction is an important issue in the field of social robotics. Single-person pose estimation is the core technology for human–robot interaction in social robots. Benefiting from the development of deep learning, single-person pose estimation has made great progress. This paper reviews the development of single-person pose estimation from four aspects: data augmentation, the evolution of SPPE model, learning target and post-processing. Besides, we give the commonly used datasets and evaluation metrics. Finally, the problems of SPPE are discussed and the future research trends are given. [ABSTRACT FROM AUTHOR]

Customer service is an important and expensive aspect of business, often being the largest department in most companies. With growing societal acceptance and increasing cost efficiency due to mass production, service robots are beginning to cross from the industrial domain to the social domain. Currently, customer service robots tend to be digital and emulate social interactions through on-screen text, but state-of-the-art research points towards physical robots soon providing customer service in person. This article explores the feasibility of Transfer Learning different customer service domains to improve chatbot models. In our proposed approach, transfer learning-based chatbot models are initially assigned to learn one domain from an initial random weight distribution. Each model is then tasked with learning another domain by transferring knowledge from the previous domains. To evaluate our approach, a range of 19 companies from domains such as e-Commerce, telecommunications, and technology are selected through social interaction with X (formerly Twitter) customer support accounts. The results show that the majority of models are improved when transferring knowledge from at least one other domain, particularly those more data-scarce than others. General language transfer learning is observed, as well as higher-level transfer of similar domain knowledge. For each of the 19 domains, the Wilcoxon signed-rank test suggests that 16 have statistically significant distributions between transfer and non-transfer learning. Finally, feasibility is explored for the deployment of chatbot models to physical robot platforms including "Pepper", a semi-humanoid robot manufactured by SoftBank Robotics, and "Temi", a personal assistant robot. [ABSTRACT FROM AUTHOR]

Social robots represent a valid opportunity to manage the diagnosis, treatment, care, and support of older people with dementia. The aim of this study is to validate the Mini-Mental State Examination (MMSE) test administered by the Pepper robot equipped with systems to detect psychophysical and emotional states in older patients. Our main result is that the Pepper robot is capable of administering the MMSE and that cognitive status is not a determinant in the effective use of a social robot. People with mild cognitive impairment appreciate the robot, as it interacts with them. Acceptability does not relate strictly to the user experience, but the willingness to interact with the robot is an important variable for engagement. We demonstrate the feasibility of a novel approach that, in the future, could lead to more natural human–machine interaction when delivering cognitive tests with the aid of a social robot and a Computational Psychophysiology Module (CPM).

Biological rhythms are periodic internal variations of living organisms that act as adaptive responses to environmental changes. The human pacemaker is the suprachiasmatic nucleus, a brain region involved in biological functions like homeostasis or emotion. Biological rhythms are ultradian (24 h) depending on their period. Circadian rhythms are the most studied since they regulate daily sleep, emotion, and activity. Ambient and internal stimuli, such as light or activity, influence the timing and the period of biological rhythms, making our bodies adapt to dynamic situations. Nowadays, robots experience unceasing development, assisting us in many tasks. Due to the dynamic conditions of social environments and human-robot interaction, robots exhibiting adaptive behavior have more possibilities to engage users by emulating human social skills. This paper presents a biologically inspired model based on circadian biorhythms for autonomous and adaptive robot behavior. The model uses the Dynamic Circadian Integrated Response Characteristic method to mimic human biology and control artificial biologically inspired functions influencing the robot’s decision-making. The robot’s clock adapts to light, ambient noise, and user activity, synchronizing the robot’s behavior to the ambient conditions. The results show the adaptive response of the model to time shifts and seasonal changes of different ambient stimuli while regulating simulated hormones that are key in sleep/activity timing, stress, and autonomic basal heartbeat control during the day.

Children commonly experience pain and distress in healthcare settings related to medical procedures such as blood tests and intravenous insertions (IVIs). Inadequately addressed pain and distress can result in both short- and long-term negative consequences. The use of socially assistive robotics (SARs) to reduce procedure-related distress and pain in children’s healthcare settings has shown promise; however, the current options lack autonomous adaptability. This study presents a descriptive qualitative needs assessment of healthcare providers (HCPs) in two Canadian pediatric emergency departments (ED) to inform the design an artificial intelligence (AI)-enhanced social robot to be used as a distraction tool in the ED to facilitate IVIs. Semi-structured virtual individual and focus group interviews were conducted with eleven HCPs. Four main themes were identified: (1) common challenges during IVIs (i.e., child distress and resource limitations), (2) available tools for pain and distress management during IVIs (i.e., pharmacological and non-pharmacological), (3) response to SAR appearance and functionality (i.e., personalized emotional support, adaptive distraction based on child’s preferences, and positive reinforcement), and (4) anticipated benefits and challenges of SAR in the ED (i.e., ensuring developmentally appropriate interactions and space limitations). HCPs perceive AI-enhanced social robots as a promising tool for distraction during IVIs in the ED.