Social robots, particularly in assisting children with autism, have exhibited positive impacts on mental health. While prior studies concentrated on social robots in the Global North, there’s limited exploration in the Global South. It’s essential to comprehend special educators’ perspectives for effective integration in resource-constrained settings. Our mixed-methods approach, involving interviews, workshops, and a panel discussion with 25 educators in India, uncovers challenges and opportunities in integrating social robots into autism interventions. The findings highlight the urgent need to democratise the benefits of social robotics. Special educators express concerns about their functional capacity and fear potential redundancy due to the replacement of human efforts by social robots. Despite initial scepticism, professionals suggest various ways to incorporate social robots, emphasising the importance of technological innovation in reshaping and enhancing their roles in autism therapy. We discuss the implications of these findings for developing context-aware solutions and policy-level initiatives necessary in resource-constrained settings.
1 Introduction
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterised by challenges in social communication and interaction and the presence of restricted and repetitive patterns of behaviour and interests [9]. According to the latest estimates from the Center for Disease Control and Prevention, approximately 2.8% (1 in 36) children have been diagnosed with ASD [32], while in India, the prevalence is found to be 1.12% (1 in 89) [10, 107]. The increased prevalence of ASD has increased the burden on the global healthcare system to a great extent [36].
Some of the standard therapeutic resources in ASD include technological interventions such as computers and tablets that help children learn various everyday activities to ameliorate their difficulty with social skills. Within the myriad of interventions available, social robots have emerged as a promising tool for technological interventions for CwA in enhancing the acquisition of everyday skills and as a means to enhance the overall quality of life [39, 45, 83, 106, 121, 131, 140]. Roboticists define social robots as autonomous robots to which people apply a social model to interact with and understand them [28, 97]. Researchers have explored the efficacy of Robot-assisted Therapy (RAT) to attain particular therapeutic goals for CwA. RAT, with social robots as interactive agents, are specifically designed to interact in a socially and emotionally intelligent manner, thereby enhancing the overall therapeutic experience [124]. Robots endowed with verbal capabilities have resulted in better responses from children, where facial features and limb movements for such robots have been categorised as crucial elements for better developing a child’s gestures and expressions [94]. Recent studies have also suggested that economically viable and easy-to-use toy-like robots have shown better therapy outcomes [61] in clinical settings. However, the continuous development of such devices has been suggested for refining the various nuances of RAT after thoroughly examining the strengths and weaknesses of the device [68].
Social robots in autism therapy often require collaboration with experts like special educators. While some educators have reservations concerning the privacy, roles and responsibilities of the robot, accountability of consequences of interventions and cultural bias about using them [134, 142], many see benefits in terms of workload reduction and personalised care [7, 154]. However, most research on social robots in autism intervention focuses on resource-rich communities in the Global North [6, 42, 51, 110], which have better technological infrastructure and digital literacy [89]. While the global utilisation of electronic devices in ASD therapy is well-documented [23, 30], scholars like Diep et al. [51] have argued that social robots are often limited to mechanical tasks, neglecting emotional and communicative dimensions. Along similar lines, researchers such as Rudovic et al. [134] and Van Straten et al. [167] contend that autistic learners require "predictability" and "consistency" in robots for optimal support.
In India, special educators echo global sentiments, endorsing technological aid in therapy [148, 169]. However, disparities in technology adoption exist between the professional and private spheres, driven by cultural and accessibility factors [35]. Alongside, there remains the rampant problem of financial shortcomings across almost all special schools and educational institutes, causing their professional fraternity to be divided into formal and informal categories [76]. To address such issues scholars have highlighted the necessity of user-centered design approaches in technology building, coupled with sustainable business models and capacity-building initiatives that can support marginalised communities [25]. Additionally, as many scholars argue that underrepresented populations in the Global South often face exclusion in technology development and analysis, it becomes necessary to acknowledge such a situation, as otherwise, one may limit the generalisability of findings and overlook the unique challenges and opportunities present [114] in resource-constrained, digitally less-aware, or technologically under-equipped regions.
Addressing this concern, our study aims to understand the perspectives of special educators in India regarding social robots and their potential applicability within their professional practice. At the moment, figures provided by the Rehabilitation Council of India1 show that there are 75686 special educators registered with the government body [3]. They are known to provide caregiving services to various children on the autism spectrum. While some educators are affiliated with the government and private facilities, many provide door-to-door services. Since in India, despite the growing nature of technological advancements, robotic interventions in autism are still in their very early stages, it becomes crucial to understand if RAT would be pertinent and meaningful in the country’s socio-economic landscape. To achieve this aim, our study was structured around the following research questions:
•
RQ1: What is the nature of the present ecosystem of technology-enabled interventions for autism in India?
•
RQ2: What, according to the Indian special educators, are the perceived benefits and challenges of using social robots as therapeutic interventions for ASD?
•
RQ3: What initiatives can be taken to integrate social robots seamlessly into the Indian special education landscape?
To achieve this objective, we conducted preliminary surveys and field visits to gain insights into the current state of autism intervention services in India. Following this, we conducted interviews with special educators to explore further the context and specific needs within their daily practice. This was followed by hands-on workshops that allowed the special educators to engage with a social robot and a panel discussion with the educators and technology experts to collaboratively formulate preliminary and actionable guidelines for designing and developing social robots tailored to meet the specific requirements of such professionals in resource-constrained environments. Our findings highlight the paradoxical attitude of Indian special educators towards social robots for autism intervention. While enthusiastic about technological innovation, they face challenges like high costs, fears of job displacement, and concerns related to personalisation, linguistic limitations, and over-commercialisation.
Drawing upon these findings, we assemble crucial insights for designers and developers interested in introducing social robots for autism intervention in the resource-constraint settings of India. Additionally, we shed light on the intricate identity challenges experienced by special educators in this region due to the incorporation of social robots into their practice. Lastly, we emphasise the need to alleviate educators’ scepticism through enhanced dialogue and public awareness, a role that government agencies should actively support.
2 Related Works
The effectiveness of robot-assisted interventions for autism has been extensively studied in resource-rich settings, primarily in the Global North. However, there is a significant gap in understanding the use of such interventions in the Global South, especially in resource-constrained and culturally diverse contexts like India. Historically, technology-based interventions in India have been limited, focusing on utilising assistive technologies. In this section, we first explore the literature on social robots in autism and mental health globally, followed by technology-based interventions employed in the Global South, specifically focusing on CwA. Finally, we delve into the perceptions of technology for autism intervention, examining past research that provides insights into the integration of technology in autism interventions in India.
2.1 Social Robots in Mental Health
In recent years, there has been a global surge in exploring social robots as innovative therapeutic tools to address the increasing demand for alternative intervention modalities in mental health care [141]. Various studies have employed PARO seal robots [151], humanoid robots like NAO [146], and Pepper [108], along with the AIBO dog robot [59], although less frequently. These interventions target participants with a spectrum of conditions, such as dementia [77, 104], cognitive impairment [171], schizophrenia [41], depression [113], ASD [147], attention-deficit hyperactivity disorder [119], and intellectual disability [15]. The outcomes studied encompass a wide array, including engagement, social interaction, emotional state, agitation, behaviour, and overall quality of life. As outlined in a comprehensive review by Guemghar et al., the outcomes of these interventions have ranged from generally positive to mixed [64].
In the realm of autism, social robots play a vital role in both diagnostic decision-making and therapeutic interventions, significantly contributing to the enhanced quality of life for individuals with autism (CwA) [49, 130, 139]. Research indicates that CwA often find interactions with social robots more comfortable than with humans [50, 129, 138]. This comfort facilitates the elicitation of target behaviours for diagnosis. Notable initiatives include the development of a parrot-like robot, RoboParrot, designed for screening autistic children [46]. The European Commission-funded DREAM project is another noteworthy initiative. It created a Robot Enhanced Therapy (RET) system that integrates behavioural assessment and inference mechanisms for diagnostic and intervention purposes [128]. Social robots and reinforcement learning methods were employed to ensure reliable ASD assessments [115].
Additionally, studies by Ramírez-Duque et al. [120] explored the application of computer vision techniques to capture child behaviour, conducting automated face analysis to code nonverbal cues relevant to the robot-assisted diagnostic process. Beyond diagnostic applications, social robots are utilised in interventions to support individuals with autism. They provide therapeutic interactions and teach social and communication skills [62, 88, 123, 132], enhance engagement [44, 118], and assist with behavioural interventions [70, 92, 168].
In mental health interventions, technologies like social robots are mostly used by professionals like therapists and special educators. Across the world, these experts hold diverse views on integrating social robots into their practice while highlighting challenges such as technical issues and even concerns about replacing the essential human touch in working with Children with Autism (CwA) [126]. Additionally, the fear of robots being potential bullies [30] and worries about social isolation in children [51, 79] have been expressed by them. Despite these concerns, in general, social robots in interventions demonstrate positive effects on mental health, offering individual and group-level support [2, 64]. Some studies even indicate support for the idea of robots as collaborative partners [58, 170]. However, the limited exploration of robot-assisted interventions in resource-constrained contexts of the Global South necessitates further investigation due to the unique socio-cultural experiences of the region[114].
2.2 Technology-Based Interventions and Care for Autism in Global South
Research into mental health in resource-constrained settings remains limited despite growing global interest in leveraging technology for mental health care. Technology-based interventions, spanning diverse tools from mobile phones to virtual reality and social robots, have proven effective in treating various mental health concerns. These include serious conditions like schizophrenia and bipolar disorder, as well as common challenges such as depression, autism, anxiety disorders, and post-traumatic stress disorder [103]. Telepsychiatry, employing videoconferencing, has shown feasibility and acceptance in countries like Somaliland [4], South Africa [38], and India [102], proving effective for diagnosing mental disorders and providing follow-up care, though gradual integration with conventional care is required.
Studies on autism have further explored the effectiveness of digital tools for diagnosing mental disorders. In Bangladesh, machine-learning approaches have successfully contributed to the early and remote detection of autism in children [163]. Mobile-based systems have facilitated the monitoring and treatment of ASD by enabling interaction between patients and caregivers [5]. In India, non-medical health workers have effectively utilised mobile-based screening tools to diagnose depression and psychiatric disorders in clinical settings. Despite limited technology tools in India, which include tablets and mobile applications, their applications vary—from assisting parents and caregivers in delivering skill training to CwA to providing diagnostic and screening tools [12, 52]. Noteworthy examples include the "PedNeuroAiims Diagnostics" application developed by doctors at the All India Institute of Medical Sciences (AIIMS) [65], Screening Tools for Autism Risk using Technology (START) [12], and Chandigarh Autism Screening Instrument (CASI) [52], which aid in diagnosing neurodevelopmental disorders in children. Technology-assisted interventions such as Avaz2 and Jellow3, tablet-based applications, support communication skill development, especially in autism, by serving as augmentative and alternative communication (AAC) tools tailored to India’s cultural and linguistic diversity.
In the domain of autism interventions, the innovative approaches extend to cutting-edge technologies, such as virtual reality (VR) tools, showcasing their effectiveness in enhancing the performance of individuals with autism (CwA) in social tasks [75, 86, 90, 91]. Complementary to these tools, computer vision applications assess skills and emotions during interactive sessions [116]. Additionally, AI-based methodologies, particularly deep learning [85], are under exploration for early ASD detection through video analysis. Despite promising research outcomes, it’s essential to acknowledge that these interventions are still in their early stages, with limited practical applicability.
Despite the limited exploration of social robots in the Global South, especially concerning ASD, recent studies have started delving into this area, mainly focusing on the role of social robots in fostering skills and positive behaviours in CwA [53]. In countries like Sri Lanka, social robots primarily contribute to sensory integration in therapeutic interventions [161]. Furthermore, research has identified the capabilities of commercially available robots in developing robot-assisted interventions for prerequisite skills in CwA [154]. Robotic training kits have proven effective in teaching psychomotor skills to these children. Caregivers of CwA have also shown a positive reception toward using robots in interventions aimed at developing learning and social interaction skills [24]. Similar initiatives were identified in the low-resource settings of Kazakhstan, exploring the robot activities for robot-assisted ASD therapy [136, 172].
Regardless of the advancements in technology-based interventions, significant untapped potential lies in harnessing technological solutions, especially social robots, to address the distinctive challenges of mental health and autism in resource-constrained environments like India. Like any AI system, deploying robots in marginalised communities, without understanding the knowledge, needs, and perceptions of the stakeholders who operate them may risk introducing technology that causes extra workload, inefficiencies, or harm to the communities they aim to assist [18].
2.3 Embracing Innovation: Examining the Perception of Technology for Autism Intervention in India
The use and significance of technology-driven solutions, particularly in the Global South, remains a subject of ongoing debate, influenced by complex socio-economic factors [101]. Especially with regard to the deployment of various technologies, in countries like India, infrastructure has become a rising concern [143]. For instance, the majority of special education institutions in the country have been reported to be heavily dependent on external financial donations, facing a notable absence of government support [133, 157]. The meagre private contributions are often irregular and poorly timed. Given this situation, independent practitioners of autism therapy providing door-to-door services rely solely on their remuneration or seek additional income through other means. Moreover, many special educators in India face issues of being underpaid [164], and some even treat special education as a secondary job [76].
Scholars emphasise that, given the prevalence of poverty, techno-logy-oriented services must prioritise cost-effectiveness [33] and inclusive design [84] to ensure widespread adoption and equitable access of technologies in marginalised communities. This is because high maintenance costs related to the infrastructure of technology-based solutions [66] have led to situations where promising devices and tools have offered limited value in resource-constrained settings [22]. This diminished value has been further attributed to inadequate familiarity with the devices [95] and a lack of efforts to provide essential information for optimal utilisation [109], resulting in suboptimal technology representation and deployment. This scenario underscores the numerous challenges documented in the Indian special education landscape, in addition to the shortage of well-qualified and experienced practitioners [96]. In the evolving landscape of healthcare technology in the Global South, the role of special educators emerges as a critical link to ensure the accessibility of healthcare services in low-resource environments.
Different cultures have different exposure to robots through media or personal experience and hence different attitudes [17]. Towards this, Chauhan et al. examine parents’ perspectives, exploring the challenges and opportunities of technology-based instruction for their CwA in training and therapy [37]. Despite supporting technological interventions, the study exposed participating parents’ apprehensions regarding their children’s heightened reliance on technology tools, resulting in diminished social interaction, prolonged screen time, and a shortage of personalised intervention support. The qualitative analysis of a mobile-based application developed for assessing autism demonstrated feasibility and acceptability, particularly among children actively engaging in the app’s games [52]. While the parents also expressed acceptability, they raised concerns about the reliability of using an app for assessing their child’s developmental progress. An exploratory study involving a collaborative gesture-based application to enhance joint attention skills showed improved social interaction among CwA when interacting with typically developed peers.
Further, within the scope of intervention, special educators also encounter diverse challenges, from difficulties related to curriculum implementation [82] to the strain of managing each autistic child, often leading to rapid burnout [29]. Beyond these challenges, previous research has shed light on emotional struggles faced by workers in the Global South due to the widespread adoption of AI [78, 98] and concurrently highlighted their concerns about potential job displacement by robots [43, 72]. The perceived implications of AI have also been reported to evoke feelings of insecurity and speculative anxiety among professionals in diverse fields, including accounting [135] and artistic creativity [74].
Qualitative analysis also indicated a positive shift in the urban Indian attitude towards technology-oriented interventions [13, 149]. However, the study also uncovered challenges associated with introducing computer-supported social interventions in developing countries. These challenges included meeting high technology expectations through on-the-fly customisation, accommodating a spectrum of children with one application, and addressing issues related to low technology acceptance.
In creating technology-supported applications to meet the requirements of autistic children, researchers noted the active engagement and participation of stakeholders, including educators, therapists, and parents [150]. This active involvement served as evidence of technology acceptance in autism care. However, researchers also identified challenges stemming from resource limitations, economic barriers, and the presence of a digital divide, limiting the use of this robotic intervention. At the same time, there is also a lack of research on understanding the perceptions and experiences of using social robots among special educators of CwA. Accordingly, our study employs qualitative methods to explore the current landscape of autism intervention practices in India, aiming to uncover the challenges, opportunities and perceptions associated with using social robots in autism intervention, with the hopes of contributing to developing robot-assisted therapy with a human-centred and responsible approach.
3 Research Methods
3.1 Positionality and Reflexivity
All the authors are of Indian origin. The authors are well aware of and exposed to the constraints surrounding different kinds of resources in the Indian socio-economic landscape. One of the authors is a special educator with 15+ years of experience working with CwA. Three authors have previously conducted studies in human-computer interaction (HCI) in the context of socially assistive robots for autism. Two authors have experience working with community-based approaches towards computing and design. To mitigate potential bias stemming from authors’ familiarity with work contexts, we pre-registered our methodology at the study’s commencement, promoting transparency and reducing bias risks in analysis. Furthermore, we emphasise the diversity in our authorship, which spans various backgrounds, to counteract individual biases. However, we recognise the inherent challenge of our strong desire to enhance the state of special education, which may influence our data analysis. We ask that our paper be read as such.
3.2 Ethical Considerations
Ethical approval for this study was obtained from the Institutional Review Board (IRB) of Indraprastha Institute of Information Technology (IIITD) (IIITD-IRB/FR/01/2022/04). Before the interviews, the participants were provided with an overview of the study and verbal and written consent was obtained. Participants were allowed to converse in Hindi, English, or Bengali based on their preference during the interviews.
3.3 Participants and Recruitment
The study was conducted in urban settings within Delhi and Kolkata, two metropolitan cities in India, from February 2022 to June 2023. The study comprised 25 participants who were licensed special education practitioners (F=22, M =3, Age range (years): 24-58) in these cities, possessing at least five years of experience working with CwA registered with the Rehabilitation Council of India (RCI). RCI is a statutory body that regulates and monitors services given to persons with disability and maintains the centralised register of qualified professionals and personnel in the field of rehabilitation and special education. Informants were contacted and recruited through professional organisations, special schools, and rehabilitation centres using a combination of email solicitation, snowballing methods, and the authors’ contacts. The recruitment did not consider the participants’ prior experience with or knowledge of technology or robotics-assisted interventions to mitigate potential bias in the study. However, many participants mentioned routinely incorporating some form of technology into their work. Each of our informants had extensive experience (Mean experience (years): 13.4) working with CwA. Appendix A presents the demographic information of our participants.
3.4 Methodology
3.4.1 Field Trips and Groundwork.
Prior to data collection, the authors conducted extensive field visits to 4 rehabilitation centres in Delhi. At each centre, three authors conducted several field observations to understand the various activities and strategies that special educators working with CwA regularly use. Extensive notes and schematic sketches were developed to understand and document special educators’ activities. Special attention was also paid to understanding how different tools and technologies were used in these activities. Care was taken to ensure our field visits were not disruptive to the children’s daily routine. No photographs or videos were taken to ensure the privacy of the children. As elaborated in subsequent portions of the methodology section, these field trips were invaluable for us to familiarise ourselves with the educational activities, rehabilitation strategies, work routines, and the challenges they face in their day-to-day professional lives. The data collected during these field visits was extremely useful for programming our NAO robot deployed to carry out the demo RAT activities, developing our semi-structured interview guide, and setting the workshop agenda.
3.4.2 Study Materials.
Figure 1:
Figure 1: Snapshots from the video probe depicting different intervention activities
Video for Elicitation Activity. Video provocation has been previously employed in literature [99, 105] to educate participants about the technology. Building on previous research, we employed video provocation to educate special educators about the capabilities and potential use cases of robots in interventions.
Based on our field observations, interactions with the special educators, and an extensive review of literature, we identified the most common set of activities performed with CwA across the globe and in India. The final set of exercises selected was as follows: response to name, emotion identification using card matching, imitation, vocabulary building, and making eye contact using a peek-a-boo game. This set of exercises was widely used by special educators in India. These exercises were derived from a therapeutic approach based on applied behaviour analysis (ABA), commonly used to improve social, communication, and learning skills in individuals with autism [22]. This approach utilises reinforcement techniques to encourage the development of desired behaviours and skills. ABA is an evidence-based and scientifically validated practice that has been demonstrated to be effective in enhancing the abilities of individuals with autism and other developmental disabilities. Taking these activities as a basis, we programmed an interactive social robot agent - the NAO robot, which is a widely used robot in autism research [11, 154, 162] to deliver intervention activities to CwA under the guidance of a special education expert. The robot was programmed to speak in Hindi (a native language widely spoken in North India, where the study took place).
Centred on the study’s goal, the primary aim of the video was to emphasise the application of robots in intervention activities rather than highlighting the interactions between robots and the child. Consequently, we trained a seven-year-old non-ASD child to engage with the NAO robot and carry out the specified activities. Subsequently, a video with English subtitles was created of six RAT activities (response to name, joint attention, imitation, vocabulary, emotions, anticipation play) outlined above to inform special educators about the potential use of robots in therapeutic practices. The video showcased both positive and negative use cases of using robots in such settings. One example of a positive use case demonstrated in the video was when the robot successfully completed a task without any errors. On the other hand, a negative use case was showcased where the robot failed to complete an activity due to technical glitches or hardware limitations. The exploratory video was of ∼ 10 minutes in duration.
3.5 Procedure
The study protocol incorporated a qualitative research approach, combining video provocation/elicitation with semi-structured interviews, workshops and panel discussions. The study unfolded in three phases.
Phase I: Video Presentation. Participants were contacted at their preferred time, either in person or through Zoom. In the beginning, one researcher briefly explained the scope of the study, shared the study information sheet and obtained the participants’ consent. Then, the author presented an overview of NAO robot-based intervention activities and presented the exploratory video to them. Exploratory videos were used to elicit participants’ responses and gain insights into their experiences and perspectives. Conti et al. utilised both video and oral presentations in their study to assess the readiness of psychologists to incorporate robots into their professional practice [42]. Previously, to examine the perceptions of Community Health Workers (CHWs) about integrating AI into their workflow and identifying the anticipated benefits and challenges, video provocation was utilised as an exploration artefact by Chinasa et al. [54]. Based on this previous work, in this study, we use video elicitation to inform special educators about the possibilities and challenges of incorporating social robots into their educational interventions for CwA. Snapshots of the video are presented in Figure 1.
Phase II: Semi-structured interviews. Once the video presentation had concluded, the informants participated in a semi-structured interview. The interview protocol was structured around four thematic categories: a) background of the educators, b) challenges and concerns faced by educators in facilitating routine autism interventions, c) exploring their perceptions regarding the usability of robots in interventions, and d) understanding perceived trust and meaningfulness of social robots in the Indian context. As recommended by Jacob and Furgerson [73], after each interview, we systematically reviewed and revised the questionnaire, incorporating additional prompts and follow-up questions. This iterative process continued until we reached saturation, ensuring the gathered information was comprehensive. The interview protocol is given in Appendix B. The interviews were conducted in Hindi, English, or Bengali according to each of the participant’s language preferences and lasted 1 to 1.5 hours.
Phase III: Expanding on the insights acquired during the interviews, the study’s third phase included three one-day workshops for educators, providing them with hands-on experience with the robot. This was followed by a single panel discussion, which brought together special educators (participants in our previous interviews) and technologists, including developers and researchers specialising in social robotics and artificial intelligence.
Workshop. In each of these workshops, we facilitated the special educators to interact with the robot. The participants were afforded the opportunity to actively engage with the robot and experiment with the activities demonstrated in the video probe. Participants had the freedom to choose activities based on their preferences, and each participant performed at least four out of the six activities. Participants who did not complete all the activities mentioned reasons such as being technically unfamiliar, while a few found certain activities to be less challenging. During this time, we also answered any of the questions that they had in their mind. Each session had 8-9 participants and lasted between 1 to 1.5 hours. The screenshot from one of our workshop sessions is given in Figure 2.
Figure 2:
Figure 2: Snapshots from workshop sessions
Panel Discussion. Following the workshop, we extended invitations to these participants for a panel discussion to explore the future possibilities of integrating robots into autism intervention. Upon receiving confirmation of participation, the panel discussion was conducted one month after the workshops. It involved 14 special educators from the previous participant pool and four technology experts. The inclusion of panel discussions in the study protocol aimed to develop guidelines that are grounded in the specific needs of special educators and the technological feasibility of implementation. Throughout each session, participants were actively encouraged to share their ideas on how robots could be better built and designed to enhance integration, particularly in resource-constrained settings like India. Furthermore, they were prompted to articulate their perspectives on the role of robots in interventions and were encouraged to present and justify their views in an explainable manner. The sessions also saw many of our special educators provide us with pictorial depictions of social robots as they imagined themselves, as shown in Figure 3. It’s crucial to acknowledge that design considerations were outside the scope of this study; nonetheless, their visualisations were taken into account to gain insights into their perceived role of robots in interventions. The technologists who formed a part of our panel discussion belonged mostly to research institutions across India and had an average experience of nine years in ideating, building, and maintaining assistive technologies.
Figure 3:
Figure 3: Participants’ drawings of the social robot with their comments
As we took field notes from these conversations, every workshop session and panel discussion was audio-visually recorded with prior permission from all the respondents. The recordings from these sessions were transcribed for the purpose of further analysis. For taking part in these three sessions, participants were compensated with a 700 INR (∼ USD 8.4) Amazon gift card.
3.6 Data Collection and Analysis
The data included 25 semi-structured interviews (∼ 50 hours of audio) and ∼ 5 hours of video recordings of the three workshops and panels. Data also included detailed field notes made during the field visits, interviews, workshops, and a panel discussion. The audio recordings of interviews conducted in Hindi, English, and Bengali were translated and transcribed into English for analysis. For coding, the transcripts from interviews, workshops, and panel discussions were initially treated separately and first-level codes such as "sharing of resources", "robots are more animate than tablet", etc. were extracted based on the emerging patterns in data. Each author independently analysed and open-coded the data using thematic analysis employing an inductive, constant comparison method [27]. Following this, we proceeded with the second iteration of open coding, identifying commonalities within the initial sets of the code list from different phases. To ensure accuracy and consistency, the authors periodically met to compare the codes being generated, resolve any discrepancies, refine them and conceptualise themes to a higher level, such as "Special educators talk about financial difficulties", "Social robots can be partners in different activities", etc. We repeated this process until all the interviews were coded, we reached data saturation, and all authors reached a consensus on the identified themes. The final codebook with derived themes and the frequency of each code are presented in Appendix D. To protect the privacy of our participants, we used pseudonyms and anonymised the quotes in the manuscript.
4 Findings
This section is organised as follows. We begin by answering the first research question by highlighting the current socio-technical contexts of special education schools (Section 4.1) and the challenges that therapists face when using various technologies in their daily practice (Section 4.2). Next, we document Indian special educators’ perspectives on the perceived benefits and challenges of using social robots as therapeutic interventions for CwA (Section 4.3) and the perceived transformation of their roles in therapy (Section 4.4) to answer the second research question. Lastly, to answer the third research question, we explore the initiatives that can be taken to integrate social robots into the Indian special education landscape (Section 4.5).
4.1 Current Socio-Technical Landscape of Special Education Schools
To understand and situate our work in such realities, we requested our interviewees to articulate the intricacies of their work environments. Our interviews revealed that nearly all the schools we investigated possessed fundamental infrastructural facilities, including essential tools for therapy. While some institutions managed to distribute these devices adequately, many centres had inadequate technological resources and a large number of faculty members or students who used them, thus creating a scarcity. For instance, respondents P12 and P13 highlighted that their school lacked devices such as laptops and tablets due to limited donor funds, which only covered locally manufactured desktops with outdated software, which limited their use.
“ Our donors have made it explicitly clear that they do not want to invest all their money in hardware and software. Over time, we requested them for another PC, but our request was denied. ” (P12)
Distribution Hierarchies and Educator’s Sentiments. Special education centres often employed creative strategies for addressing such deficits when resources were inadequate. For instance, respondents P1 and P2 highlighted that a rotating system was implemented every week in their school that ensured that nearly all teachers, regardless of their positions, could use technological equipment at least once every two weeks. These workarounds involving the sharing of devices have emerged as a common practice to overcome the lack of adequate resources in the Global South [122]. In addition, some participants also pointed out that factors like individual therapists’ proximity to the centre’s head and the attrition rate of children in their groups often influenced their access to and allocation of technological resources.
“Despite the policy for rotational equipment use, it often appears effective only on paper, influenced by proximity to the head and children’s attrition affecting favour. High attrition is seen as the educator’s inability, with no explanations permitted.” (P1)
Educators, Conflicts and Emotional Impact. The challenges stemming from insufficient technological resources seemed to result in various conflicts among educators, resulting in a personal sense of dissatisfaction. For instance, respondents like P23, P24, and P25 conveyed their frustration around the scarcity of resources, noting that it occasionally led them to question their professional purpose and triggered feelings of being "unwanted" and "undesired." In many well-funded schools, we also observed that the special education classrooms were segregated into distinct areas within the regular institutions, amplifying these feelings. As highlighted by several respondents, this sense of othering was especially pronounced when they were physically segregated and denied access to basic amenities like internet connectivity, thus necessitating them to look for alternative arrangements.
"The setup appears odd to me. Our centre is part of the school, which caters to both neurotypical and neurodivergent children. I don’t understand the need for a separate building outside the main premises. Teachers from there don’t engage with us or share resources. We even have our own internet connection." (P23)
4.2 Understanding Educator’s Needs and Challenges with Technology-Assisted Interventions for Autism
The therapists we interviewed employed a range of technological interventions for therapeutic purposes. These included Android applications such as Avaz and Talk With Me4 to enhance speech, specifically curated YouTube videos to bolster social skills and vocabulary acquisition, and the integration of electronic toys and hand puppets to help with conversational interactions. Additionally, confident special educators utilised computer games installed on their centre’s PCs to facilitate the learning of everyday activities. On the one hand, several therapists appreciated the value these technologies bring to their therapeutic practice in terms of enhanced learning outcomes. At the same time, they also expressed several apprehensions, as outlined below.
Expensive Machines and Educators’ Frustrations. Several interviewees (n=13) revealed that using expensive devices like tablets and PCs was always risky and made them anxious. One special educator, P23, informed us that often, some centres and schools were “ruthless” and asked the educators to compensate for any damage done to the devices. P13, P18, and P23 acknowledged that such a policy fostered a perception that the institution lacked complete trust in their educators and prioritised the economic value of the devices over their expertise in delivering services using such devices.
“When I have to constantly worry about the chances of a portion of my earnings being deducted whenever I use a device, my frustration is directed more towards the device itself than the centre’s trust in my responsibility. After all, it’s all because the device is expensive, right? ” (P13)
Challenges in Therapy Applications. Our interviewees had mixed opinions about using different therapy software. Some were concerned about the language options in popular apps like Avaz, which only offered seven Indian languages. Some interviewees felt this to be a lack of inclusiveness that showed a disregard for cultural and linguistic diversity across India. Some participants disagreed when discussing the feasibility of incorporating a large number of languages to be slightly difficult, suggesting that such apps were constructed without considering inclusiveness. At least three participants complained that such applications needed very powerful mobile devices with high storage capacities, and their inability to own such devices prevented them from using these Apps. Two other participants who worked independently providing door-to-door services found that subscribing to these applications was too expensive, and owning a mobile phone that would support such apps would add more cost to their practice.
“I have used the app called Avaz. I hear a lot of people using it these days. I have used other software as well. The problem with these technologies is that they lack in multilingual setups.” (P7)
Technologies for Alleviating Fatigue in Autism Therapy. Reflecting on their professional challenges, our participants echoed narratives underscoring the pervasive issue of fatigue and emotional drain among educators. The repetitive nature of autism therapy tasks, coupled with extended periods of managing children with special needs, is noted as particularly demanding and emotionally taxing [29, 82]. Given these factors, our respondents expressed that they would find it helpful if technological interventions could be developed to share their work burden and alleviate their stress and fatigue.
“We do the same tasks every day, and it makes us exhausted. It would be good to have technologies that can share this exhaustion.” (P4)
Needs, Requirements, and a Call for Collaboration. A common consensus that emerged among the experts we interviewed indicated that existing technology-mediated methods were falling short of meeting educators’ needs across different contexts and circumstances. Particularly concerning software applications, they noted that these tools appeared to prioritise "the needs of the affected children" without adequately considering the operational capabilities and needs of the experts themselves. Nevertheless, despite facing several challenges, none of our participants were reluctant to use technological tools and applications for therapeutic interventions. Many special educators stressed the importance of collaborative tool development rather than merely critiquing technology for its inherent limitations. This sentiment was shared by experts across the spectrum, including both independent therapy service providers and those who work at institutions.
4.3 Educator’s Perspectives on Benefits and Challenge of Robot-Assisted Therapy
In this section, we elaborate on the sentiments expressed by our participants about integrating social robots in their therapeutic practices and establish whether the latter would be a meaningful addition to the existing technological interventions.
Following the video demonstration, our respondents were prompted to share their opinions about the robot’s engagement in diverse therapeutic activities. Many of our participants appreciated that social robots could replicate many therapeutic exercises they conducted regularly. Sixteen of the twenty-five therapists we interviewed confirmed that such technologies “defined the future of autism therapy.” A few others remained “optimistic” about seeing if future robotic technological developments will address the specific needs of special educators, as this would increase the uptake and use of social robots in therapeutic practice.
“I am quite fascinated with how it works and understand everything. I think it is going to be really useful for the children as well as for myself. Unlike a tablet or a computer, this seems quite animate and should do well with the kids.” (P21)
Anthropomorphism and Perceived Benefits in Therapeutic Practice.
The anthropomorphic characteristics of the social robot captured the attention of almost all the participants. Some of them even projected their ideas using their drawings during our workshop sessions, as depicted in Figure 2. A few even pointed out that using social robots in therapy would be similar to their existing use of sock puppets that mimicked human-like features. Mirroring the participants’ views in Duffy’s study [54], several of our participants also agreed that incorporating human-like attributes would make working with robots easier than with other software tools or digital devices. Without exception, all the participants highlighted that the presence of humanoid traits made them perceive the robot as a "partner" capable of "walking and talking" and "interacting" like humans. Research also shows that the anthropomorphic attributes of social robots influence people to perceive them to be more sociable [81], leading to increased likability [31]. Some special educators, like P9 and P10, said that programming the robot to talk in Hindi and not English was a good step and an important one in countries like India, where many people don’t speak English. With the robots being able to speak Hindi, the educators felt that they would be “at ease” and “socially connected” while using the technology, which was crucial for the therapy process. Perceiving social robots to be similar to a helping hand, special educators also explained that participating in therapeutic activities with a robot by their side would make them more “confident” and feel more “empowered” when working with the children.
“Working with a Hindi-speaking robot would make me feel very proud and comfortable. I would know that I at least have an assistant with me who can perform some of my tasks if I am too fatigued. I would know I am not alone in this. Plus, I would always prefer a robot which looks more like a human.” (P9)
Robots as the Non-Judgmental Aids. After learning that special educators contemplated utilising social robots as therapy partners and assistants, we proceeded to delve deeper into their rationale for envisioning social robots as work partners. We were especially interested in understanding if only their humanoid characteristics bolstered educators’ confidence or if other factors influenced their decision. In response to this question, our participants pointed out that it did not matter if the robots were capable of actual human emotions or, for that matter, whether they had any sense of camaraderie towards their users. Instead, what mattered to them was a human-like entity that acted as a support system and could help in some of the exercises and activities consistently without fatigue and without exhibiting any prejudice or discriminatory behaviour towards the patients or causing physical harm to them.
“When called for assistance, our aides would sometimes think of us as incapable of working independently. I think a robot like this serves my purpose and, at the same time, will not judge me for using it as a helping hand.” (P24)
Regulating Robot Engagement in Therapy. While acknowledging the importance of robot-assisted interventions for ASD patients, our interviewees also stressed the importance of finding the right balance between the level of human and robotic engagement in providing therapy. In this direction, several special educators pointed out that one way to achieve this was to deploy social robots customised to support specific repetitive tasks in their therapeutic routines under their supervision. Several participants saw this ability of social robots as potentially useful tools to reduce their workload and alleviate mental stress and fatigue, in concert with findings from previous studies [153].
“We get psychologically drained after our consultations. With this robot, I think I could escape from some of that. I could ask my assistants then to conduct some of the sessions for which the robot could be helpful.” (P6)
Robots Could Lead to "Over-Professionalisation." Senior experts in our study expressed the need for educators in India to have a strong moral sense while working with social robots. This is in concert with previous studies in human-robot interaction (HRI) exploring ethical and accountability dimensions where researchers have highlighted the need for educators to be responsible towards the appropriate use of social robots [158]. These experts speculated that introducing social robots, like tablets and smartphones, might lead special educators to prioritise enhancing their professional identity and marketability over using the technology to benefit their patients. Therapists in our study also voiced concerns about the potential impact of robotic interventions on their capacity for empathy toward the children they work with. They were worried that over-reliance on social robots could lead to disregarding social and cultural issues faced by CwA such as stigma [48, 112].
“Computers initially gained popularity more for trendiness than therapy. Although not all educators were swayed, parental demand sometimes fueled market-driven approaches rather than thoughtful planning. Introducing robots could likely elevate professionalism for those who can afford them, potentially skewing therapy towards economic interests.” (P9)
Concern for Damages and the Fear of Technical Complexity. In line with their anxiety about dealing with expensive digital devices, as elaborated in section 4.1, concerns among educators were also related to affordability. Explaining the rationale behind their apprehension towards social robots, participants like P1 and P2 articulated that, similar to other devices, their schools could ask them to pay for any damages inflicted on the robot. The educators were also concerned about the technical complexities of handling a social robot. They asked us if the therapy exercises could be controlled through an easy-to-use mobile application as per the needs of a child.
“I do not doubt the capabilities, just that using it would be risky if the kids throw it away or damage it. I might be asked to pay for the damages.” (P2)
4.4 AI, Robots and Role Transformation: Fear of Replacement and Strategies for Collaboration
In this section, we discuss some of the anxieties and fears expressed by our participants to highlight the necessity of user perception in human-AI collaboration [127], with the hopes of contributing to developing artificial intelligence-based technologies with a human-centred and responsible approach.
Fear of AI for Personal Reasons. On the same lines, concerns about potential job loss due to replacement by social robots emerged as a common theme in several of our interviews and panel discussions. In response, we explored the reasons behind these reservations to alleviate their fears and to draw insights and implications for the design community to address this issue from a practice perspective.
“As I am seeing technologies like ChatGPT become more and more a part of everyday life, I fear that, soon, robots like these could make us redundant. I was discussing this the earlier day with a colleague of mine, and even she felt the same way.” (P10)
Our interviews revealed that much of the fears and anxieties of our participants were rooted in their in-direct experiences of seeing family members being laid off due to the introduction of computers, being exposed to speculative technology news reports, hearing stories of friends and distant relatives struggling to adapt to technological systems in industrial settings and personal experiences of losing occupational status with the introduction of technology-enabled therapeutic routines. Several respondents perceived robotic technologies as a force that only "devalued their work" or rendered their roles unnecessary.
“We suffered a lot as a family when both my parents lost their jobs. Their job was mostly administrative pen and paperwork. The problem was with the introduction of computers. My parents did not know anything about them, and the company office thought they were not needed anymore. That left me completely traumatised.” (P11)
Taking a slightly different view, while admitting to her family member losing his job due to automation, participant P19 nevertheless advocated for using sophisticated technologies like robots in a controlled and collaborative manner.
“I like this robot; it’s not like I don’t like it. I am a bit sceptical. I have witnessed my father lose his job in the factory because of automation." (P16)
Collaboration for The Greater Good. Though ideas for collaborating with a social robot ranged across different tasks, almost all the therapists agreed that robots are highly beneficial in conducting therapeutic exercises that involve repetitive tasks designed to improve joint attention and imitation. They reasoned that it would make the process more enticing and appealing to CwA and reduce fatigue associated with repeated tasks for practitioners.
“I think for joint attention or imitation exercises, I can work out the robot to ensure better child engagement. Autistic children usually like these things and find them to be very attractive. I imagine in such cases, I and the robot can both do the exercise in unison, and then the child can follow. I could maybe do a couple of jumps with my hand, and the robot can do the rest.” (P7)
Further, many participants suggested that robots could perform a part of a particular task by themselves and let the educator complete the rest in a collaborative, iterative manner. These ideas we gathered from our participants were consistent with those documented in other related studies [7, 80]. Another critical area in which our participants found robots potentially useful is training special educators. Participants P9, P16, and P22, who owned their special education schools, proposed that social robots could be deployed to help young professionals practice therapy activities before they start working with CwA. They even enquired from us if the technology could be developed in such a way that it could assess an educator’s capabilities before the individual is assigned to a working group.
“I think apart from doing the tasks, I could even make this work for teaching the educators themselves and letting them practise with it before they work with the children. Some educators have difficulties in working with children immediately after training, so this might help them in acclimatising with the environment of therapy.” (P5)
Two highly experienced experts discussed the possibility of using social robots as "positive behaviour influences" for CwA. Their emphasis was on robots being pivotal in cultivating socially acceptable behaviours, particularly when children resisted guidance from special educators. More importantly, these experts firmly believed that robots could foster a constructive experience for children and educators during therapy sessions. Lastly, drawing on their experiences, these senior practitioners gently dismissed the fears of facing obsolescence. They asserted that the role of special educators might transform in the years to come, fueled by the advent of robots. Rather than harbouring fears about being replaced, they advocated a proactive approach to harnessing technology and exploring innovative collaboration avenues to co-opt social robots in their practice.
“I believe the special education profession would have a different purpose and meaning with the rise of technology these days. I do not think it is going to wither away. But at the same time, we need to adapt to technology instead of simply postponing its use. Personally, I would advocate the use of social robot systems, but at the same time, our professional community has to work together to make it a prudent addition to their work.” (P16)
4.5 On Trust, Belief and Reliability: Negotiating Meaningfulness of Social Robots in India
Previous research shows that understanding how users perceive technology in terms of trustworthiness and user-friendliness can be crucial to its successful implementation and use [40]. As social robots are relatively new in India compared to other assistive technologies, we agree with Kok and Soh’s submission that it is crucial to investigate perceptions about their safety and reliability directly from their users. The section below documents our participants’ conceptualisation of such crucial aspects to expand the scope of the broader HCI work revolving around user perception surrounding trustworthiness [20, 173] and making social robots meaningful [111] and appropriate for a professional setting [137].
Understanding Trust, Inequality, and Fragmentation. In our study, many special educators acknowledged the potential of robots as beneficial additions to their work. However, they also expressed concerns about their appropriateness, citing India’s economic conditions and social inequalities. Such ambivalence in opinions has been reported in other studies as well [159, 160]. Some educators, like P12 and P18, expressed that robots might worsen existing differences, contributing to their hesitation despite recognising the technology’s potential. Our findings align with previous work reporting similar attitudes that were due to factors such as anticipated issues of trust in robots [56], reluctance to embrace new technologies due to a negative bias [57] and a belief that technologies can be harmful to the society [21] emerging from personal first-hand experiences.
"There is already so much inequality in our country. I feel with this robot, those differences might get amplified." (P19)
Twenty out of the twenty-five interviewees highlighted two trust-related issues. First, they hesitated to accept social robots as trustworthy as they believed that it might cause them to be undervalued in a society where they were already marginalised despite their professional acumen. Secondly, recognising the differences within their fraternity, they believed that social robots could cause further divisions and may lead to financially well-off special educators looking down upon those who provided “door-to-door services” who were sometimes labelled as "not really professionals" or "pseudo-experts."
"The introduction of a robot could amplify existing inequalities in our profession. Those with access to robots could enhance efficiency, income, and prestige, while others struggle to earn a living. In India, our professional community is polarised, and I can’t fully endorse robots considering these dynamics." (P20)
Interestingly, some of the educators we spoke to linked the inequities ingrained in their profession to the broader social divisions in the country. They informed us that they preferred using "simple" tools without extravagant features since they believed it could potentially worsen societal and professional divides. One respondent, P11, particularly attuned to prevalent discrimination based on factors like caste and class, expressed reluctance to adopt technology that might accentuate these differences. Echoing these concerns, our participants suggested restrictions on the long-term use of robots in special education.
“I think, in a country like India where inequality is everywhere, I cannot knowingly let a machine disrupt the already fractured environment of my profession. If that happens, then the entire community will have to suffer, and the children will suffer more." (P22)
Support for Robots as The Future of Therapy. Educators supporting the assimilation of social robots into therapeutic interventions presented different reasons. In our study, participants P8, P11, and P13 underscored humanoid features and demonstrative abilities of social robots as crucial indicators of their positive outlook. They emphasised that the robot’s impact on the child was pivotal in the Indian context, assessing the demonstrated features’ appropriateness and utility. Participants like P10 and P16 highlighted that futuristic technologies like robotics were inherently trustworthy, seeing their success in other countries for RAT. They also embraced innovation despite poverty and inequality, foreseeing long-term benefits. They drew parallels to how computers and mobile phones, once expensive novelties, became everyday essentials, thus projecting a similar trajectory for robots.
“We are standing at such a time when India is growing as an economy, and of course, like every developing nation, we have our own problems. But that does not mean I will not support something for the children. As a professional, I am not afraid to say I fully support social robots.” (P8)
Training Special Educators for Using Social Robots. The two veteran special educators, P16 and P22, expressed that while robots might find utility and relevance in Indian settings, concerns about the use of such technologies arise with regard to their proper knowledge of handling and operation among professionals. Noting that technological interventions in autism therapy remain insufficiently accessible in rural and semi-urban areas due to a lack of proficient professionals, they emphasised the potential for mismanagement of these interventions in these contexts due to inadequate training and the risk of mishandling expensive devices. Hence, these experts called for adopting appropriate training programs advocating for government and civil society collaboration to ensure effective technological interventions among marginalised communities.
“I wholeheartedly support the use of social robots. From my experience, it would be my opinion that the government should work with NGOs and scientists to explore the options of making these cost-effective and widely available with sufficient training resources." (P16)
5 Discussion
Despite increased efforts to develop social robots in resource-constr-ained settings like India, limited progress has been made in understanding the needs and perceptions of end-users regarding robot-assisted interventions, with only a few studies addressing these aspects [13, 87]. Earlier implementations of technologies in low-resource clinical settings have demonstrated failures, resulting in added inefficiencies to clinical workflows and, at worst, the harm inflicted upon the communities they aim to benefit [19, 47, 67] necessitating proactive examination before deployment [105]. This is particularly crucial for addressing the vulnerability and marginalisation experienced by professionals in resource-constrained settings in the Global South, as observed in previous studies [142].
In our research, we aim to ensure social equity and justice in perception-oriented investigations of HRI, contributing to the glaring absence of scholarship in HRI literature. Organising our findings, the discussion section is structured into three segments: a) highlighting implications for designing social robotics in resource-constrained settings, b) providing guidelines for preparing educators in robot-assisted interventions, and c) suggesting institutional strategies for seamless integration of social robots in autism intervention.
5.1 Design and Development of Social Robotics for Resource Constrained Settings
In our endeavour to devise a robot-assisted therapeutic system tailored for resource-limited communities, the study underscores the need for a cautious approach in deploying robot-assisted therapeutic systems, emphasising educators’ pivotal role in decision-making during therapy. The suggested strategies involve creating culturally suitable and lightweight activity modules, ensuring multilingual capabilities, endorsing open-source moderation, prioritising the improvement of existing applications rather than introducing entirely new systems for smooth integration into educators’ routines and employing participatory design to ensure contextual appropriateness.
5.1.1 What should developers keep in mind while creating social robots?
Our findings underscore educators’ preference for a clearly defined strategic role in robot-assisted interventions, emphasising the enhancement of their efforts in the therapy process. To align with this preference, developers, as suggested by Elbeleidy et al. [55], can focus on creating robots with reduced autonomy, fostering improved collaboration between humans and robots. To further address educators’ insecurities, we propose involving them in the decision-making process during therapy, promoting confidence and mitigating potential complications in the event of technical failures. We also find it crucial to present the social robot as a peer or role model for children [87] as well as a non-judgemental aid, prioritising cost-effectiveness and robust build quality. To this end, we advocate for industry engagement in producing low-cost, durable devices that ensure longevity. Such an approach not only addresses concerns but also reduces fear, fostering the acceptance of advanced technologies, especially within marginalised communities, in a confident and approachable manner [152].
In light of our findings, prioritising cost reduction while preserving the social robot’s functionality emerges as crucial. Accessibility, particularly for educators managing RAT, becomes a focal point [145]. Prior studies in the Global South have explored initiatives like using mobile phones to enhance online accessibility [117], reduce poverty [155], and provide free basic Internet connectivity [34]. Given the prevalent ubiquity of smartphones in marginalised communities, the limited digital literacy among educators, and storage challenges reported by our participants, a lightweight mobile application featuring an intuitive interface [125] stands out as a potential solution. Such an application could facilitate the curation of therapy exercises without necessitating direct robot reprogramming. Furthermore, we underscore the significance of integrating robotic intervention seamlessly into existing practices. This strategy avoids the imposition of an entirely new intervention approach tailored exclusively for robots. By adopting this approach, the transition from current tools to the incorporation of social robots into daily practice becomes smooth and natural.
Drawing from existing literature [1] and the input of our educators, we recognise the diversity in the needs of children on the spectrum. We thus advocate for flexibility in designing activity modules [8, 26], accommodating unique requirements and allowing for future improvements. Additionally, these applications should enable educators to customise and personalise therapeutic experiences for each child, aligning with fundamental autism therapy guidelines. Inspired by Barba, we also recommend making activity modules open-source by the companies developing social robots in order to promote transparency and reproducibility and allow for its iteration by educational institutions and non-profit research labs [16].
Educators also stressed the importance of ensuring cultural appropriateness in the robot’s activity modules, e.g. such as performing positive reinforcement through clapping of hands and not by giving a flying kiss. Recognising the significance of aligning technology implementation with cultural understanding, as emphasised by Gross et al. [63], we follow Pal et al.’s approach to assistive technology in emerging regions and propose a collaborative design process involving professionals such as computer scientists, engineers, cultural theorists, interaction designers from various geographical contexts, and caregiving professionals in diverse regions [144].
Our educators also expressed the need for robots to communicate in vernacular languages beyond English. It’s therefore recommended that these robots possess multi-linguistic capabilities, considering the diverse linguistic landscape in countries like India and the Global South, with the aim to include non-prominent vernacular languages as well in the future. Additionally, based on the perspective of some of our participants, we suggest that new applications for robot-assisted interventions should enhance existing therapy practices to facilitate seamless integration into educators’ routines.
5.1.2 Participatory Design for Social Robotics: An Appropriate Answer for Contextual Appropriateness?
Based on the outlined recommendations, we advocate for a participatory design (PD) approach to responsibly develop and deploy social robots in resource-constrained environments. PD, as noted in prior research, offers a safe space for exploration and experimentation before technology deployment in marginalised communities, especially in the Global South [105]. Additionally, PD has demonstrated effectiveness in HRI, contributing to the creation of robots for depression management and mood stabilisation [60]. It also plays a significant role in ecosystem mapping for differently-abled populations, supporting democratic and respectful technology development [69, 84]. Considering the varied mechanisms of PD [14], our suggestion is anchored in fostering a dynamic power balance between users and designers, encouraging critical dialogue for user empowerment and collective decision-making [156]. In crafting social robots for environments like India, our proposed PD approach, with a focus on ethnography, must consider the socioeconomic backgrounds and everyday experiences of special educators. Emphasising the importance of acknowledging disparities in opportunities and resource awareness, inspired by Toyama [165], the design process should incorporate the voices of all educators, including those marginalised within the professional community. A more respectful and collaborative approach could nurture meaningful partnerships and contribute to deploying well-designed social robots for resource-constrained communities in the Global South.
5.2 Beyond Robotic Realities: Preparing the Educators for Robot-Assisted Interventions
All our participants stressed the need to prepare special educators for robot-assisted interventions. In light of the perceived technical complexity of social robots, we were given to understand that their widespread adoption in marginalised communities could be a tough affair without proper training. Researchers in HCI have consistently recognised the crucial influence of training and support initiatives on the effectiveness of technology interventions [47, 67]. Typically, these training programs concentrate on instructing users on utilising new technology [47]. In our case, we turn to Irani et al.’s inspirational work on postcolonial computing [71] to put forward our recommendation about first identifying the level of digital literacy and understanding of social robots among special educators and then leveraging their willingness to use technological tools for the overarching benefit of the children. Following this, educators would need training to develop technical competence and proficiency in operating social robot systems. Simultaneously, they would need to engage in critical thinking processes to foster a balanced understanding of social robot systems, recognising both their strengths and weaknesses. This would be crucial towards making educators understand their significance in the therapeutic process despite using AI-enabled agents like social robots. It would also help in making them realise how AI would not be able to replace them entirely or not make their roles redundant [100]. Additionally, educators should be prepared for the risks and potential errors of using social robots in special educational settings. This preparedness could encourage adoption and boost confidence among them.
When addressing the education and training of special educators on assistive robots, it’s crucial to recognise educators’ hesitancy towards adopting new technology, stemming from their comfort with existing tools. Therefore, training efforts should prioritise sensitising educators about the potential benefits of social robots, providing insights into their strengths and weaknesses, and recognising and leveraging their strong commitment to the overarching goal of benefiting children. Additionally, during the training process, educators should be informed about ethical and privacy-preserving practices related to technologies like social robots, requiring programs to navigate diverse cultural perspectives and values within the context of privacy [105].
Additionally, drawing from the works of Uzorka et al. on professional development [166], we suggest that by actively involving educators and care workers as participants in scientific projects encompassing assistive technology, the role of robots as tools for empowerment rather than a burden could be strongly established in their minds. For the effective execution of training and exposure programs, it is imperative for academic institutions, research laboratories, and well-resourced researchers to proactively participate in outreach activities and capacity-building initiatives. This involvement includes collaborative efforts with various organisations such as special education schools, non-governmental organisations dedicated to differently-abled children, and publicly funded schools catering to children with special needs. This collaboration can take the form of workshops and joint seminars. In particular, there should be a concerted effort to encourage young researchers under supervision to take the lead in community-driven endeavours, fostering the dissemination of scientific knowledge across all sections of society.
5.3 On the Future of RAT in India: Unpacking Strategies for the Adoption of Social Robots in Low-Resource Settings
Our final suggestion rests on the future of RAT in low-resource settings by envisioning a pathway for the adoption of SARs over a period of time. To that effect, we feel that there are three important institutions, namely government agencies, civil society organisations and special education schools themselves, that have to contribute and commit in different ways.
As discussed in existing literature, technology interventions from non-domestic sources for accessibility are often costly due to high procurement expenses, including taxes and import duties [143]. The United Nations Convention on the Rights of Persons with Disabilities 5 (UN-CRPD) forms a critical foundation for promoting low-cost Assistive Technology (AT) in developing nations by mandating signatory countries to ensure accessible conditions for their citizens. Based on this principle, we, therefore, agree with Pal et al. in suggesting the development of assistive technological interventions to materialise cost-effective solutions for resource-constrained communities [143, 144].
The Government of India has already implemented schemes such as Make-in-India6 and Digital India7, along with grants from the Science and Engineering Research Board8 and the Department of Science and Technology9, to promote domestically produced technology products. Under these projects, there have been initiatives that have materialised into the development of indigenous robots like Vyommitra, Daksha, and Manav. A significant development in the area of robotics in India has been the establishment of a National Robotics Mission10 and the Indian National Mission on Interdisciplinary Cyber-Physical Systems,11 specifically with centres for Robotics and Cobotics, emphasising indigenous technology creation and transfer. The proposal outlines a replicable model for other Global South countries, offering strategic support through institutional backing, tax exemptions, and facilitator grants to promote innovation and technology development.
Despite the program’s emphasis on innovation and technology development, there remains a gap in addressing public awareness about technological advances and educating individuals on technology building and innovations. To address these gaps, we propose that the National Education Policy (NEP)12 should prioritise Science, Technology, Engineering, and Mathematics (STEM) education, fostering a skilled workforce for technological advancements and creating an educational environment that encourages students to contribute to progress in diverse technological domains, especially in social robotics. Simultaneously, programs like India’s National Mental Health Programme13 that currently lacks avenues for technology inclusion require to be revamped to the integration of technology-enabled mental health interventions by incorporating technology education into their training and awareness programs. By providing comprehensive training programs, educators can overcome apprehensions and misconceptions, fostering a more informed and confident approach to incorporating these technologies into the learning environment.
Our findings further point towards the need to infuse accountability at the institutional level within special education schools and organisations. In this regard, we suggest special education institutions should take charge of the maintenance and preservation of social robots, assuming responsibility for their upkeep and, bearing the costs associated with accidental breakdowns, partnering with civil society organisations. Simultaneously, financially well-off schools can collaborate with civil society organisations to prioritise training and awareness for educators, fostering a collective understanding of social robot usage. We suggest that emphasising institutional accountability and responsible robotics development ensures a sustainable framework for adopting and utilising social robots, empowering special education professionals simultaneously.
We further recommend establishing appropriate policies and ethical guidelines to regulate robot-assisted interventions for diagnostic and therapeutic purposes. In contrast to the Global North, where guidelines exist for the safe and ethical use of such technologies [93], countries like India seem to lack such frameworks. While there have been initiatives in framing policies governing AI in healthcare 14, we strongly advocate developing similar frameworks to ensure the safe and responsible use of social robots in the Global South.
In summary, our multifaceted strategy envisions indigenous development, robust government support, targeted training, affordable pricing models, and collaborative efforts at societal and institutional levels. In materialising them, some of the challenges in the effective and responsible integration of social robots into the clinical settings of marginalised communities like India could be adequately addressed.
5.4 Limitations and Future Works
While our research has provided insights into technology adoption and HRI in the Global South, it is not without its limitations. Primarily influenced by perspectives from professionals in Indian metropolitan cities, our study lacks representation from care workers or special educators in rural and semi-urban regions. This limitation is particularly noteworthy given India’s significant rural population. In future endeavours, we commit to addressing this gap by actively involving participants from non-urban areas.
Furthermore, our study provides insights from 25 special education professionals only, who were initiated for recruitment immediately after the pandemic. Initial hesitancy arose due to perceived time constraints, with educators expressing reservations influenced by past mistreatment by researchers. We anticipate that our set of recommendations will guide researchers to be more considerate of special educators and other respondents, fostering greater participation in future studies.
In addition, our current study centres on the viewpoints of special education professionals, recognising the necessity of parental consent for interventions like social robots in India. While valuable, we acknowledge the need to broaden our scope in future studies to include parents and other stakeholders. We encourage researchers in the Global South to adopt a comprehensive approach, involving children, parents, and the larger community in their investigations. Such inclusivity is crucial, especially when the values of researchers, special educators, and communities may diverge at times, as seen in safeguarding personal health data privacy [105].
The integration of social robots in healthcare raises ethical concerns and is currently being discussed, particularly within the HRI community. In low-resource settings like India, there’s a notable absence of regulatory requirements for AI systems. As social robots address societal challenges, especially in healthcare in resource-constrained settings, there’s an urgent need for regulatory frameworks. These should prioritise ethics, safety, and privacy, emphasising diverse values within a particular context. Our upcoming research aims to explore the ethical dimensions and tensions in real-world deployment, validating findings in healthcare facilities. A long-term study in clinics will further assess the effectiveness of recommendations, focusing on patient care and ethical considerations.
6 Conclusion
In our qualitative exploration, we delve into the perceptions of Indian special educators regarding the integration of social robots in autism intervention. Using a mixed-methods approach encompassing interviews, workshops, and a panel discussion with 25 educators, our aim is to unravel the challenges and opportunities inherent in the adoption of social robots within autism intervention practices. The findings shed light on critical issues, notably the pressing necessity to democratise social robotics and AI more broadly. Special educators, particularly in resource-constrained environments like India, express reservations about the functional capacity of these technologies. A prevailing concern is the potential replacement of human efforts by social robots, rendering educators redundant. Despite initial scepticism, professionals propose diverse avenues for integrating social robots into their efforts. They stress the importance of technological innovation in autism therapy, envisioning it as a futuristic initiative that could reshape and enhance their roles. Drawing from our findings, we deliberate on the implications of designing social robotics in resource-constrained settings. We provide practical guidelines for preparing educators for robot-assisted interventions and propose institutional strategies for the seamless integration of social robots in autism intervention. While our insights may extend beyond India to similar low-resource settings grappling with autism, it’s crucial to acknowledge the need for future research to gauge the generalisability of our findings.
Acknowledgments
This research work is funded by the Start-up Research Grant (Ref. ID.: SRG/2020/002454) of the Science and Engineering Research Board, Government of India, and partially supported by the Centre for Design and New Media (a TCS Foundation Initiative supported by Tata Consultancy Services) and Infosys Centre for Artificial Intelligence at IIIT Delhi. We are thankful to our reviewers for their comments and suggestions. Further, we extend our gratitude to the team of educators at the NGO Deepalaya, as well as all other participants, for their invaluable support throughout the study.
A Participant Demographics
Table 1:
Serial No.
Respondent
Experience (in years)
Category
Educational Qualification
Exposure to Technological Resources
1
P1
12
Employed at Institute
Bachelor of Education
Computer, Tablet, Mobile Applications,
Electronic toys, feedback receiving pens
2
P2
15
Employed at institute
Bachelor of Education
Computer, Tablet, Mobile Applications,
3
P3
16
Door-to-door
Bachelor of Education
Computer, Tablet, Mobile Applications,
4
P4
8
Employed at Institute
Bachelor of Education
Computer, Tablet, Mobile Applications,
5
P5
9
Door-to-door
Bachelor of Education
Computer, Tablet, Mobile Applications,
6
P6
25
Door-to-door
Bachelor of Education
Computer, Tablet, Mobile Applications,
7
P7
11
Door-to-door
Master of Education with
Computer, Tablet, Mobile Applications
specialisation in Special Education,
8
P8
14
Employee at institute
Master of Arts in Psychology,
Computer, Tablet, Mobile Applications
Bachelor of Education,
9
P9
28
Own Institute
Bachelor of Arts in English,
Computer, Tablet, Mobile Applications,
Master of Education with
specialisation in Special Education,
10
P10
11
Door-to-door
Master of Arts in Psychology,
Computer, Tablet, Mobile Applications,
Bachelor of Education
11
P11
11
Door-to-door
Master of Arts in Psychology
Computer, Tablet, Mobile Applications,
12
P12
13
Employed at institute
Bachelor of Education
Computer, Tablet, Mobile Applications,
13
P13
12
Employed at Institute
Bachelor of Education
Computer, Tablet, Mobile Applications,
Social robots
14
P14
16
Door-to-door
Bachelor of Education
Computer, Tablet, Mobile Applications
15
P15
7
Door-to-door
Bachelor of Education
Computer, Tablet, Mobile Applications,
16
P16
18
Own institute
Bachelor of Education
Computer, Tablet, Mobile Applications
17
P17
15
door-to-door
Bachelor of Education
Computer, Tablet, Mobile Applications
18
P18
12
Employed at Institute
Bachelor of Education
Computer, Tablet, Mobile Applications
19
P19
9
Door-to-door
Bachelor of Education
Computer, Tablet, Mobile Applications
20
P20
8
Door-to-door
Master of Arts in Psychology,
Computer, Tablet, Mobile Applications
Bachelor of Education
21
P21
7
Door-to-door
MPhil in Psychology,
Computer, Tablet, Mobile Applications
Diploma in Special Education
22
P22
19
Own institute
Bachelor of Education
Computer, Tablet, Mobile Applications
23
P23
15
Employed at institute
Bachelor of Education
Computer, Tablet, Mobile Applications,
24
P24
13
Employed at institute
Master of Arts in Psychology,
Computer, Tablet, Mobile Applications
Bachelor of Education
25
P25
11
Employed at institute
Bachelor of Education
Computer, Tablet, Mobile Applications
Table 1: Participant Demographics
B Interview Protocol
•
BACKGROUND QUESTIONS
-
Could you please introduce yourself?
-
Could you please tell me about your daily routine at your centre?
-
Could you please tell me about the specific programmes for CwA at your school/centre?
-
What role do you play as a special educator in order to facilitate these activities?
-
What are the challenges that you face in your work?
-
What kind of strategies do you use as a special educator?
-
What kind of tools/props do you use in your activities as a special educator? Do you make use of technological equipment? If yes, then what are they and are you satisfied using them?
-
Do you find the technology that you use or any kind of technology to be helpful in your work? What are your needs and requirements in general from a social robot that you would like to point out?
-
The tools and technologies that you use when it comes to CwA, do they fulfil your needs and requirements?
-
What are these tools presently lacking?
-
Why do you think they are lacking in your needs and how do you think that can be rectified?
•
ANXIETY
-
Are you aware of the use of social robots as interventions by special educators in autism spectrum disorder?
-
Have you ever used one of them, or have seen it being used?
-
Do you think the kind of robot that we showed here would be able to help you in your work?
-
Given a situation in which we give you such a robot as it has been shown in this video, would you be inclined to use it? If yes, then why? If not, then why?
-
What would be confident in using if you were given to use such a robot, in case you have not used a robot before?
-
Would you be comfortable using such a robot in your work?
-
In case you find using such a robot to be an uncomfortable exercise, which part(s) exactly would you find to be uncomfortable?
-
What do you make of social robots, since you have seen in the videos that they can fail, too? Does that make you anxious?
•
PERCEIVED USABILITY
-
In what way do you think the robot will be helpful towards a) your work? and b) children?
-
Do you think the use of social robots would make your work a) easy b) efficient?
-
What are some of the activities that you think the robot can do all by itself, where it does not require your help or support? Give us some probable situations. What exactly would be the functionality of the robot in this case?
-
What are some of the activities that you can think of that you can do with the robot, that is you and the robot can work together? Give us some probable situations. What exactly would be the functionality of the robot in this case?
-
What kind of activities do you think you can let the robot do alone with the children, that is, activities where there would be minimum participation and supervision from your side?
-
What do you think could be any other use of social robots apart from therapy?
•
CONTEXTUAL MEANINGFULNESS
-
What would you think would be the biggest challenges in a country like India if social robots were to be used in autism?
-
Can you identify personal challenges on your part if you were to use social robots?
-
What do you think could be the various ways in which these challenges could be addressed?
-
Personally, would you encourage/not encourage the use of social robots in autism in a country like India? If yes, then why and if no, then why?
•
TRUST
-
Would you find a robot to be a trustworthy intervention in your work?
-
In case you find it to be trustworthy, then could you please indicate what aspect about it would make it seem untrustworthy to you?
-
How do you think a) parents, b) peers, and c) children perceive the use of social robots? Would you take into account their perception if/before you are to use it?
-
Let us say that you perceive the robot to be a trustworthy intervention, but others do not (parents/peers). Would you try to influence them to trust the use of social robots or do something that would make them trust it?
-
Do you find SARs to be safe as an intervention when it comes to CwA?
C Nudges for Panel Discussion
•
Having viewed in the videos as well as in your interactions that a social robot might fail and it is not foolproof, do you still think it will replace you?
•
When it comes to working with the robot, how do you view yourself as a specialist? Then, do you think your role changes, or does it have any new dimensions?
•
What are your suggestions about making the use of social robots more contextualised in your practice, based on your perception?
•
What would be your idea about making social robots more trustworthy as an intervention in your practice?
•
What would be your suggestions on balancing the role of social robots in your practice?
•
Did you feel that because this is a humanoid robot, it can replace you?
•
Can you draw a robot that you might be looking for in case of therapy?
•
Could you please explain why you drew such a robot?
•
Some of you have highlighted that social robots can help not only in giving therapy but also in performing other activities, such as helping in building an inclusionary environment. How do you think your role as a special educator would transform should you use the robot in a non-therapeutic context such as this?
•
How well do you find yourself prepared for handling a social robot in therapy?
•
If no, what kind of support/assistance do you expect in making yourself prepared?
•
Some of you have pointed out that CwAs can get attached to robots, which might turn out to be dangerous. Do you have some suggestions on how we can use social robots and, at the same time, not have children get attached to them? What do you think would be a healthy trade-off in that case?
•
Do you have any other suggestions when it comes to social robots and their use for Autism therapy?
D Codebook and Themes for Qualitative Analysis
Theme / Code
Count
Theme / Code
Count
Infrastructural Issues & Resource Constraints
214
Special Educators’ perception towards RAT
410
Special educators talk poor infrastructural facilities Special educators talk about financial difficulties Special educators provide instances of resource constraint Special educators outline her concerns about how such infrastructural issues affects them Special educators inform about institutional practices of ensuring responsibility by creating an atmosphere of fear Special educators describe such an atmosphere of fear by signifying instructions from schools to repay the cost of damages if expensive devices are damaged. Special educators point out fear of using expensive technologies in general
12 25 23 11 61 22 60
Special educators understand RAT Special educators are aware about the features of RAT Special educators feel RAT will help them have unbiased and non-judgmental aids Special educators feel RAT is futuristic Special educators feel RAT could help them battle psychological fatigue Robots could do repetitive tasks Robots could be good role models Robots need to speak in different languages Robots need to be less expensive Erosion of parental touch and affection Overprofessionalisation due to RAT Humanoid robots are better for therapy Children should not develop an attraction for robots Provision for customisation of activities Provision for robot control through mobile phone-based applications
33 28 50 41 60 21 11 7 37 9 3 66 19 11 14
Present technologies, needs and challenges for ASD
471
Fear of AI and strategies for collaboration
370
Special educators use different kinds of computer software/mobile applications/YouTube videos to perform activities. Special educators come across a wide variety of challenges in using technological interventions Special educators are supportive of technological interventions Special educators are not supportive of technological interventions Special educators do not use technological intervention unless absolutely necessary Special educators use technological intervention as and when required depending on their own experience and the requirement of children Applications do not allow linguistic diversity Some applications are not lightweight Applications need high speed internet Technological interventions are not culturally contextualised Expensive software with costly annual subscription becomes difficult to maintain Special educators feel their work is monotonous Special educators feel judged by their aids when they need help Special educators find it difficult to cope with the psychological burden of their work Senior special educators have concern for YouTube videos Special educators need to customise training as per needs and requirements of the child Some mobile applications have poor UI
54 63 27 3 6 28 33 19 11 29 15 51 38 60 2 17 15
Special educators have positive attitude toward AI Special educators have negative attitudes towards AI Special educators feel that technologies like AI and those machines using them will soon make them redundant AI based systems need through cultural auditing before being deployed Fear of AI on part of some educators is based on personal experiences Fear of AI on part of some educators is based on professional experiences Educators welcome the use AI in social robots as new age technology Human-AI collaboration at all levels is necessary for betterment of society and humankind Social robots can be partners in different activities Social robots can serve as useful training agents for educators who are beginning their careers Social robots can keep children engaged Social robots would transform the role of the special educators Social robots would augment the efforts of special educators and help in ensuring better therapy practices AI use in countries like India should be regulated in all forms AI could replace teachers and educators in schools for atypical children first
25 5 45 21 11 3 54 6 33 29 30 51 41 2 14
Trust, Reliability & Appropriateness of Social Robots in India
336
India has pressing issues of social inequality Special educators are fragmented as a single professional class Not all special educators command the same manner of societal reverence Not all special educators are financially well-off Social robots could fragment the professional fraternity of special educator Social robots could widen the margin of difference between special educators As therapeutic agent social robots are trustworthy Social robots could not be trusted unless implemented on large scale and effects are seen RAT has been successful in other nations and hence reliable inherently Social robots could follow the trajectories of mobile phones in India and become inexpensive in the long run Government programs should be implemented for building of low -cost robots Government programs should be planned to make educators more aware about social robots Government and NGOs should partner with each other for the training of special educators with social robots Social robots could cause digital divide Rural India should be focused upon while developing social robots
Yakoub Aden Abdi and Jama Yusuf Elmi. 2011. Internet based telepsychiatry: a pilot case in Somaliland. Medicine, Conflict and Survival 27, 3 (2011), 145–150.
Raafat Aburukba, Fadi Aloul, Anam Mahmoud, Kamil Kamili, and Suad Ajmal. 2017. AutiAid: A learning mobile application for autistic children. In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom). 1–6. https://doi.org/10.1109/HealthCom.2017.8210788
Alyssa M Alcorn, Eloise Ainger, Vicky Charisi, Stefania Mantinioti, Sunčica Petrović, Bob R Schadenberg, Teresa Tavassoli, and Elizabeth Pellicano. 2019. Educators’ views on using humanoid robots with autistic learners in special education settings in England. Frontiers in Robotics and AI 6 (2019), 107.
Alyssa M. Alcorn, Eloise Ainger, Vicky Charisi, Stefania Mantinioti, Sunčica Petrović, Bob R. Schadenberg, Teresa Tavassoli, and Elizabeth Pellicano. 2019. Educators’ Views on Using Humanoid Robots With Autistic Learners in Special Education Settings in England. Frontiers in Robotics and AI 6 (2019). https://www.frontiersin.org/articles/10.3389/frobt.2019.00107
Narendra K Arora, MKC Nair, Sheffali Gulati, Vaishali Deshmukh, Archisman Mohapatra, Devendra Mishra, Vikram Patel, Ravindra M Pandey, Bhagabati C Das, Gauri Divan, 2018. Neurodevelopmental disorders in children aged 2–9 years: Population-based burden estimates across five regions in India. PLoS medicine 15, 7 (2018), e1002615.
Bojana Arsić, Anja Gajić, Sara Vidojković, Dragana Maćešić-Petrović, Aleksandra Bašić, and Ružica Zdravković Parezanović. 2022. The use of nao robots in teaching children with autism. European Journal of Alternative Education Studies 7, 1 (2022).
Priti Arun and Bir Singh Chavan. 2018. Development of a screening instrument for autism spectrum disorder: Chandigarh Autism Screening Instrument. The Indian journal of medical research 147, 4 (2018), 369.
B Ashwini, Vrinda Narayan, Ananya Bhatia, and Jainendra Shukla. 2021. Responsiveness towards robot-assisted interactions among pre-primary children of Indian ethnicity. In 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) (Vancouver, BC, Canada). IEEE.
Saminda Sundeepa Balasuriya, Laurianne Sitbon, Margot Brereton, and Stewart Koplick. 2020. How can social robots spark collaboration and engagement among people with intellectual disability?. In Proceedings of the 31st Australian Conference on Human-Computer-Interaction (Fremantle, WA, Australia) (OzCHI ’19). Association for Computing Machinery, New York, NY, USA, 209–220. https://doi.org/10.1145/3369457.3370915
Lorena A. Barba. 2022. Defining the Role of Open Source Software in Research Reproducibility. Computer 55, 8 (2022), 40–48. https://doi.org/10.1109/MC.2022.3177133
Emma Beede, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, and Laura M Vardoulakis. 2020. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1–12.
Emma Beede, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, and Laura M. Vardoulakis. 2020. A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3313831.3376718
Thomas Beelen, Ella Velner, Khiet P. Truong, Roeland Ordelman, Theo Huibers, and Vanessa Evers. 2023. Children’s Trust in Robots and the Information They Provide. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (, Hamburg, Germany,) (CHI EA ’23). Association for Computing Machinery, New York, NY, USA, Article 66, 7 pages. https://doi.org/10.1145/3544549.3585801
Rosanna Bellini. 2023. Paying the price: When intimate partners use technology for financial harm. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg Germany). ACM, New York, NY, USA.
Nicolas Belorgey. 2023. The Aadhaar battle: Why some players in the corporate world needed a biometric ID?Global Policy n/a, n/a (March 2023). https://doi.org/10.1111/1758-5899.13172
Jaishankar Bharatharaj, Loulin Huang, Rajesh Elara Mohan, Ahmed Al-Jumaily, and Christian Krägeloh. 2017. Robot-assisted therapy for learning and social interaction of children with autism spectrum disorder. Robotics 6, 1 (2017), 4.
Molly Bode, Tristan Goodrich, Marilyn Kimeu, Peter Okebukola, and Matt Wilson. 2021. Unlocking digital healthcare in lower-and middle-income countries. McKinsey & Company (2021).
Guy André Boy. 2021. A framework for flexibility analysis in sociotechnical systems. In Human–Computer Interaction Series. Springer International Publishing, Cham, 7–19.
Nelson C Brunsting, Melissa A Sreckovic, and Kathleen Lynne Lane. 2014. Special education teacher burnout: A synthesis of research from 1979 to 2013. Educ. Treat. Children 37, 4 (2014), 681–711.
John-John Cabibihan, Hifza Javed, Mohammed Aldosari, Thomas W. Frazier, and Haitham Elbashir. 2017. Sensing Technologies for Autism Spectrum Disorder Screening and Intervention. Sensors 17, 1 (Jan. 2017), 46. https://doi.org/10.3390/s17010046 Number: 1 Publisher: Multidisciplinary Digital Publishing Institute.
Álvaro Castro-González, Henny Admoni, and Brian Scassellati. 2016. Effects of form and motion on judgments of social robots’ animacy, likability, trustworthiness and unpleasantness. International Journal of Human-Computer Studies 90 (2016), 27–38.
Priyank Chandra, Syed Ishtiaque Ahmed, and Joyojeet Pal. 2017. Market Practices and the Bazaar: Technology Consumption in ICT Markets in the Global South. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems(CHI ’17). Association for Computing Machinery, New York, NY, USA, 4741–4752. https://doi.org/10.1145/3025453.3025970
Susmita Chatterjee, Sangita Dutta Gupta, and Parijat Upadhyay. 2020. Technology adoption and entrepreneurial orientation for rural women: Evidence from India. Technological Forecasting and Social Change 160 (Nov. 2020), 120236. https://doi.org/10.1016/j.techfore.2020.120236
Divya Chaudhary, Bhargav Bhat, Gemma E Shields, Linda M Davies, Jonathan Green, Tara Verghis, Reetabrata Roy, Divya Kumar, Minal Kakra, Vivek Vajaratkar, 2022. Development of a cost of illness inventory questionnaire for children with autism spectrum disorder in South Asia. BMC health services research 22, 1 (2022), 1–10.
Pallavi Chauhan, Jayanti Pujari, Prashant Yadav, and Sampurna Guha. 2022. Challenges And Opportunities of Technology Related Instruction For Children With Autism Spectrum Disorder: Parents Perspective. MIER Journal of Educational Studies Trends and Practices (2022), 320–337.
Jennifer Chipps, P Brysiewicz, and M Mars. 2012. Effectiveness and feasibility of telepsychiatry in resource constrained environments? A systematic review of the evidence. African journal of psychiatry 15, 4 (2012), 235–243.
Eva Yin-han Chung. 2019. Robotic intervention program for enhancement of social engagement among children with autism spectrum disorder. Journal of Developmental and Physical Disabilities 31, 4 (2019), 419–434.
Laura Cohen, Mahdi Khoramshahi, Robin N Salesse, Catherine Bortolon, Piotr Słowiński, Chao Zhai, Krasimira Tsaneva-Atanasova, Mario Di Bernardo, Delphine Capdevielle, Ludovic Marin, 2017. Influence of facial feedback during a cooperative human-robot task in schizophrenia. Scientific reports 7, 1 (2017), 15023.
Daniela Conti, Allegra Cattani, Santo Di Nuovo, and Alessandro Di Nuovo. 2019. Are future psychologists willing to accept and use a humanoid robot in their practice? Italian and English students’ perspective. Frontiers in psychology 10 (2019), 2138.
Eric Dahlin. 2022. Are Robots Really Stealing Our Jobs? Perception versus Experience. Socius: Sociological Research for a Dynamic World 8 (Jan. 2022), 237802312211313. https://doi.org/10.1177/23780231221131377
Kerstin Dautenhahn and Aude Billard. 2002. Games children with autism can play with Robota, a humanoid robotic doll. In Universal access and assistive technology: Proceedings of the Cambridge workshop on UA and AT’02. Springer, 179–190.
Julia Dawe, Craig Sutherland, Alex Barco, and Elizabeth Broadbent. 2019. Can social robots help children in healthcare contexts? A scoping review. BMJ paediatrics open 3, 1 (2019).
Pegah Soleimman Dehkordi, Hadi Moradi, Maryam Mahmoudi, and Hamid Reza Pouretemad. 2015. The design, development, and deployment of roboparrot for screening autistic children. International Journal of Social Robotics 7 (2015), 513–522.
Nicola Dell and Neha Kumar. 2016. The Ins and Outs of HCI for Development. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 2220–2232. https://doi.org/10.1145/2858036.2858081
Miraj U. Desai, Gauri Divan, Frederick J. Wertz, and Vikram Patel. 2012. The discovery of autism: Indian parents’ experiences of caring for their child with an autism spectrum disorder. Transcultural Psychiatry 49, 3–4 (June 2012), 613–637. https://doi.org/10.1177/1363461512447139
Laurie Dickstein-Fischer, Elizabeth Alexander, Xiaoan Yan, Hao Su, Kevin Harrington, and Gregory S. Fischer. 2011. An affordable compact humanoid robot for autism spectrum disorder interventions in children. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 5319–5322. https://doi.org/10.1109/IEMBS.2011.6091316
Joshua J Diehl, Lauren M Schmitt, Michael Villano, and Charles R Crowell. 2012. The clinical use of robots for individuals with autism spectrum disorders: A critical review. Research in autism spectrum disorders 6, 1 (2012), 249–262.
Lucy Diep, John-John Cabibihan, and Gregor Wolbring. 2015. Social Robots. In Proceedings of the 3rd 2015 Workshop on ICTs for improving Patients Rehabilitation Research Techniques (Lisbon Portugal). ACM, New York, NY, USA.
Indu Dubey, Rahul Bishain, Jayashree Dasgupta, Supriya Bhavnani, Matthew K Belmonte, Teodora Gliga, Debarati Mukherjee, Georgia Lockwood Estrin, Mark H Johnson, Sharat Chandran, 2021. Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap. Autism (2021), 13623613231182801.
Indu Dubey, Rahul Bishain, Jayashree Dasgupta, Supriya Bhavnani, Matthew K Belmonte, Teodora Gliga, Debarati Mukherjee, Georgia Lockwood Estrin, Mark H Johnson, Sharat Chandran, Vikram Patel, Sheffali Gulati, Gauri Divan, and Bhismadev Chakrabarti. 2023. Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap. Autism (July 2023), 13623613231182801.
Saad Elbeleidy, Terran Mott, and Tom Williams. 2022. Practical, ethical, and overlooked: Teleoperated socially assistive robots in the quest for autonomy. In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (Sapporo, Japan). IEEE.
Connor Esterwood and Lionel P Robert. 2022. Having the Right Attitude: How Attitude Impacts Trust Repair in Human—Robot Interaction. In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 332–341.
Marina Fridin and Mark Belokopytov. 2014. Acceptance of socially assistive humanoid robot by preschool and elementary school teachers. Comput. Human Behav. 33 (April 2014), 23–31.
M. Fujita. 2004. On activating human communications with pet-type robot AIBO. Proc. IEEE 92, 11 (2004), 1804–1813. https://doi.org/10.1109/JPROC.2004.835364
Norina Gasteiger, Ho Seok Ahn, Christopher Lee, Jongyoon Lim, Bruce A MacDonald, Geon Ha Kim, and Elizabeth Broadbent. 2022. Participatory design, development, and testing of assistive health robots with older adults: An international four-year project. ACM Trans. Hum. Robot Interact. 11, 4 (Dec. 2022), 1–19.
Davide Ghiglino, Pauline Chevalier, Federica Floris, Tiziana Priolo, and Agnieszka Wykowska. 2021. Follow the white robot: Efficacy of robot-assistive training for children with autism spectrum disorder. Research in Autism Spectrum Disorders 86 (Aug. 2021), 101822. https://doi.org/10.1016/j.rasd.2021.101822
Davide Ghiglino, Pauline Chevalier, Federica Floris, Tiziana Priolo, and Agnieszka Wykowska. 2021. Follow the white robot: Efficacy of robot-assistive training for children with autism spectrum disorder. Research in Autism Spectrum Disorders 86 (2021), 101822.
Shikoh Gitau and Gary Marsden. 2009. Fair Partnerships – Working with NGOs. In Human-Computer Interaction – INTERACT 2009. Springer Berlin Heidelberg, Berlin, Heidelberg, 704–707.
Imane Guemghar, Paula Pires de Oliveira Padilha, Amal Abdel-Baki, Didier Jutras-Aswad, Jesseca Paquette, and Marie-Pascale Pomey. 2022. Social robot interventions in mental health care and their outcomes, barriers, and facilitators: scoping review. JMIR Mental Health 9, 4 (2022), e36094.
Sheffali Gulati. 2017. Neurodevelopmental disorders: The Journey, the dreams and their realization. Annals of the National Academy of Medical Sciences (India) 53, 01 (2017), 030–035.
Richard Heeks. 2022. Digital inequality beyond the digital divide: conceptualizing adverse digital incorporation in the global South. Information Technology for Development 28, 4 (2022), 688–704.
Kurtis Heimerl, Esther Jang, and Innocent Ndubuisi-Obi. 2023. Stories from the Field (of Networking): Lessons from Deploying Research Systems in the Real World. (2023), 15–22. https://doi.org/10.1145/3609396.3610547
Vasiliki Holeva, V. A. Nikopoulou, C. Lytridis, C. Bazinas, P. Kechayas, G. Sidiropoulos, M. Papadopoulou, M. D. Kerasidou, C. Karatsioras, N. Geronikola, G. A. Papakostas, V. G. Kaburlasos, and A. Evangeliou. 2022. Effectiveness of a Robot-Assisted Psychological Intervention for Children with Autism Spectrum Disorder. J Autism Dev Disord (Nov. 2022). https://doi.org/10.1007/s10803-022-05796-5
Lorraine Hudson, Clement Amponsah, Josephine Ohenewa Bampoe, Julie Marshall, Nana Akua Victoria Owusu, Khalid Hussein, Jess Linington, Zoe Banks Gross, Jane Stokes, and Róisín McNaney. 2020. Co-designing digital tools to enhance speech and language therapy training in Ghana. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu HI USA). ACM, New York, NY, USA.
Claire AGJ Huijnen, Monique AS Lexis, Rianne Jansens, and Luc P de Witte. 2017. How to implement robots in interventions for children with autism? A co-creation study involving people with autism, parents and professionals. Journal of autism and developmental disorders 47 (2017), 3079–3096.
Lilly Irani, Janet Vertesi, Paul Dourish, Kavita Philip, and Rebecca E Grinter. 2010. Postcolonial computing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Atlanta Georgia USA). ACM, New York, NY, USA.
Stanislav Ivanov, Mihail Kuyumdzhiev, and Craig Webster. 2020. Automation fears: Drivers and solutions. Technology in Society 63 (Nov. 2020), 101431. https://doi.org/10.1016/j.techsoc.2020.101431
Stacy A Jacob and S Paige Furgerson. 2012. Writing interview protocols and conducting interviews: Tips for students new to the field of qualitative research.Qualitative report 17 (2012), 6.
Vishav Jyoti and Uttama Lahiri. 2020. Virtual Reality Based Joint Attention Task Platform for Children With Autism. IEEE Transactions on Learning Technologies 13, 1 (2020), 198–210. https://doi.org/10.1109/TLT.2019.2912371
Maya Kalyanpur. 2008. The Paradox of Majority Underrepresentation in Special Education in India: Constructions of Difference in a Developing Country. J Spec Educ 42, 1 (May 2008), 55–64. https://doi.org/10.1177/0022466907313610 Publisher: SAGE Publications Inc.
Hee Sun Kang, Kiyoko Makimoto, Rie Konno, and In Soon Koh. 2020. Review of outcome measures in PARO robot intervention studies for dementia care. Geriatric Nursing 41, 3 (2020), 207–214.
Patrick Gage Kelley, Yongwei Yang, Courtney Heldreth, Christopher Moessner, Aaron Sedley, Andreas Kramm, David T. Newman, and Allison Woodruff. 2021. Exciting, Useful, Worrying, Futuristic: Public Perception of Artificial Intelligence in 8 Countries. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society(AIES ’21). Association for Computing Machinery, New York, NY, USA, 627–637. https://doi.org/10.1145/3461702.3462605
James Kennedy, Séverin Lemaignan, and Tony Belpaeme. 2016. The cautious attitude of teachers towards social robots in schools. In Robots 4 Learning Workshop at IEEE RO-MAN 2016.
James Kennedy, Séverin Lemaignan, and Tony Belpaeme. 2016. The Cautious Attitude of Teachers Towards Social Robots in Schools. (2016). https://api.semanticscholar.org/CorpusID:49539703
Sara Kiesler, Aaron Powers, Susan R Fussell, and Cristen Torrey. 2008. Anthropomorphic interactions with a robot and robot–like agent. Soc. Cogn. 26, 2 (April 2008), 169–181.
Karen L Kilgore and Cynthia C Griffin. 1998. Beginning special educators: Problems of practice and the influence of school context. Teach. Educ. Spec. Educ. 21, 3 (July 1998), 155–173.
Elizabeth S Kim, Lauren D Berkovits, Emily P Bernier, Dan Leyzberg, Frederick Shic, Rhea Paul, and Brian Scassellati. 2013. Social robots as embedded reinforcers of social behavior in children with autism. Journal of autism and developmental disorders 43 (2013), 1038–1049.
Lynn Kirabo. 2020. "Our perspective matters.": using universal design goals to guide technology design in the global south. SIGACCESS Access. Comput.128 (Dec. 2020), 3:1–3:4. https://doi.org/10.1145/3441497.3441500
Manu Kohli, Arpan Kumar Kar, Varun Ganjigunte Prakash, and AP Prathosh. 2022. Deep Learning-Based Human Action Recognition Framework to Assess Children on the Risk of Autism or Developmental Delays. In International Conference on Neural Information Processing. Springer, 459–470.
Pradeep Raj Krishnappa Babu, Poojan Oza, and Uttama Lahiri. 2018. Gaze-Sensitive Virtual Reality Based Social Communication Platform for Individuals with Autism. IEEE Transactions on Affective Computing 9, 4 (2018), 450–462. https://doi.org/10.1109/TAFFC.2016.2641422
Divyanshu Kumar Singh, Sumita Sharma, Jainendra Shukla, and Grace Eden. 2020. Toy, tutor, peer, or pet?. In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge United Kingdom). ACM, New York, NY, USA.
Hirokazu Kumazaki, Yuichiro Yoshikawa, Yuko Yoshimura, Takashi Ikeda, Chiaki Hasegawa, Daisuke N Saito, Sara Tomiyama, Kyung-min An, Jiro Shimaya, Hiroshi Ishiguro, 2018. The impact of robotic intervention on joint attention in children with autism spectrum disorders. Molecular autism 9, 1 (2018), 1–10.
Aubrey J Kumm, Marisa Viljoen, and Petrus J de Vries. 2021. The digital divide in technologies for autism: feasibility considerations for low-and middle-income countries. Journal of Autism and Developmental Disorders (2021), 1–14.
Selvia Kuriakose, Subhash Kunche, B Narendranath, Pritish Jain, Suraj Sonker, and Uttama Lahiri. 2013. A step towards virtual reality based social communication for children with Autism. In 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE). 1–6. https://doi.org/10.1109/CARE.2013.6733744
Selvia Kuriakose and Uttama Lahiri. 2015. Understanding the Psycho-Physiological Implications of Interaction With a Virtual Reality-Based System in Adolescents With Autism: A Feasibility Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23, 4 (2015), 665–675. https://doi.org/10.1109/TNSRE.2015.2393891
Gabriella Lakatos, Luke Jai Wood, Dag Sverre Syrdal, Ben Robins, Abolfazl Zaraki, and Kerstin Dautenhahn. 2020. Robot-mediated intervention can assist children with autism to develop visual perspective taking skills. Paladyn, Journal of Behavioral Robotics 12, 1 (2020), 87–101.
Sofya Langman, Nicole Capicotto, Yaser Maddahi, and Kourosh Zareinia. 2021. Roboethics principles and policies in Europe and North America. SN Applied Sciences 3, 12 (Nov. 2021). https://doi.org/10.1007/s42452-021-04853-5
Jaeryoung Lee, Hiroki Takehashi, Chikara Nagai, Goro Obinata, and Dimitar Stefanov. 2012. Which Robot Features Can Stimulate Better Responses from Children with Autism in Robot-Assisted Therapy?International Journal of Advanced Robotic Systems 9, 3 (Sept. 2012), 72. https://doi.org/10.5772/51128 Publisher: SAGE Publications.
Connie Liu, Kassie Wang, Miyuki Goay, and Sofia (Hee Won) Yoon. 2022. Note: Examining the Gender Digital Divide in ICT: A Closer Look at Ghana, South Africa, and India. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)(COMPASS ’22). Association for Computing Machinery, New York, NY, USA, 623–627. https://doi.org/10.1145/3530190.3534832
Rajneesh Mahajan and Rajesh Sagar. 2023. Adequate Management of Autism Spectrum Disorder in Children in India. Indian J Pediatr 90, 4 (April 2023), 387–392. https://doi.org/10.1007/s12098-022-04352-4
Hamza Mahdi, Sami Alperen Akgun, Shahed Saleh, and Kerstin Dautenhahn. 2022. A survey on the design and evolution of social robots—Past, present and future. Robotics and Autonomous Systems (2022), 104193.
Paul K. McClure. 2018. “You’re Fired,” Says the Robot: The Rise of Automation in the Workplace, Technophobes, and Fears of Unemployment. Social Science Computer Review 36, 2 (April 2018), 139–156. https://doi.org/10.1177/0894439317698637 Publisher: SAGE Publications Inc.
Indrani Medhi and Kentaro Toyama. 2007. Full-context videos for first-time, non-literate PC users. In 2007 International Conference on Information and Communication Technologies and Development. IEEE, 1–9.
Patrick Mikalef, Kieran Conboy, Jenny Eriksson Lundström, and Aleš Popovič. 2022. Thinking responsibly about responsible AI and ‘the dark side’ of AI. European Journal of Information Systems 31, 3 (Feb. 2022), 257–268. https://doi.org/10.1080/0960085x.2022.2026621
Bonnie Nardi, Bill Tomlinson, Donald J. Patterson, Jay Chen, Daniel Pargman, Barath Raghavan, and Birgit Penzenstadler. 2018. Computing within limits. Commun. ACM 61, 10 (Sept. 2018), 86–93. https://doi.org/10.1145/3183582
Subrata Naskar, Robin Victor, Himabrata Das, and Kamal Nath. 2017. Telepsychiatry in India-Where do we stand? A comparative review between global and Indian telepsychiatry programs. Indian journal of psychological medicine 39, 3 (2017), 223–242.
John A Naslund, Kelly A Aschbrenner, Ricardo Araya, Lisa A Marsch, Jürgen Unützer, Vikram Patel, and Stephen J Bartels. 2017. Digital technology for treating and preventing mental disorders in low-income and middle-income countries: a narrative review of the literature. The Lancet Psychiatry 4, 6 (2017), 486–500.
Nirupama Natarajan, Sridhar Vaitheswaran, Maria R Lima, Maitreyee Wairagkar, and Ravi Vaidyanathan. 2022. Acceptability of social robots and adaptation of Hybrid-Face Robot for Dementia Care in India: A qualitative study. Am. J. Geriatr. Psychiatry 30, 2 (Feb. 2022), 240–245.
Chinasa T Okolo, Srujana Kamath, Nicola Dell, and Aditya Vashistha. 2021. “It cannot do all of my work”: Community Health Worker Perceptions of AI-Enabled Mobile Health Applications in Rural India. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama Japan). ACM, New York, NY, USA.
Roxanna Pakkar, Caitlyn Clabaugh, Rhianna Lee, Eric Deng, and Maja J Mataricć. 2019. Designing a Socially Assistive Robot for Long-Term In-Home Use for Children with Autism Spectrum Disorders. In 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 1–7. https://doi.org/10.1109/RO-MAN46459.2019.8956468
Melanie Palmer, Joanne Tarver, Virginia Carter Leno, Juan Paris Perez, Margot Frayne, Vicky Slonims, Andrew Pickles, Stephen Scott, Tony Charman, and Emily Simonoff. 2023. Parent, teacher and observational reports of emotional and behavioral problems in young autistic children. Journal of Autism and Developmental Disorders 53, 1 (2023), 296–309.
Amit Kumar Pandey and Rodolphe Gelin. 2018. A mass-produced sociable humanoid robot: Pepper: The first machine of its kind. IEEE Robot. Autom. Mag. 25, 3 (Sept. 2018), 40–48.
George A Papakostas, George K Sidiropoulos, Cristina I Papadopoulou, Eleni Vrochidou, Vassilis G Kaburlasos, Maria T Papadopoulou, Vasiliki Holeva, Vasiliki-Aliki Nikopoulou, and Nikolaos Dalivigkas. 2021. Social robots in special education: A systematic review. Electronics 10, 12 (2021), 1398.
Soomi Park, Patrick G T Healey, and Antonios Kaniadakis. 2021. Should Robots Blush?. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama Japan). ACM, New York, NY, USA.
Suravi Patra and Binod Kumar Patro. 2019. Affiliate stigma among parents of children with autism in eastern India. Asian Journal of Psychiatry 44 (Aug. 2019), 45–47. https://doi.org/10.1016/j.ajp.2019.07.018
Tobiaz Paulsson, Mengyu Zhong, Isabel García Velázquez, and Ginevra Castellano. 2023. Exploring Mothers’ Perspectives on Socially Assistive Robots in Peripartum Depression Screening. In Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. 486–490.
Sachin R Pendse, Naveena Karusala, Divya Siddarth, Pattie Gonsalves, Seema Mehrotra, John A Naslund, Mamta Sood, Neha Kumar, and Amit Sharma. 2019. Mental health in the global south: challenges and opportunities in HCI for development. In Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies. 22–36.
Frano Petric and Zdenko Kovačić. 2019. Hierarchical POMDP framework for a robot-assisted ASD diagnostic protocol. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 286–293.
Varun Ganjigunte Prakash, Manu Kohli, Swati Kohli, A. P. Prathosh, Tanu Wadhera, Diptanshu Das, Debasis Panigrahi, and John Vijay Sagar Kommu. 2023. Computer Vision-Based Assessment of Autistic Children: Analyzing Interactions, Emotions, Human Pose, and Life Skills. IEEE Access 11 (2023), 47907–47929. https://doi.org/10.1109/ACCESS.2023.3269027
Sajda Qureshi. 2013. What is the role of mobile phones in bringing about growth?Information Technology for Development 19, 1 (Jan. 2013), 1–4. https://doi.org/10.1080/02681102.2013.764597
Nazerke Rakhymbayeva, Aida Amirova, and Anara Sandygulova. 2021. A long-term engagement with a social robot for autism therapy. Frontiers in Robotics and AI 8 (2021), 669972.
Nazerke Rakhymbayeva, Nurila Seitkazina, Dauren Turabayev, Alina Pak, and Anara Sandygulova. 2020. A long-term study of robot-assisted therapy for children with severe autism and ADHD. In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. 401–402.
Andres A Ramirez-Duque, Anselmo Frizera-Neto, and Teodiano Freire Bastos. 2019. Robot-assisted autism spectrum disorder diagnostic based on artificial reasoning. Journal of Intelligent & Robotic Systems 96 (2019), 267–281.
Rebecca Ramnauth, Emmanuel Adéníran, Timothy Adamson, Michal A. Lewkowicz, Rohit Giridharan, Caroline Reiner, and Brian Scassellati. 2022. A Social Robot for Improving Interruptions Tolerance and Employability in Adults with ASD. In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 4–13. https://doi.org/10.1109/HRI53351.2022.9889383
Nimmi Rangaswamy and Nithya Sambasivan. 2011. Cutting Chai, Jugaad, and Here Pheri: towards UbiComp for a global community. Pers. Ubiquitous Comput. 15, 6 (Aug. 2011), 553–564.
Anastasia Raptopoulou, Antonios Komnidis, Panagiotis D Bamidis, and Alexandros Astaras. 2021. Human–robot interaction for social skill development in children with ASD: A literature review. Healthcare Technology Letters 8, 4 (2021), 90–96.
Samira Rasouli, Garima Gupta, Elizabeth Nilsen, and Kerstin Dautenhahn. 2022. Potential applications of social robots in robot-assisted interventions for social anxiety. International Journal of Social Robotics 14, 5 (2022), 1–32.
Natalia Reich-Stiebert and Friederike Eyssel. 2016. Robots in the classroom: What teachers think about teaching and learning with education robots. In Social Robotics(Lecture notes in computer science). Springer International Publishing, Cham, 671–680.
Jeba Rezwana and Mary Lou Maher. 2022. Understanding user perceptions, collaborative experience and user engagement in different human-AI interaction designs for co-creative systems. In Creativity and Cognition (Venice Italy). ACM, New York, NY, USA.
Kathleen Richardson, Mark Coeckelbergh, Kutoma Wakunuma, Erik Billing, Tom Ziemke, Pablo Gomez, Bram Vanderborght, and Tony Belpaeme. 2018. Robot enhanced therapy for children with autism (DREAM): A social model of autism. IEEE Technology and society magazine 37, 1 (2018), 30–39.
Daniel J Ricks and Mark B Colton. 2010. Trends and considerations in robot-assisted autism therapy. In 2010 IEEE international conference on robotics and automation. IEEE, 4354–4359.
Ben Robins, Kerstin Dautenhahn, R Te Boekhorst, and Aude Billard. 2005. Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills?Universal access in the information society 4 (2005), 105–120.
Ben Robins, Kerstin Dautenhahn, Rene Te Boekhorst, and Aude Billard. 2004. Effects of repeated exposure to a humanoid robot on children with autism. In Designing a more inclusive world. Springer, 225–236.
Ben Robins, Nuno Otero, Ester Ferrari, and Kerstin Dautenhahn. 2007. Eliciting requirements for a robotic toy for children with autism-results from user panels. In RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 101–106.
Richard Rose. 2022. Inclusive Education; Imposition or a Process of Shared International Learning? In The Inclusion Dialogue. Routledge. Num Pages: 11.
Ognjen Rudovic, Jaeryoung Lee, Lea Mascarell-Maricic, Björn W. Schuller, and Rosalind W. Picard. 2017. Measuring Engagement in Robot-Assisted Autism Therapy: A Cross-Cultural Study. Frontiers in Robotics and AI 4 (2017). https://www.frontiersin.org/articles/10.3389/frobt.2017.00036
Justine S. Chang, Manchun Hsiao, and Yiting Peng. 2021. An exploration on accounting professionals facing the development of AI. In The 2021 7th International Conference on Industrial and Business Engineering (Macau China). ACM, New York, NY, USA.
Anara Sandygulova, Zhanel Zhexenova, Bolat Tleubayev, Aidana Nurakhmetova, Dana Zhumabekova, Ilyas Assylgali, Yerzhan Rzagaliyev, and Aliya Zhakenova. 2019. Interaction design and methodology of robot-assisted therapy for children with severe ASD and ADHD. Paladyn, Journal of Behavioral Robotics 10, 1 (2019), 330–345.
Allison Sauppé and Bilge Mutlu. 2015. The social impact of a robot co-worker in industrial settings. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul Republic of Korea). ACM, New York, NY, USA.
Brian Scassellati. 2007. How social robots will help us to diagnose, treat, and understand autism. In Robotics research: Results of the 12th international symposium ISRR. Springer, 552–563.
Brian Scassellati, Laura Boccanfuso, Chien-Ming Huang, Marilena Mademtzi, Meiying Qin, Nicole Salomons, Pamela Ventola, and Frederick Shic. 2018. Improving social skills in children with ASD using a long-term, in-home social robot. Science Robotics 3, 21 (2018), eaat7544.
Bob R Schadenberg, Dennis Reidsma, Vanessa Evers, Daniel P Davison, Jamy J Li, Dirk KJ Heylen, Carlos Neves, Paulo Alvito, Jie Shen, Maja Pantić, 2021. Predictable Robots for Autistic Children—Variance in Robot Behaviour, Idiosyncrasies in Autistic Children’s Characteristics, and Child–Robot Engagement. ACM Transactions on Computer-Human Interaction (TOCHI) 28, 5 (2021), 1–42.
Arielle AJ Scoglio, Erin D Reilly, Jay A Gorman, and Charles E Drebing. 2019. Use of social robots in mental health and well-being research: systematic review. Journal of medical Internet research 21, 7 (2019), e13322.
Sofia Serholt, Wolmet Barendregt, Asimina Vasalou, Patrícia Alves-Oliveira, Aidan Jones, Sofia Petisca, and Ana Paiva. 2017. The case of classroom robots: teachers’ deliberations on the ethical tensions. AI & Soc 32, 4 (Nov. 2017), 613–631. https://doi.org/10.1007/s00146-016-0667-2
Tina Shahian, Gregory M Lee, Ana Lazic, Leggy A Arnold, Priya Velusamy, Christina M Roels, R Kiplin Guy, and Charles S Craik. 2009. Inhibition of a viral enzyme by a small-molecule dimer disruptor. Nat. Chem. Biol. 5, 9, 640–646.
Tina Shahian, Gregory M Lee, Ana Lazic, Leggy A Arnold, Priya Velusamy, Christina M Roels, R Kiplin Guy, and Charles S Craik. 2009. Inhibition of a viral enzyme by a small-molecule dimer disruptor. Nat. Chem. Biol. 5, 9, 640–646.
Suleman Shahid, Omar Mubin, Abdullah Al Mahmud, Zainab Iftikhar, and Rabiah Arshad. 2021. Child-Computer Interaction in the Global South: Designing for Children on the Margins. In Proceedings of the 20th Annual ACM Interaction Design and Children Conference (Athens, Greece) (IDC ’21). Association for Computing Machinery, New York, NY, USA, 655–657. https://doi.org/10.1145/3459990.3460518
Syamimi Shamsuddin, Luthffi Idzhar Ismail, Hanafiah Yussof, Nur Ismarrubie Zahari, Saiful Bahari, Hafizan Hashim, and Ahmed Jaffar. 2011. Humanoid robot NAO: Review of control and motion exploration. In 2011 IEEE international conference on Control System, Computing and Engineering. IEEE, 511–516.
Syamimi Shamsuddin, Hanafiah Yussof, Luthffi Idzhar Ismail, Salina Mohamed, Fazah Akhtar Hanapiah, and Nur Ismarrubie Zahari. 2012. Initial response in HRI-a case study on evaluation of child with autism spectrum disorders interacting with a humanoid robot Nao. Procedia Engineering 41 (2012), 1448–1455.
Sumita Sharma, Krishnaveni Achary, Harmeet Kaur, Juhani Linna, Markku Turunen, Blessin Varkey, Jaakko Hakulinen, and Sanidhya Daeeyya. 2018. ’Wow! You’re Wearing a Fitbit, You’re a Young Boy Now!": Socio-Technical Aspirations for Children with Autism in India. In Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility(ASSETS ’18). Association for Computing Machinery, New York, NY, USA, 174–184. https://doi.org/10.1145/3234695.3239329
Sumita Sharma, Saurabh Srivastava, Krishnaveni Achary, Blessin Varkey, Tomi Heimonen, Jaakko Samuli Hakulinen, Markku Turunen, and Nitendra Rajput. 2016. Promoting joint attention with computer supported collaboration in children with autism. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing. 1560–1571.
Sumita Sharma, Blessin Varkey, Krishnaveni Achary, Jaakko Hakulinen, Markku Turunen, Tomi Heimonen, Saurabh Srivastava, and Nitendra Rajput. 2018. Designing gesture-based applications for individuals with developmental disabilities: guidelines from user studies in India. ACM Transactions on Accessible Computing (TACCESS) 11, 1 (2018), 1–27.
Takanori Shibata. 2010. Integration of therapeutic robot, paro, into welfare systems. In Proceedings of the 28th Annual European Conference on Cognitive Ergonomics. 3–3.
Kristen Shinohara and Jacob O Wobbrock. 2016. Self-conscious or self-confident? A diary study conceptualizing the Social Accessibility of assistive technology. ACM Trans. Access. Comput. 8, 2 (Jan. 2016), 1–31.
Jainendra Shukla, Miguel Barreda-Ángeles, Joan Oliver, and Domènec Puig. 2017. Effectiveness of socially assistive robotics during cognitive stimulation interventions: Impact on caregivers. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). 62–67. https://doi.org/10.1109/ROMAN.2017.8172281 ISSN: 1944-9437.
Jainendra Shukla, Venkata Ratnadeep Suri, Jatin Garg, Krit Verma, and Prarthana Kansal. 2019. Mapping robotic affordances with pre-requisite learning interventions for children with autism spectrum disorder. In 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 1–8.
Alfred Said Sife, Elizabeth Kiondo, and Joyce G. Lyimo‐Macha. 2010. Contribution of Mobile Phones to Rural Livelihoods and Poverty Reduction in Morogoro Region, Tanzania. THE ELECTRONIC JOURNAL OF INFORMATION SYSTEMS IN DEVELOPING COUNTRIES 42, 1 (July 2010), 1–15. https://doi.org/10.1002/j.1681-4835.2010.tb00299.x
Nidhi Singal. 2006. Inclusive Education in India: International concept, national interpretation. International Journal of Disability, Development and Education 53, 3 (Sept. 2006), 351–369. https://doi.org/10.1080/10349120600847797 Publisher: Routledge _eprint: https://doi.org/10.1080/10349120600847797.
Matthijs Smakman, Paul Vogt, and Elly A Konijn. 2021. Moral considerations on social robots in education: A multi-stakeholder perspective. Computers & Education 174 (2021), 104317.
Matthijs Smakman, Paul Vogt, and Elly A Konijn. 2021. Moral considerations on social robots in education: A multi-stakeholder perspective. Comput. Educ. 174, 104317 (Dec. 2021), 104317.
Matthijs H J Smakman, Elly A Konijn, Paul Vogt, and Paulina Pankowska. 2021. Attitudes towards social robots in education: Enthusiast, practical, troubled, sceptic, and mindfully positive. Robotics 10, 1 (Jan. 2021), 24.
Amani Induni Soysa and Abdullah Al Mahmud. 2019. Technology for children with autism spectrum disorder: What do Sri Lankan parents and practitioners want?Interact. Comput. 31, 3 (May 2019), 282–302.
Micol Spitale, Silvia Silleresi, Franca Garzotto, and Maja J Matarić. 2023. Using socially assistive robots in speech-language therapy for children with language impairments. Int. J. Soc. Robot. 15, 9-10 (Oct. 2023), 1525–1542.
Qandeel Tariq, Scott Lanyon Fleming, Jessey Nicole Schwartz, Kaitlyn Dunlap, Conor Corbin, Peter Washington, Haik Kalantarian, Naila Z Khan, Gary L Darmstadt, and Dennis Paul Wall. 2019. Detecting developmental delay and autism through machine learning models using home videos of Bangladeshi children: Development and validation study. J. Med. Internet Res. 21, 4 (April 2019), e13822.
Afam Uzorka, Shiellah Namara, and Ademola Olatide Olaniyan. 2023. Modern technology adoption and professional development of lecturers. Educ. Inf. Technol. (April 2023), 1–27.
Caroline L. van Straten, Iris Smeekens, Emilia Barakova, Jeffrey Glennon, Jan Buitelaar, and Aoju Chen. 2018. Effects of robots’ intonation and bodily appearance on robot-mediated communicative treatment outcomes for children with autism spectrum disorder. Pers Ubiquit Comput 22, 2 (April 2018), 379–390. https://doi.org/10.1007/s00779-017-1060-y
Bram Vanderborght, Ramona Simut, Jelle Saldien, Cristina Pop, Alina S Rusu, Sebastian Pintea, Dirk Lefeber, and Daniel O David. 2012. Using the social robot probo as a social story telling agent for children with ASD. Interaction Studies 13, 3 (2012), 348–372.
Pratibha Vellanki, Stewart Greenhill, Thi Duong, Dinh Phung, Svetha Venkatesh, Jayashree Godwin, Kishna V. Achary, and Blessin Varkey. 2016. Computer assisted autism interventions for India. In Proceedings of the 28th Australian Conference on Computer-Human Interaction - OzCHI ’16. ACM Press, Launceston, Tasmania, Australia, 618–622. https://doi.org/10.1145/3010915.3011007
Jacqueline Kory Westlund, Goren Gordon, Samuel Spaulding, Jin Joo Lee, Luke Plummer, Marayna Martinez, Madhurima Das, and Cynthia Breazeal. 2016. Lessons from teachers on performing HRI studies with young children in schools. In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (Christchurch, New Zealand). IEEE.
Sally Whelan, Kathy Murphy, Eva Barrett, Cheryl Krusche, Adam Santorelli, and Dympna Casey. 2018. Factors affecting the acceptability of social robots by older adults including people with dementia or cognitive impairment: A literature review. Int. J. Soc. Robot. 10, 5 (Nov. 2018), 643–668.
Aida Zhanatkyzy, Zhansaule Telisheva, Aida Amirova, Nazerke Rakhymbayeva, and Anara Sandygulova. 2023. Multi-Purposeful Activities for Robot-Assisted Autism Therapy: What Works Best for Children’s Social Outcomes?. In Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. 34–43.
Xinyu Zhu, Xingguo Zhang, Zinan Chen, Zhanxun Dong, Zhenyu Gu, and Danni Chang. 2022. The trusted listener: The influence of anthropomorphic eye design of social robots on user’s perception of trustworthiness. In CHI Conference on Human Factors in Computing Systems (New Orleans LA USA). ACM, New York, NY, USA.
This study aimed to explore the potential value of a humanoid robot, NAO, in assisting the therapist during therapy sessions with children in the autism spectrum (ASD). We report findings from a single case, a 10-year old boy, Joe (pseudonym), ...
Social robots have been increasingly involved in our daily lives and provide a new environment for children's growth. The current study aimed to examine how children with and without Autism Spectrum Disorders (ASD) learned complex ...
Highlights
We examined how children with autism distrusted and deceived a social robot.
This article studies perceptual differences of three social robots by elementary school children of ages 6–13 years (n = 107) at research fairs. The autonomous humanoid robot Pepper, an advanced social robot primarily designed as a personal ...
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
B Ashwini, 
Honorable Mention