robotics education business model

• Open access
• Published: 10 February 2023

Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model

• Darmawansah Darmawansah   ORCID: orcid.org/0000-0002-3464-4598 1 ,
• Gwo-Jen Hwang   ORCID: orcid.org/0000-0001-5155-276X 1 , 3 ,
• Mei-Rong Alice Chen   ORCID: orcid.org/0000-0003-2722-0401 2 &
• Jia-Cing Liang   ORCID: orcid.org/0000-0002-1134-527X 1  

International Journal of STEM Education volume  10 , Article number:  12 ( 2023 ) Cite this article

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Fostering students’ competence in applying interdisciplinary knowledge to solve problems has been recognized as an important and challenging issue globally. This is why STEM (Science, Technology, Engineering, Mathematics) education has been emphasized at all levels in schools. Meanwhile, the use of robotics has played an important role in STEM learning design. The purpose of this study was to fill a gap in the current review of research on Robotics-based STEM (R-STEM) education by systematically reviewing existing research in this area. This systematic review examined the role of robotics and research trends in STEM education. A total of 39 articles published between 2012 and 2021 were analyzed. The review indicated that R-STEM education studies were mostly conducted in the United States and mainly in K-12 schools. Learner and teacher perceptions were the most popular research focus in these studies which applied robots. LEGO was the most used tool to accomplish the learning objectives. In terms of application, Technology (programming) was the predominant robotics-based STEM discipline in the R-STEM studies. Moreover, project-based learning (PBL) was the most frequently employed learning strategy in robotics-related STEM research. In addition, STEM learning and transferable skills were the most popular educational goals when applying robotics. Based on the findings, several implications and recommendations to researchers and practitioners are proposed.

Introduction

Over the past few years, implementation of STEM (Science, Technology, Engineering, and Mathematics) education has received a positive response from researchers and practitioners alike. According to Chesloff ( 2013 ), the winning point of STEM education is its learning process, which validates that students can use their creativity, collaborative skills, and critical thinking skills. Consequently, STEM education promotes a bridge between learning in authentic real-life scenarios (Erdoğan et al., 2016 ; Kelley & Knowles, 2016 ). This is the greatest challenge facing STEM education. The learning experience and real-life situation might be intangible in some areas due to pre- and in-conditioning such as unfamiliarity with STEM content (Moomaw, 2012 ), unstructured learning activities (Sarama & Clements, 2009), and inadequate preparation of STEM curricula (Conde et al., 2021 ).

In response to these issues, the adoption of robotics in STEM education has been encouraged as part of an innovative and methodological approach to learning (Bargagna et al., 2019 ; Ferreira et al., 2018 ; Kennedy et al., 2015 ; Köse et al., 2015 ). Similarly, recent studies have reported that the use of robots in school settings has an impact on student curiosity (Adams et al., 2011 ), arts and craftwork (Sullivan & Bers, 2016 ), and logic (Bers, 2008 ). When robots and educational robotics are considered a core part of STEM education, it offers the possibility to promote STEM disciplines such as engineering concepts or even interdisciplinary practices (Okita, 2014 ). Anwar et. al. ( 2019 ) argued that integration between robots and STEM learning is important to support STEM learners who do not immediately show interest in STEM disciplines. Learner interest can elicit the development of various skills such as computational thinking, creativity and motivation, collaboration and cooperation, problem-solving, and other higher-order thinking skills (Evripidou et al., 2020 ). To some extent, artificial intelligence (AI) has driven the use of robotics and tools, such as their application to designing instructional activities (Hwang et al., 2020 ). The potential for research on robotics in STEM education can be traced by showing the rapid increase in the number of studies over the past few years. The emphasis is on critically reviewing existing research to determine what prior research already tells us about R-STEM education, what it means, and where it can influence future research. Thus, this study aimed to fill the gap by conducting a systematic review to grasp the potential of R-STEM education.

In terms of providing the core concepts of roles and research trends of R-STEM education, this study explored beyond the scope of previous reviews by conducting content analysis to see the whole picture. To address the following questions, this study analyzed published research in the Web of Science database regarding the technology-based learning model (Lin & Hwang, 2019 ):

In terms of research characteristic and features, what were the location, sample size, duration of intervention, research methods, and research foci of the R-STEM education research?

In terms of interaction between participants and robots, what were the participants, roles of the robot, and types of robot in the R-STEM education research?

In terms of application, what were the dominant STEM disciplines, contribution to STEM disciplines, integration of robots and STEM, pedagogical interventions, and educational objectives of the R-STEM research?

• Literature review

Previous studies have investigated the role of robotics in R-STEM education from several research foci such as the specific robot users (Atman Uslu et al., 2022 ; Benitti, 2012 ; Jung & Won, 2018 ; Spolaôr & Benitti, 2017 ; van den Berghe et al., 2019 ), the potential value of R-STEM education (Çetin & Demircan, 2020 ; Conde et al., 2021 ; Zhang et al., 2021 ), and the types of robots used in learning practices (Belpaeme et al., 2018 ; Çetin & Demircan, 2020 ; Tselegkaridis & Sapounidis, 2021 ). While their findings provided a dynamic perspective on robotics, they failed to contribute to the core concept of promoting R-STEM education. Those previous reviews did not summarize the exemplary practice of employing robots in STEM education. For instance, Spolaôr and Benitti ( 2017 ) concluded that robots could be an auxiliary tool for learning but did not convey whether the purpose of using robots is essential to enhance learning outcomes. At the same time, it is important to address the use and purpose of robotics in STEM learning, the connections between theoretical pedagogy and STEM practice, and the reasons for the lack of quantitative research in the literature to measure student learning outcomes.

First, Benitti ( 2012 ) reviewed research published between 2000 and 2009. This review study aimed to determine the educational potential of using robots in schools and found that it is feasible to use most robots to support the pedagogical process of learning knowledge and skills related to science and mathematics. Five years later, Spolaôr and Benitti ( 2017 ) investigated the use of robots in higher education by employing the adopted-learning theories that were not covered in their previous review in 2012. The study’s content analysis approach synthesized 15 papers from 2002 to 2015 that used robots to support instruction based on fundamental learning theory. The main finding was that project-based learning (PBL) and experiential learning, or so-called hands-on learning, were considered to be the most used theories. Both theories were found to increase learners’ motivation and foster their skills (Behrens et al., 2010 ; Jou et al., 2010 ). However, the vast majority of discussions of the selected reviews emphasized positive outcomes while overlooking negative or mixed outcomes. Along the same lines, Jung and Won ( 2018 ) also reviewed theoretical approaches to Robotics education in 47 studies from 2006 to 2017. Their focused review of studies suggested that the employment of robots in learning should be shifted from technology to pedagogy. This review paper argued to determine student engagement in robotics education, despite disagreements among pedagogical traits. Although Jung and Won ( 2018 ) provided information of teaching approaches applied in robotics education, they did not offer critical discussion on how those approaches were formed between robots and the teaching disciplines.

On the other hand, Conde et. al. ( 2021 ) identified PBL as the most common learning approach in their study by reviewing 54 papers from 2006 to 2019. Furthermore, the studies by Çetin and Demircan ( 2020 ) and Tselegkaridis and Sapounidis ( 2021 ) focused on the types of robots used in STEM education and reviewed 23 and 17 papers, respectively. Again, these studies touted learning engagement as a positive outcome, and disregarded the different perspectives of robot use in educational settings on students’ academic performance and cognition. More recently, a meta-analysis by Zhang et. al. ( 2021 ) focused on the effects of robotics on students’ computational thinking and their attitudes toward STEM learning. In addition, a systematic review by Atman Uslu et. al. ( 2022 ) examined the use of educational robotics and robots in learning.

So far, the review study conducted by Atman Uslu et. al. ( 2022 ) could be the only study that has attempted to clarify some of the criticisms of using educational robots by reviewing the studies published from 2006 to 2019 in terms of their research issues (e.g., interventions, interactions, and perceptions), theoretical models, and the roles of robots in educational settings. However, they failed to take into account several important features of robots in education research, such as thematic subjects and educational objectives, for instance, whether robot-based learning could enhance students’ competence of constructing new knowledge, or whether robots could bring either a motivational facet or creativity to pedagogy to foster students’ learning outcomes. These are essential in investigating the trends of technology-based learning research as well as the role of technology in education as a review study is aimed to offer a comprehensive discussion which derived from various angles and dimensions. Moreover, the role of robots in STEM education was generally ignored in the previous review studies. Hence, there is still a need for a comprehensive understanding of the role of robotics in STEM education and research trends (e.g., research issues, interaction issues, and application issues) so as to provide researchers and practitioners with valuable references. That is, our study can remedy the shortcomings of previous reviews (Additional file 1 ).

The above comments demonstrate how previous scholars have understood what they call “the effectiveness of robotics in STEM education” in terms of innovative educational tools. In other words, despite their useful findings and ongoing recommendations, there has not been a thorough investigation of how robots are widely used from all angles. Furthermore, the results of existing review studies have been less than comprehensive in terms of the potential role of robotics in R-STEM education after taking into account various potential dimensions based on the technology-based model that we propose in this study.

The studies in this review were selected from the literature on the Web of Science, our sole database due to its rigorous journal research and qualified studies (e.g., Huang et al., 2022 ), discussing the adoption of R-STEM education, and the data collection procedures for this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009 ) as referred to by prior studies (e.g., Chen et al., 2021a , 2021b ; García-Martínez et al., 2020 ). Considering publication quality, previous studies (Fu & Hwang, 2018 ; Martín-Páez et al., 2019 ) suggested using Boolean expressions to search Web of Science databases. The search terms for “robot” are “robot” or “robotics” or “robotics” or “Lego” (Spolaôr & Benitti, 2017 ). According to Martín-Páez et. al. ( 2019 ), expressions for STEM education include “STEM” or “STEM education” or “STEM literacy” or “STEM learning” or “STEM teaching” or “STEM competencies”. These search terms were entered into the WOS database to search only for SSCI papers due to its wide recognition as being high-quality publications in the field of educational technology. As a result, 165 papers were found in the database. The search was then restricted to 2012–2021 as suggested by Hwang and Tsai ( 2011 ). In addition, the number of papers was reduced to 131 by selecting only publications of the “article” type and those written in “English”. Subsequently, we selected the category “education and educational research” which reduced the number to 60 papers. During the coding analysis, the two coders screened out 21 papers unrelated to R-STEM education. The coding result had a Kappa coefficient of 0.8 for both coders (Cohen, 1960 ). After the screening stage, a final total of 39 articles were included in this study, as shown in Fig.  1 . Also, the selected papers are marked with an asterisk in the reference list and are listed in Appendixes 1 and 2 .

PRISMA procedure for the selection process

Theoretical model, data coding, and analysis

This study comprised content analysis using a coding scheme to provide insights into different aspects of the studies in question (Chen et al., 2021a , 2021b ; Martín-Páez et al., 2019 ). The coding scheme adopted the conceptual framework proposed by Lin and Hwang ( 2019 ), comprising “STEM environments”, “learners”, and “robots”, as shown in Fig.  2 . Three issues were identified:

In terms of research issues, five dimensions were included: “location”, “sample size”, “duration of intervention”, (Zhong & Xia, 2020 ) “research methods”, (Johnson & Christensen, 2000 ) and “research foci”. (Hynes et al., 2017 ; Spolaôr & Benitti, 2017 ).

In terms of interaction issues, three dimensions were included: “participants”, (Hwang & Tsai, 2011 ), “roles of the robot”, and “types of robot” (Taylor, 1980 ).

In terms of application, five dimensions were included, namely “dominant STEM disciplines”, “integration of robot and STEM” (Martín‐Páez et al., 2019 ), “contribution to STEM disciplines”, “pedagogical intervention”, (Spolaôr & Benitti, 2017 ) and “educational objectives” (Anwar et al., 2019 ). Table 1 shows the coding items in each dimension of the investigated issues.

Model of R-STEM education theme framework

Figure  3 shows the distribution of the publications selected from 2012 to 2021. The first two publications were found in 2012. From 2014 to 2017, the number of publications steadily increased, with two, three, four, and four publications, respectively. Moreover, R-STEM education has been increasingly discussed within the last 3 years (2018–2020) with six, three, and ten publications, respectively. The global pandemic in the early 2020s could have affected the number of papers published, with only five papers in 2021. This could be due to the fact that most robot-STEM education research is conducted in physical classroom settings.

Number of publications on R-STEM education from 2012 to 2021

Table 2 displays the journals in which the selected papers were published, the number of papers published in each journal, and the journal’s impact factor. It can be concluded that most of the papers on R-STEM education research were published in the Journal of Science Education and Technology , and the International Journal of Technology and Design Education , with six papers, respectively.

Research issues

The geographic distribution of the reviewed studies indicated that more than half of the studies were conducted in the United States (53.8%), while Turkey and China were the location of five and three studies, respectively. Taiwan, Canada, and Italy were indicated to have two studies each. One study each was conducted in Australia, Mexico, and the Netherlands. Figure  4 shows the distribution of the countries where the R-STEM education was conducted.

Locations where the studies were conducted ( N  = 39)

Sample size

Regarding sample size, there were four most common sample sizes for the selected period (2012–2021): greater than 80 people (28.21% or 11 out of 39 studies), between 41 and 60 (25.64% or 10 out of 39 studies), 1 to 20 people (23.08% or 9 out of 39), and between 21 and 40 (20.51% or 8 out of 39 studies). The size of 61 to 80 people (2.56% or 1 out of 39 studies) was the least popular sample size (see Fig.  5 ).

Sample size across the studies ( N  = 39)

Duration of intervention

Regarding the duration of the study (see Fig.  6 ), experiments were mostly conducted for less than or equal to 4 weeks (35.9% or 14 out of 39 studies). This was followed by less than or equal to 8 weeks (25.64% or 10 out of 39 studies), less than or equal to 6 months (20.51% or 8 out 39 studies), less than or equal to 12 months (10.26% or 4 out of 39 studies), while less than or equal to 1 day (7.69% or 3 out of 39 studies) was the least chosen duration.

Duration of interventions across the studies ( N  = 39)

Research methods

Figure  7 demonstrates the trends in research methods from 2012 to 2021. The use of questionnaires or surveys (35.9% or 14 out of 39 studies) and mixed methods research (35.9% or 14 out of 39 studies) outnumbered other methods such as experimental design (25.64% or 10 out of 39 studies) and system development (2.56% or 1 out of 39 studies).

Frequency of each research method used in 2012–2021

Research foci

In these studies, research foci were divided into four aspects: cognition, affective, operational skill, and learning behavior. If the study involved more than one research focus, each issue was coded under each research focus.

In terms of cognitive skills, students’ learning performance was the most frequently measured (15 out of 39 studies). Six studies found that R-STEM education brought a positive result to learning performance. Two studies did not find any significant difference, while five studies showed mixed results or found that it depends. For example, Chang and Chen ( 2020 ) revealed that robots in STEM learning improved students’ cognition such as designing, electronic components, and computer programming.

In terms of affective skills, just over half of the reviewed studies (23 out of 39, 58.97%) addressed the students’ or teachers’ perceptions of employing robots in STEM education, of which 14 studies showed positive perceptions. In contrast, nine studies found mixed results. For instance, Casey et. al. ( 2018 ) determined students’ mixed perceptions of the use of robots in learning coding and programming.

Five studies were identified regarding operational skills by investigating students’ psychomotor aspects such as construction and mechanical elements (Pérez & López, 2019 ; Sullivan & Bers, 2016 ) and building and modeling robots (McDonald & Howell, 2012 ). Three studies found positive results, while two reported mixed results.

In terms of learning behavior, five out of 39 studies measured students’ learning behavior, such as students’ engagement with robots (Ma et al., 2020 ), students’ social behavior while interacting with robots (Konijn & Hoorn, 2020 ), and learner–parent interactions with interactive robots (Phamduy et al., 2017 ). Three studies showed positive results, while two found mixed results or found that it depends (see Table 3 ).

Interaction issues

Participants.

Regarding the educational level of the participants, elementary school students (33.33% or 13 studies) were the most preferred study participants, followed by high school students (15.38% or 6 studies). The data were similar for preschool, junior high school, in-service teachers, and non-designated personnel (10.26% or 4 studies). College students, including pre-service teachers, were the least preferred study participants. Interestingly, some studies involved study participants from more than one educational level. For example, Ucgul and Cagiltay ( 2014 ) conducted experiments with elementary and middle school students, while Chapman et. al. ( 2020 ) investigated the effectiveness of robots with elementary, middle, and high school students. One study exclusively investigated gifted and talented students without reporting their levels of education (Sen et al., 2021 ). Figure  8 shows the frequency of study participants between 2012 and 2021.

Frequency of research participants in the selected period

The roles of robot

For the function of robots in STEM education, as shown in Fig.  9 , more than half of the selected articles used robots as tools (31 out of 39 studies, 79.49%) for which the robots were designed to foster students’ programming ability. For instance, Barker et. al. ( 2014 ) investigated students’ building and programming of robots in hands-on STEM activities. Seven out of 39 studies used robots as tutees (17.95%), with the aim of students and teachers learning to program. For example, Phamduy et. al. ( 2017 ) investigated a robotic fish exhibit to analyze visitors’ experience of controlling and interacting with the robot. The least frequent role was tutor (2.56%), with only one study which programmed the robot to act as tutor or teacher for students (see Fig.  9 ).

Frequency of roles of robots

Types of robot

Furthermore, in terms of the types of robots used in STEM education, the LEGO MINDSTORMS robot was the most used (35.89% or 14 out of 39 studies), while Arduino was the second most used (12.82% or 5 out of 39 studies), and iRobot Create (5.12% or 2 out of 39 studies), and NAO (5.12% or 2 out of 39 studies) ranked third equal, as shown in Fig.  10 . LEGO was used to solve STEM problem-solving tasks such as building bridges (Convertini, 2021 ), robots (Chiang et al., 2020 ), and challenge-specific game boards (Leonard et al., 2018 ). Furthermore, four out of 36 studies did not specify the robots used in their studies.

Frequency of types of robots used

Application issues

The dominant disciplines and the contribution to stem disciplines.

As shown in Table 4 , the most dominant discipline in R-STEM education research published from 2012 to 2021 was technology. Engineering, mathematics, and science were the least dominant disciplines. Programming was the most common subject for robotics contribution to the STEM disciplines (25 out of 36 studies, 64.1%), followed by engineering (12.82%), and mathematical method (12.82%). We found that interdisciplinary was discussed in the selected period, but in relatively small numbers. However, this finding is relevant to expose the use of robotics in STEM disciplines as a whole. For example, Barker et. al. ( 2014 ) studied how robotics instructional modules in geospatial and programming domains could be impacted by fidelity adherence and exposure to the modules. The dominance of STEM subjects based on robotics makes it necessary to study the way robotics and STEM are integrated into the learning process. Therefore, the forms of STEM integration are discussed in the following sub-section to report how teaching and learning of these disciplines can have learning goals in an integrated STEM environment.

Integration of robots and STEM

There are three general forms of STEM integration (see Fig.  11 ). Of these studies, robot-STEM content integration was commonly used (22 studies, 56.41%), in which robot activities had multiple STEM disciplinary learning objectives. For example, Chang and Chen ( 2020 ) employed Arduino in a robotics sailboat curriculum. This curriculum was a cross-disciplinary integration, the objectives of which were understanding sailboats and sensors (Science), the direction of motors and mechanical structures (Engineering), and control programming (Technology). The second most common form was supporting robot-STEM content integration (12 out of 39 studies, 30.76%). For instance, KIBO robots were used in the robotics activities where the mechanical elements content area was meaningfully covered in support of the main programming learning objectives (Sullivan & Bers, 2019 ). The least common form was robot-STEM context integration (5 out of 39 studies, 12.82%) which was implemented through the robot to situate the disciplinary content goals in another discipline’s practices. For example, Christensen et. al. ( 2015 ) analyzed the impact of an after-school program that offered robots as part of students’ challenges in a STEM competition environment (geoscience and programming).

The forms of robot-STEM integration

Pedagogical interventions

In terms of instructional interventions, as shown in Fig.  12 , project-based learning (PBL) was the preferred instructional theory for using robots in R-STEM education (38.46% or 15 out 39 studies), with the aim of motivating students or robot users in the STEM learning activities. For example, Pérez and López ( 2019 ) argued that using low-cost robots in the teaching process increased students’ motivation and interest in STEM areas. Problem-based learning was the second most used intervention in this dimension (17.95% or 7 out of 39 studies). It aimed to improve students’ motivation by giving them an early insight into practical Engineering and Technology. For example, Gomoll et. al. ( 2017 ) employed robots to connect students from two different areas to work collaboratively. Their study showed the importance of robotic engagement in preliminary learning activities. Edutainment (12.82% or 5 out of 39 studies) was the third most used intervention. This intervention was used to bring together students and robots and to promote learning by doing. Christensen et. al. ( 2015 ) and Phamduy et. al. ( 2017 ) were the sample studies that found the benefits of hands-on and active learning engagement; for example, robotics competitions and robotics exhibitions could help retain a positive interest in STEM activities.

The pedagogical interventions in R-STEM education

Educational objectives

As far as the educational objectives of robots are concerned (see Fig.  13 ), the majority of robots are used for learning and transfer skills (58.97% or 23 out of 39 studies) to enhance students’ construction of new knowledge. It emphasized the process of learning through inquiry, exploration, and making cognitive associations with prior knowledge. Chang and Chen’s ( 2020 ) is a sample study on how learning objectives promote students’ ability to transfer science and engineering knowledge learned through science experiments to design a robotics sailboat that could navigate automatically as a novel setting. Moreover, it also explicitly aimed to examine the hands-on learning experience with robots. For example, McDonald and Howell ( 2012 ) described how robots engaged with early year students to better understand the concepts of literacy and numeracy.

Educational objectives of R-STEM education

Creativity and motivation were found to be educational objectives in R-STEM education for seven out of 39 studies (17.94%). It was considered from either the motivational facet of social trend or creativity in pedagogy to improve students’ interest in STEM disciplines. For instance, these studies were driven by the idea that employing robots could develop students’ scientific creativity (Guven et al., 2020 ), confidence and presentation ability (Chiang et al., 2020 ), passion for college and STEM fields (Meyers et al., 2012 ), and career choice (Ayar, 2015 ).

The general benefits of educational robots and the professional development of teachers were equally found in four studies each. The first objective, the general benefits of educational robotics, was to address those studies that found a broad benefit of using robots in STEM education without highlighting the particular focus. The sample studies suggested that robotics in STEM could promote active learning and improve students’ learning experience through social interaction (Hennessy Elliott, 2020 ) and collaborative science projects (Li et al., 2016 ). The latter, teachers’ professional development, was addressed by four studies (10.25%) to utilize robots to enhance teachers’ efficacy. Studies in this category discussed how teachers could examine and identify distinctive instructional approaches with robotics work (Bernstein et al., 2022 ), design meaningful learning instruction (Ryan et al., 2017 ) and lesson materials (Kim et al., 2015 ), and develop more robust cultural responsive self-efficacy (Leonard et al., 2018 ).

This review study was conducted using content analysis from the WOS collection of research on robotics in STEM education from 2012 to 2021. The findings are discussed under the headings of each research question.

RQ 1: In terms of research, what were the location, sample size, duration of intervention, research methods, and research foci of the R-STEM education research?

About half of the studies were conducted in North America (the USA and Canada), while limited studies were found from other continents (Europe and the Asia Pacific). This trend was identified in the previous study on robotics for STEM activities (Conde et al., 2021 ). Among 39 studies, 28 (71.79%) had fewer than 80 participants, while 11 (28.21%) had more than 80 participants. The intervention’s duration across the studies was almost equally divided between less than or equal to a month (17 out of 39 studies, 43.59%) and more than a month (22 out of 39 studies, 56.41%). The rationale behind the most popular durations is that these studies were conducted in classroom experiments and as conditional learning. For example, Kim et. al. ( 2018 ) conducted their experiments in a course offered at a university where it took 3 weeks based on a robotics module.

A total of four different research methodologies were adopted in the studies, the two most popular being mixed methods (35.89%) and questionnaires or surveys (35.89%). Although mixed methods can be daunting and time-consuming to conduct (Kucuk et al., 2013 ), the analysis found that it was one of the most used methods in the published articles, regardless of year. Chang and Chen ( 2022 ) embedded a mixed-methods design in their study to qualitatively answer their second research question. The possible reason for this is that other researchers prefer to use mixed methods as their research design. Their main research question was answered quantitatively, while the second and remaining research questions were reported through qualitative analysis (Casey et al., 2018 ; Chapman et al., 2020 ; Ma et al., 2020 ; Newton et al., 2020 ; Sullivan & Bers, 2019 ). Thus, it was concluded that mixed methods could lead to the best understanding and integration of research questions (Creswell & Clark, 2013 ; Creswell et al., 2003 ).

In contrast, system development was the least used compared to other study designs, as most studies used existing robotic systems. It should be acknowledged that the most common outcome we found was to enable students to understand these concepts as they relate to STEM subjects. Despite the focus on system development, the help of robotics was identified as increasing the success of STEM learning (Benitti, 2012 ). Because limited studies focused on system development as their primary purpose (1 out of 39 studies, 2.56%), needs analyses may ask whether the mechanisms, types, and challenges of robotics are appropriate for learners. Future research will need further design and development of personalized robots to fill this part of the research gap.

About half of the studies (23 studies, 58.97%) were focused on investigating the effectiveness of robots in STEM learning, primarily by collecting students’ and teachers’ opinions. This result is more similar to Belpaeme et al. ( 2018 ) finding that users’ perceptions were common measures in studies on robotics learning. However, identifying perceptions of R-STEM education may not help us understand exactly how robots’ specific features afford STEM learning. Therefore, it is argued that researchers should move beyond such simple collective perceptions in future research. Instead, further studies may compare different robots and their features. For instance, whether robots with multiple sensors, a sensor, or without a sensor could affect students’ cognitive, metacognitive, emotional, and motivational in STEM areas (e.g., Castro et al., 2018 ). Also, there could be instructional strategies embedded in R-STEM education that can lead students to do high-order thinking, such as problem-solving with a decision (Özüorçun & Bicen, 2017 ), self-regulated and self-engagement learning (e.g., Li et al., 2016 ). Researchers may also compare the robotics-based approach with other technology-based approaches (e.g., Han et al., 2015 ; Hsiao et al., 2015 ) in supporting STEM learning.

RQ 2: In terms of interaction, what were the participants, roles of the robots, and types of robots of the R-STEM education research?

The majority of reviewed studies on R-STEM education were conducted with K-12 students (27 studies, 69.23%), including preschool, elementary school, junior, and high school students. There were limited studies that involved higher education students and teachers. This finding is similar to the previous review study (Atman Uslu et al., 2022 ), which found a wide gap among research participants between K-12 students and higher education students, including teachers. Although it is unclear why there were limited studies conducted involving teachers and higher education students, which include pre-service teachers, we are aware of the critical task of designing meaningful R-STEM learning experiences which is likely to require professional development. In this case, both pre- and in-service teachers could examine specific objectives, identify topics, test the application, and design potential instruction to align well with robots in STEM learning (Bernstein et al., 2022 ). Concurrently, these pedagogical content skills in R-STEM disciplines might not be taught in the traditional pre-service teacher education and particular teachers’ development program (Huang et al., 2022 ). Thus, it is recommended that future studies could be conducted to understand whether robots can improve STEM education for higher education students and teachers professionally.

Regarding the role of robots, most were used as learning tools (31 studies, 79.48%). These robots are designed to have the functional ability to command or program some analysis and processing (Taylor, 1980 ). For example, Leonard et. al. ( 2018 ) described how pre-service teachers are trained in robotics activities to facilitate students’ learning of computational thinking. Therefore, robots primarily provide opportunities for learners to construct knowledge and skills. Only one study (2.56%), however, was found to program robots to act as tutors or teachers for students. Designing a robot-assisted system has become common in other fields such as language learning (e.g., Hong et al., 2016 ; Iio et al., 2019 ) and special education (e.g., Özdemir & Karaman, 2017 ) where the robots instruct the learning activities for students. In contrast, R-STEM education has not looked at the robot as a tutor, but has instead focused on learning how to build robots (Konijn & Hoorn, 2020 ). It is argued that robots with features as human tutors, such as providing personalized guidance and feedback, could assist during problem-solving activities (Fournier-Viger et al., 2013 ). Thus, it is worth exploring in what teaching roles the robot will work best as a tutor in STEM education.

When it comes to types of robots, the review found that LEGO dominated robots’ employment in STEM education (15 studies, 38.46%), while the other types were limited in their use. It is considered that LEGO tasks are more often associated with STEM because learners can be more involved in the engineering or technical tasks. Most researchers prefer to use LEGO in their studies (Convertini, 2021 ). Another interesting finding is about the cost of the robots. Although robots are generally inexpensive, some products are particularly low-cost and are commonly available in some regions (Conde et al., 2021 ). Most preferred robots are still considered exclusive learning tools in developing countries and regions. In this case, only one study offered a low-cost robot (Pérez & López, 2019 ). This might be a reason why the selected studies were primarily conducted in the countries and continents where the use of advanced technologies, such as robots, is growing rapidly (see Fig.  4 ). Based on this finding, there is a need for more research on the use of low-cost robots in R-STEM instruction in the least developed areas or regions of the world. For example, Nel et. al. ( 2017 ) designed a STEM program to build and design a robot which exclusively enabling students from low-income household to participate in the R-STEM activities.

RQ 3: In terms of application, what were the dominant STEM disciplines, contribution to STEM disciplines, integration of robots and STEM, pedagogical interventions, and educational objectives of the R-STEM research?

While Technology and Engineering are the dominant disciplines, this review found several studies that directed their research to interdisciplinary issues. The essence of STEM lies in interdisciplinary issues that integrate one discipline into another to create authentic learning (Hansen, 2014 ). This means that some researchers are keen to develop students’ integrated knowledge of Science, Technology, Engineering, and Mathematics (Chang & Chen, 2022 ; Luo et al., 2019 ). However, Science and Mathematics were given less weight in STEM learning activities compared to Technology and Engineering. This issue has been frequently reported as a barrier to implementing R-STEM in the interdisciplinary subject. Some reasons include difficulties in pedagogy and classroom roles, lack of curriculum integration, and a limited opportunity to embody one learning subject into others (Margot & Kettler, 2019 ). Therefore, further research is encouraged to treat these disciplines equally, so is the way of STEM learning integration.

The subject-matter results revealed that “programming” was the most common research focus in R-STEM research (25 studies). Researchers considered programming because this particular topic was frequently emphasized in their studies (Chang & Chen, 2020 , 2022 ; Newton et al., 2020 ). Similarly, programming concepts were taught through support robots for kindergarteners (Sullivan & Bers, 2019 ), girls attending summer camps (Chapman et al., 2020 ), and young learners with disabilities (Lamptey et al., 2021 ). Because programming simultaneously accompanies students’ STEM learning, we believe future research can incorporate a more dynamic and comprehensive learning focus. Robotics-based STEM education research is expected to encounter many interdisciplinary learning issues.

Researchers in the reviewed studies agreed that the robot could be integrated with STEM learning with various integration forms. Bryan et. al. ( 2015 ) argued that robots were designed to develop multiple learning goals from STEM knowledge, beginning with an initial learning context. It is parallel with our finding that robot-STEM content integration was the most common integration form (22 studies, 56.41%). In this form, studies mainly defined their primary learning goals with one or more anchor STEM disciplines (e.g., Castro et al., 2018 ; Chang & Chen, 2020 ; Luo et al., 2019 ). The learning goals provided coherence between instructional activities and assessments that explicitly focused on the connection among STEM disciplines. As a result, students can develop a deep and transferable understanding of interdisciplinary phenomena and problems through emphasizing the content across disciplines (Bryan et al., 2015 ). However, the findings on learning instruction and evaluation in this integration are inconclusive. A better understanding of the embodiment of learning contexts is needed, for instance, whether instructions are inclusive, socially relevant, and authentic in the situated context. Thus, future research is needed to identify the quality of instruction and evaluation and the specific characteristics of robot-STEM integration. This may place better provision of opportunities for understanding the form of pedagogical content knowledge to enhance practitioners’ self-efficacy and pedagogical beliefs (Chen et al., 2021a , 2021b ).

Project-based learning (PBL) was the most used instructional intervention with robots in R-STEM education (15 studies, 38.46%). Blumenfeld et al. ( 1991 ) credited PBL with the main purpose of engaging students in investigating learning models. In the case of robotics, students can create robotic artifacts (Spolaôr & Benitti, 2017 ). McDonald and Howell ( 2012 ) used robotics to develop technological skills in lower grades. Leonard et. al. ( 2016 ) used robots to engage and develop students’ computational thinking strategies in another example. In the aforementioned study, robots were used to support learning content in informal education, and both teachers and students designed robotics experiences aligned with the curriculum (Bernstein et al., 2022 ). As previously mentioned, this study is an example of how robots can cover STEM content from the learning domain to support educational goals.

The educational goal of R-STEM education was the last finding of our study. Most of the reviewed studies focused on learning and transferable skills as their goals (23 studies, 58.97%). They targeted learning because the authors investigated the effectiveness of R-STEM learning activities (Castro et al., 2018 ; Convertini, 2021 ; Konijn & Hoorn, 2020 ; Ma et al., 2020 ) and conceptual knowledge of STEM disciplines (Barak & Assal, 2018 ; Gomoll et al., 2017 ; Jaipal-Jamani & Angeli 2017 ). They targeted transferable skills because they require learners to develop individual competencies in STEM skills (Kim et al., 2018 ; McDonald & Howell, 2012 ; Sullivan & Bers, 2016 ) and to master STEM in actual competition-related skills (Chiang et al., 2020 ; Hennessy Elliott, 2020 ).

Conclusions and implications

The majority of the articles examined in this study referred to theoretical frameworks or certain applications of pedagogical theories. This finding contradicts Atman Uslu et. al. ( 2022 ), who concluded that most of the studies in this domain did not refer to pedagogical approaches. Although we claim the employment pedagogical frameworks in the examined articles exist, those articles primarily did not consider a strict instructional design when employing robots in STEM learning. Consequently, the discussions in the studies did not include how the learning–teaching process affords students’ positive perceptions. Therefore, both practitioners and researchers should consider designing learning instruction using robots in STEM education. To put an example, the practitioners may regard students’ zone of proximal development (ZPD) when employing robot in STEM tasks. Giving an appropriate scaffolding and learning contents are necessary for them to enhance their operational skills, application knowledge and emotional development. Although the integration between robots and STEM education was founded in the reviewed studies, it is worth further investigating the disciplines in which STEM activities have been conducted. This current review found that technology and engineering were the subject areas of most concern to researchers, while science and mathematics did not attract as much attention. This situation can be interpreted as an inadequate evaluation of R-STEM education. In other words, although those studies aimed at the interdisciplinary subject, most assessments and evaluations were monodisciplinary and targeted only knowledge. Therefore, it is necessary to carry out further studies in these insufficient subject areas to measure and answer the potential of robots in every STEM field and its integration. Moreover, the broadly consistent reporting of robotics generally supporting STEM content could impact practitioners only to employ robots in the mainstream STEM educational environment. Until that point, very few studies had investigated the prominence use of robots in various and large-scale multidiscipline studies (e.g., Christensen et al., 2015 ).

Another finding of the reviewed studies was the characteristic of robot-STEM integration. Researchers and practitioners must first answer why and how integrated R-STEM could be embodied in the teaching–learning process. For example, when robots are used as a learning tool to achieve STEM learning objectives, practitioners are suggested to have application knowledge. At the same time, researchers are advised to understand the pedagogical theories so that R-STEM integration can be flexibly merged into learning content. This means that the learning design should offer students’ existing knowledge of the immersive experience in dealing with robots and STEM activities that assist them in being aware of their ideas, then building their knowledge. In such a learning experience, students will understand the concept of STEM more deeply by engaging with robots. Moreover, demonstration of R-STEM learning is not only about the coherent understanding of the content knowledge. Practitioners need to apply both flexible subject-matter knowledge (e.g., central facts, concepts and procedures in the core concept of knowledge), and pedagogical content knowledge, which specific knowledge of approaches that are suitable for organizing and delivering topic-specific content, to the discipline of R-STEM education. Consequently, practitioners are required to understand the nature of robots and STEM through the content and practices, for example, taking the lead in implementing innovation through subject area instruction, developing collaboration that enriches R-STEM learning experiences for students, and being reflective practitioners by using students’ learning artifacts to inform and revise practices.

Limitations and recommendations for future research

Overall, future research could explore the great potential of using robots in education to build students’ knowledge and skills when pursuing learning objectives. It is believed that the findings from this study will provide insightful information for future research.

The articles reviewed in this study were limited to journals indexed in the WOS database and R-STEM education-related SSCI articles. However, other databases and indexes (e.g., SCOPUS, and SCI) could be considered. In addition, the number of studies analyzed was relatively small. Further research is recommended to extend the review duration to cover the publications in the coming years. The results of this review study have provided directions for the research area of STEM education and robotics. Specifically, robotics combined with STEM education activities should aim to foster the development of creativity. Future research may aim to develop skills in specific areas such as robotics STEM education combined with the humanities, but also skills in other humanities disciplines across learning activities, social/interactive skills, and general guidelines for learners at different educational levels. Educators can design career readiness activities to help learners build self-directed learning plans.

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

Science, technology, engineering, and mathematics

Robotics-based STEM

Project-based learning

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Acknowledgements

The authors would like to express their gratefulness to the three anonymous reviewers for providing their precious comments to refine this manuscript.

This study was supported by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST-108-2511-H-011-005-MY3; National Science and Technology Council (TW) (NSTC 111-2410-H-031-092-MY2); Soochow University (TW) (111160605-0014). Any opinions, findings, conclusions, and/or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of Ministry of Science and Technology of Taiwan.

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Darmawansah Darmawansah, Gwo-Jen Hwang & Jia-Cing Liang

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DD, MR and GJ conceptualized the study. MR wrote the outline and DD wrote draft. DD, MR and GJ contributed to the manuscript through critical reviews. DD, MR and GJH revised the manuscript. DD, MR and GJ finalized the manuscript. DD edited the manuscript. MR and GJ monitored the project and provided adequate supervision. DD, MR and JC contributed with data collection, coding, analyses and interpretation. All authors read and approved the final manuscript.

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Appendix 1. Summary of selected studies from the angle of research issue

Appendix 2. Summary of selected studies from the angles of interaction and application

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Darmawansah, D., Hwang, GJ., Chen, MR.A. et al. Trends and research foci of robotics-based STEM education: a systematic review from diverse angles based on the technology-based learning model. IJ STEM Ed 10 , 12 (2023). https://doi.org/10.1186/s40594-023-00400-3

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7 Examples of Robotics in Education to Know

Robots are improving the education industry in various ways, and these seven companies are accelerating this trend.

Teachers are receiving a much-needed boost from robotics in education , especially in light of looming losses. A National Education Association survey reveals that 55 percent of its members are planning on leaving the teaching profession early .

Uses of Robotics in Education

While robots are in no way replacements for human educators, advancements in artificial intelligence and interactive technology have made robots suitable for certain roles in and out of the classroom. From teaching language lessons to tutoring students one-on-one, robotics in education is ready to support the next wave of learners.

Benefits of Robotics in Education

Robots have taken major strides forward in the education industry, cultivating children’s social skills, personalizing lessons through one-on-one interactions and taking on other roles to alleviate the hefty workloads of teachers.  

Stronger Social Skills

To appeal to humans’ social nature , education robots come equipped with eyes, mouths and other facial features that people rely on to read emotions. These robots also feature technology that allows them to analyze speech and facial reactions, so they can determine an appropriate response. With an emphasis on emotional development, robots can teach subjects while also helping younger kids learn how to perceive and engage with others’ emotions.

Personalized Learning Options

Robots have exhibited enough autonomy to the point where they can interact with kids in one-on-one scenarios. For example, Softbank Robotics developed a Nao model as part of a European research project called L2TOR , with the goal of teaching young children a second language. The robot acted as a tutor, giving students the individual attention they needed to learn a new language at their own pace.  

Affordable Teaching Alternatives 

Teaching shortages have put pressure on remaining educators to serve more students, but robots are easing some of the strain. In addition to one-on-one conversations, robots are capable of leading small groups and assisting kids who need more in-depth attention in the classroom. Relying on robots to fulfill basic roles saves schools from scouring a limited talent pool for more teachers and further stretching their financial budgets. 

Roles of Robotics in Education

While robots are not an all-around solution for the education system, they are well-suited for certain demographics and contexts.  

Younger Age Groups

Robots can teach people of all ages, but they are especially effective at engaging younger children , who may be drawn to the novelty of robotics and the hands-on approach to learning many robots encourage. 

One-on-One Interactions 

The ability of robots to hold basic conversations with children makes them ideal for personalized learning roles. Robots can be tutors and teaching assistants, serving homeschooled students and students who need additional support in the classroom. Robots can also switch roles and become peer learners, where they learn alongside students who teach them. 

More Structured Subjects

Robots are better at guiding lessons that are structured, require short responses and center on repetition. That means subjects like math, science and language vocabulary are easier for them.

Will Classroom Robots Replace Teachers?

Despite concerns some might have about teachers’ job security , a much more likely scenario — and one that’s already playing out — involves robots supplementing teachers instead of replacing them.

Robots can handle redundant tasks like responding to student emails in higher ed, address questions in small groups within a classroom setting and make lessons more fun for younger kids by treating them in a non-judgemental manner. All these abilities improve the education process for students, making robots a tool that some educators may want to have on hand. 

Robots and other assistive technologies have become a useful component in the classroom, providing the detailed attention instructors are sometimes unable to give to students with disabilities and students who miss class or struggle with a particular topic.

Schools and instructors should collaborate on when and how to apply robotics in education to enhance the experience for both students and teachers. Finding supportive roles for robots lessens the workloads of instructors while enabling them to focus more of their energy on teaching difficult subjects and nurturing students’ personal growth.   

7 Examples of Robotics in Education

Robotics for stem learning.

Wonder Workshop

Location: San Mateo, California

How it’s using robotics in education: Wonder Workshop provides a more engaging classroom experience with its STEM learning robots Dash and Cue. Dash captivates younger kids with singing and dancing while exhibiting the ability to respond to voices. Cue comes with similar features, but specializes in more complex interactions to cater to older children. With both robots, teachers can deliver an immersive method for kids to learn robotics, coding, engineering and other STEM-based topics.

Softbank Robotics

Location: Tokyo, Japan

How it’s using robotics in education:  Softbank is the company behind Nao, the robot used in the L2TOR project, as well as Pepper, a taller high-tech humanoid robot . Both have been deployed in a variety of industries ranging from retail to healthcare, but Softbank thinks its inventions could also work well in the classroom . As teaching assistants for STEAM (Science, Technology Engineering, Arts and Math), they can serve as customized instructors for individuals or groups, engage with students to enhance social and emotional skills and keep detailed data on their interactions so teachers can track student development.

The LEGO Group

Location: Billund, Denmark

How it’s using robotics in education: In 1984, an MIT professor designed a programming language for children that could be used to make robot “turtles” move in a certain direction, turn around and draw things. Lego CEO Kjeld Kirk Kristiansen learned about the experiment and thought his toy bricks could benefit from the same technology. Lego’s collaboration with MIT eventually became known as Lego Mindstorms , a line of programmable Lego robots designed to interest kids in STEM (Science, Technology, Engineering and Math) and computer programming. The company envisions home and classroom applications , and there are even international Lego robotics tournaments as part of the  First Lego League .

VEX Robotics

Location: Greenville, Texas

How it’s using robotics in education: Vex Robotics aims to interest students in STEM by teaching them to build and program robots. The company offers an array of robotic products for students of different ages as well as a curriculum for educators to use as a guide. Its annual competitions  host entrants (elementary age through high school) from around the world who vie for top honors in robotics-related subjects like research, math and science.

Robotics for Special Education

University of Hertfordshire

Location: Hatfield, U.K.

How it’s using robotics in education: Kaspar (Kinesics and Synchronization in Personal Assistant Robotics) is a project from the University of Hertfordshire. A doll-like humanoid, Kaspar helps teachers and parents support children who have autism or other communication difficulties. Kaspar is intentionally designed with a minimally expressive face, keeping the needs of children with autism top of mind.

Location: San Francisco, California

How it’s using robotics in education: BeatBots is the company behind  Keepon , which achieved a degree of internet fame for its love of the band Spoon. A small yellow robot, Keepon was originally designed by Hideki Kozima to promote social interaction and communication skills among children with developmental difficulties . While its commercial version is used in classrooms and by therapists, its consumer model ( MyKeepon ) is available to anyone.

Robotics for Social Learning

Massachusetts Institute of Technology

Location: Cambridge, Massachusetts

How it’s using robotics in education: An MIT spinoff, Personal Robots Group conducts robotics research and engineers a variety of robots. One of them is a fuzzy social robot named Tega . Social robots are meant to promote interaction between humans and robots. This one specifically is a learning assistant that engages kids in educational activities — in part by upping the fun factor. Other PRG work focuses on using the company’s robots to help kids learn a second language .

The Future of Robotics in Education

Educators may not have a choice when it comes to deciding whether to apply robotics in education. According to a UNESCO report, the world still needs 69 million teachers to meet global education goals in 2030. The situation has become even more urgent in light of pandemic disruptions, human migrations and a general lack of resources.    

Machine learning and AI technology are already powering transcription services, online courses, tactile game sets and other interactive learning elements. The next logical step would be to integrate robots into learning environments, and robots are rising to the occasion. 

Besides demonstrating the ability to handle STEM subjects and more structured conversations, robots are also expanding into the social and emotional aspects of learning. Educators should still be careful when considering adopting robotics in education since it’s unclear how robots affect the social-emotional development of children. However, trusting robots to complete repetitive tasks and basic instructive roles can empower educators to become more efficient while making topics more enjoyable for students of different age groups. 

Introducing robotics in education in a responsible and thoughtful manner can offer much-needed support to both students and teachers as the education industry faces uncertain times.

Recent Robotics Articles

AI for Good blog

The future of educational robotics: enhancing education, bridging the digital divide, and supporting diverse learners.

• Robotics for Good
• 31 March 2023

By Alexandra Bustos Iliescu

Educational robotics is poised to revolutionize the learning landscape by equipping children with essential skills and preparing them for a future where AI and robotics are integral to their lives. However, there are challenges that must be addressed to make educational robotics accessible, affordable, and effective in classrooms. In a recent panel discussion, experts in the field explored the current state of educational robotics, its challenges, the future research directions, and the benefits for diverse learners.

“Robots increase the possibility of doing work at a distance” said Tony Prescott , Professor from University of Sheffield .

Credits: Sneh Vaswani from Miko

Role of Robots in Education

Educational robots can serve various purposes in enhancing learning experiences. They can promote active engagement, problem-solving, and collaboration among students as active learning tools. By introducing robotics in the classroom, children can develop their critical thinking and creativity skills. Robots can also serve as a scaffold for developing social skills, especially for shy children or those with special needs. Interacting with robots can be less intimidating and more predictable than interacting with peers, fostering confidence in social situations. Additionally, robots can act as co-learners or tutors, encouraging children to explain concepts or teach the robot, thereby reinforcing their own understanding.

Credits: University of Sheffield – the animal-like robot Miro-e designed for applications in research, education and healthcare

Challenges in Implementing Robots in Classrooms

Two primary barriers hinder the widespread adoption of robots in classrooms: cost and teacher training. Schools often have limited budgets, making it difficult to invest in expensive robotic equipment. The high cost of advanced robotic systems may exacerbate the digital divide between schools with access to resources and those without. Furthermore, teachers may lack the necessary time and training to effectively integrate robotics into their curriculums. With an already packed schedule and curriculum demands, incorporating robotics can be a daunting task for educators.

Addressing these challenges requires the development of better teacher training programs and government support for prioritizing robotics and AI in education. By recognizing the importance of these technologies for the future workforce, governments can promote the adoption of educational robotics.

Credits: ROBOCAT – young participants working on their robots

Accessibility and Affordability

“There has been a massive change in the past decade where kids have been empowered with new mediums of technology and parents have started getting busier in their day-to-day lives” said Sneh Vaswani , Co-Founder and CEO from Miko .

Parents are dissatisfied by screen addiction in their young kids

Parents will not prevent their kids from technology as every other child has access to it

To bridge the digital divide and ensure equal access to educational robotics, it is essential to make the technology accessible and affordable. Shared resources, such as community centers or mobile labs, can provide access to robotics equipment for multiple schools. Repurposing disused robots from universities and industries for educational purposes can reduce costs and promote sustainability. A subscription model for educational robotics can make it more affordable for consumers, allowing users to purchase a basic robot at a lower price point and subscribe to additional services and content as needed.

Hot Research Topics and the Role of Large Language Models

Current research in educational robotics focuses on swarm robotics, which explores the use of multiple robots working together to lead to more effective learning experiences. Soft robotics investigates the use of soft materials and components in robot design, opening up new possibilities for educational applications. Research in active learning and collaboration seeks to enhance active learning experiences and collaborative problem-solving capabilities.

Large language models like GPT-4 are expected to play a crucial role in enhancing conversational experiences with robots. These models, combined with improvements in emotional intelligence and collaborative problem-solving capabilities, can create more engaging and effective educational robots.

Credit: IReCHeCk EPFL

Educational robotics holds great promise for transforming learning experiences, equipping children with valuable skills for their future careers, and supporting diverse learners. By addressing the challenges of cost and teacher training, exploring innovative research areas, and focusing on accessibility, educational robotics can become an inclusive and effective tool for classrooms worldwide.

Exploring the role of socially-assistive robots in education, companionship, and care

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Managing social-educational robotics for students with autism spectrum disorder through business model canvas and customer discovery

Anshu saxena arora.

1 Department of Management, School of Business and Public Administration, University of the District of Columbia, Washington, DC, United States

K. Sivakumar

2 Department of Marketing, College of Business, Lehigh University, Bethlehem, PA, United States

John R. McIntyre

3 Center for International Business Education and Research (CIBER), Georgia Institute of Technology, Atlanta, GA, United States

Rebecca Ramnauth , Yale University, United States

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Social-educational robotics, such as NAO humanoid robots with social, anthropomorphic, humanlike features, are tools for learning, education, and addressing developmental disorders (e.g., autism spectrum disorder or ASD) through social and collaborative robotic interactions and interventions. There are significant gaps at the intersection of social robotics and autism research dealing with how robotic technology helps ASD individuals with their social, emotional, and communication needs, and supports teachers who engage with ASD students. This research aims to (a) obtain new scientific knowledge on social-educational robotics by exploring the usage of social robots (especially humanoids) and robotic interventions with ASD students at high schools through an ASD student–teacher co-working with social robot–social robotic interactions triad framework; (b) utilize Business Model Canvas (BMC) methodology for robot design and curriculum development targeted at ASD students; and (c) connect interdisciplinary areas of consumer behavior research, social robotics, and human-robot interaction using customer discovery interviews for bridging the gap between academic research on social robotics on the one hand, and industry development and customers on the other. The customer discovery process in this research results in eight core research propositions delineating the contexts that enable a higher quality learning environment corresponding with ASD students’ learning requirements through the use of social robots and preparing them for future learning and workforce environments.

1 Introduction

Social-educational robotics and technological advancements in human-robot interaction (HRI) are revolutionizing education, learning, and cognitive rehabilitation capabilities ( Leoste et al., 2022 ; Bharatharaj et al., 2023 ; Soleiman et al., 2023 ). HRI research is a rapidly expanding field of artificial intelligence (AI) encompassing science, technology, engineering, and mathematics (STEM), robotics, human-computer interaction, psychology, and social sciences. Researchers are now investigating the social, behavioral, and cognitive aspects of HRI ( Newman et al., 2022 ).

Social robots, especially humanoids, are popular with humans due to their anthropomorphic, humanlike features and their capability to perform autonomous movements, sensory-motor tasks, and verbal and non-verbal communications ( Zhang et al., 2019 ; Arora et al., 2022 ; Bertacchini et al., 2022 ). Social robotics researchers have defined ‘social robots’ in the HRI literature as ‘sociable’ (i.e., robots can be used as tools/aid for social cognition), ‘socially evocative’ (i.e., robots are anthropomorphic and evoke positive feelings in humans during human-robot interaction), ‘socially intelligent’ (i.e., robots portray social intelligence and exhibit models of social competence and human cognition), ‘socially situated’ (i.e., robots are intelligent beings and can distinguish objects and other social agents in their social space), and ‘socially interactive’ (i.e., robots can be utilized for peer-to-peer HRI for social interaction and interventions with humans) agents ( Mahdi et al., 2022 ; Newman et al., 2022 ; Roesler, 2023 ).

In the Springer Handbook of Robotics, social robots are defined as being ‘human-centric’ with the capabilities of operating in human-centered environments ( Breazeal et al., 2016 ). They can be humanoids or animal-like with a unifying feature of engaging “people in an interpersonal manner, communicating and coordinating their behavior with humans through verbal, non-verbal, and affective modalities” ( Breazeal et al., 2016 p. 1936).

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder characterized by abnormalities in social interaction and communication, and restricted, repetitive patterns of behavior, interests, or activities (DSM-5, 2013). Many students with ASD typically avoid direct physical contact, do not orient toward others, do not point to communicate, and do not display signs of happiness or interest ( Rutter, 2011 ). Some individuals with ASD require a high level of assistance in their daily lives, while others may function independently. ASD usually manifests before three and can last throughout a person’s life, though symptoms may improve with age ( Rutter, 2011 ; Rutter et al., 2011 ).

Our research aims to fill significant gaps in the robotics education literature in the context of HRI by focusing on how high school students diagnosed with ASD engage in instruction provided by educational-social robots. Many research studies are available for educational robotics with elementary and middle school students. Still, little research is available on high school ASD students’ motivation, cognition, and engagement with educational-social robots. Additionally, from robot design and curriculum development perspectives, there is a lack of expertise in creating a versatile methodology (e.g., Business Model Canvas or BMC) for robot design and curriculum development aimed at ASD students that can be validated through systematic investigation across educational research and industry applications. Even though many aspects of the BMC methodology, such as defining the system’s goals, participatory design, and conducting ethical research on children/adolescents are intuitive, no visual, structured, and standardized methodology like BMC is available to the HRI and ASD community. As a strategic management tool, BMC is used to develop new business models or improve existing ones. It considers key stakeholders, value propositions, infrastructure, customers, customer relationships, and finances. Such a tool can help bridge existing gaps in HRI research among robotic designers, roboticists, industry, academics, students (interacting with social-educational robots), parents, teachers, and counselors ( Arora et al., 2022 ).

This research makes three significant contributions. First, we develop an integrative ASD student–teacher (co-working with social robot)–social robotic interactions triad framework that considers the social context in which robots operate with ASD students and teachers co-working with social robots and robotic technology. We offer eight core research propositions that highlight avenues for research. Robotic interactions and collaborations between humans (ASD students and teachers co-working with robots to help students with ASD) and social robots help to design and make knowledge-based, social robots and robotic technology more relevant and effective. Second, we highlight the importance and relevance of the Business Model Canvas (BMC) framework, signifying the triad of ASD student–teacher (co-working with social robot)–social robots. We conducted a series of customer discovery interviews in high school contexts with ASD students and their teachers (co-working with robots to help ASD students) in a large metropolitan area and a federal district of the United States of America to illustrate the BMC framework’s relevance. Third, we will help connect the interdisciplinary fields of consumer behavior research, AI, social robotics, and human-robot interaction (HRI). Through this research, we wish to illustrate how the field of social robotics is helping to shape a sustainable future involving neurodivergent ASD individuals, which is far beyond the mere replacement of human workers.

In this research, we propose a conceptual framework through a business model canvas methodology and customer discovery interviews of key stakeholders engaged in social robotic interactions with ASD students. Our study aims to target the 2030 Sustainable Development Goals (SDGs) of the United Nations Organization, which were developed as an internationally agreed “plan of action for people, planet and prosperity,” especially item 3–Good health and wellbeing (ensuring healthy lives and wellbeing at all ages), and item 4–Quality education (ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all). In this investigation, we broaden the research focus by considering the combinational, complex dynamics of the Business Model Canvas (BMC incorporating the triad: ASD student–teacher (co-working with social robot)–social robotic interactions framework.

The rest of the paper is organized as follows. The upcoming sections focus on the literature review followed by business model canvas methodology and customer discovery interviews addressing previously mentioned research questions. After that, we focus on our conceptual framework: ASD student–teacher (co-working with social robot)–social robotic interactions triad framework leading to the development of research propositions. Lastly, we present conclusions, limitations, and future research directions.

2 Social robotics and autism: a review of literature

Social robots are proven to help both typically developing (TD) students and students with autism spectrum disorder (ASD) ( Chevallier et al., 2012 ), a neurodevelopmental disorder characterized by social communication impairments and abnormal (repetitive) behaviors ( DSM-5, 2013 ). For example, social robots can enhance engagement and motivation, promote personalized learning, and encourage STEM education for TD students ( Zhang et al., 2019 ; Arora et al., 2023 ). In contrast, for ASD students, they offer a safe and predictable environment for social interaction training, provide consistent and repetitive practice sessions, and can be customized to address individual needs, thus reducing overstimulation. These benefits, underscored by previous research (e.g., Chevallier et al., 2012 ; Belpaeme and Tanaka, 2021 ; Arora et al., 2023 ) highlight the versatility and effectiveness of social robots in educational settings, catering to the diverse needs of students across the spectrum of development. The development of ASD-specific social robots can be traced back to the seminal study by Emanuel and Weir (1976) , in which a computer-controlled electrotechnical device, a turtle-like robot (LOGO) moving on wheels around the floor, was used as a remedial tool for a student diagnosed with ASD. It was not until the late 1990s that numerous laboratories started investigating this topic (see Begum et al., 2016 ; Ismail et al., 2019 ; Leoste et al., 2022 ; Bertacchini et al., 2022 ; Soleiman et al., 2023 ; Bharatharaj et al., 2023 for reviews). In the current research, a ‘student diagnosed with ASD’ is referred to as an ‘ASD student.’

As stated earlier, ASD is a pervasive developmental disorder, and it affects social interaction, communication, and behavior development, impacting each person differently and to varying degrees of severity, as the word “spectrum” implies. ASD can appear in any order and range from mild to severe. In the social context of high schools, communication and engagement challenges can lead to social isolation and bullying for ASD adolescents ( Humphrey and Symes, 2010 ; Salhi et al., 2022 ). Adolescence offers an increasing self-awareness of social challenges for some students with autism, and negative encounters with peers can intensify social anxiety ( White et al., 2011 ). Healthy peer interactions have been shown to enhance positive social/academic results ( Lynch et al., 2013 ). Social issues are a significant obstacle to high school adolescents with ASD in achieving their scholastic goals ( Camarena and Sarigiani, 2009 ). Since most social encounters occur outside of the classroom, in the hallways, in school cafeterias, and during extracurricular activities, the more challenging aspects of social life for students with ASD (e.g., entering social circles, making friends, and cultivating intimate relationships, etc. ) may go unaddressed or overlooked by teachers and administrators.

Social Motivation Theory (SMT: Chevallier et al., 2012 ) highlights that ASD students usually prefer nonhuman and mechanical stimuli rather than seeking out or maintaining relationships with human partners ( Fosch-Villaronga and Heldeweg, 2018 ; Tavakoli, Carriere, and Torabi, 202; Burns et al., 2022 ). Social interaction challenges for ASD students stem from abnormal processing of social rewards, leading to decreased attention towards social cues early on. This diminished social focus then hinders the acquisition of social skills by limiting exposure to social learning experiences, consequently contributing to difficulties in social communication and interaction ( Chevallier et al., 2012 ; Fosch-Villaronga and Heldeweg, 2018 ; Tavakoli et al., 2020 ). SMT utilizes three socio-biological mechanisms targeting ASD students.

• • Robots represent “social agents” that can move in a three-dimensional space and physically interact with people and the environment through social orienting.
• • Adjustable sensory-cognitive stimulation can promote a more significant perceptive experience as a social reward than a simple video game.
• • A robotic system is perceived as an “artificially intelligent humanlike agent” that can simulate human behavior in social-affective development through social maintenance, guiding ASD students in the complex world of social interactions ( Chevallier et al., 2012 ; Burns et al., 2022 ).

Students with ASD show minimal activation of the brain’s reward system in response to social reinforcement, unlike their typically developing (TD) peers, for whom social interactions are inherently rewarding ( Chevallier et al., 2012 ). To simulate social interaction between humans, humanoid (anthropomorphic) robots should integrate the social motivation mechanisms of the human brain for an effective HRI ( Arora and Arora, 2020 ; Arora et al., 2022 ; Bertacchini et al., 2022 ; Leoste et al., 2022 ; Newman et al., 2022 ; Salhi et al., 2022 ; Bharatharaj et al., 2023 ; Soleiman et al., 2023 ). Given the student’s ASD characteristics, it appears worthwhile to investigate whether a social robot, with its motivational appeal, behavioral repetition, simplified appearance, and lack of social judgment, might appeal more to people with ASD than humans ( Bertacchini et al., 2022 ; Leoste et al., 2022 ; Salhi et al., 2022 ; Bharatharaj et al., 2023 ; Soleiman et al., 2023 ).

The increased popularity/interest in robotics education has raised questions about its performance and efficiency for students ( Chandra, 2014 ; Somyürek, 2015 ; Berry et al., 2016 ; Nemiro et al., 2017 ; Bertacchini et al., 2022 ; Leoste et al., 2022 ; Salhi et al., 2022 ; Bharatharaj et al., 2023 ; Soleiman et al., 2023 ). ASD students exhibit favorable outcomes when engaging with social robots during HRI field experiments, attributed to the social motivation theory of autism, highlighting the role of SMT in understanding the interactions between ASD individuals and robots ( Dubois-Sage et al., 2024 ). Some HRI benefits to these students include high levels of interest, elevated attention, high engagement, calm/active behaviors (with less repetitive behavior portrayals), and emotional response modification while being comfortably engaged in an activity/instruction provided by social robots ( Dautenhahn and Billard, 2002 ; Kozima, Nakagawa and Yasuda, 2005 ; Lee et al., 2012 ; Scassellati, Admoni and Matarić, 2012 ; Costescu et al., 2014 ). These robotic interventions and interactions result in positive learning environments for students diagnosed with ASD and other learning disorders and disabilities ( Zhang et al., 2019 ; Arora et al., 2022 ; Bertacchini et al., 2022 ; Leoste et al., 2022 ; Salhi et al., 2022 ; Bharatharaj et al., 2023 ; Soleiman et al., 2023 ).

The social motivation theory of autism (SMT: Chevallier et al., 2012 ) suggests that individuals with ASD may have impaired social motivation, affecting their social learning and interactions. We can connect this theory with the Business Model Canvas (BMC) methodology by understanding the social motivation challenges faced by individuals with ASD in the business context. The Business Model Canvas (BMC) is a strategic management tool/methodology that allows one to describe, design, challenge, invent, and pivot a business model.

Our research examines the use of business model canvas and customer discovery interviews to develop responsive robotics education for high school students with ASD. One of the research questions is: how can we use customer discovery interviews and the associated inquiry processes to develop responsive robotics education through the Business Model Canvas (BMC) to capture all stakeholders in the robotic intervention process with ASD students? We’ll address this in the following sections.

3 Research methodology

3.1 business model canvas (bmc) as a research methodology.

Business Model Canvas (BMC) is a research-based, industry-oriented framework highlighting key partners, key activities, and resources related to the research, value propositions, customer relationships, customer segments, and channels (as shown in Table 1 ). The BMC framework integrates user experience (UX) at its core, emphasizing UX best practices to develop responsive, ethical-educational-social robots that are commercially viable in HRI situations. As the HRI literature points out, “there is a lack of expertise in integrating and adapting UX best practices and defining UX goals in the context of HRI” ( Nielsen et al., 2021 , p. 266). The BMC seeks to address the gaps in the literature by providing a flexible, industry-oriented framework for developing and designing ethical robots or an ethical curriculum for educational-social robots. A business model canvas is developed to design ethical robots engaged in robotic interventions for high school and university students with learning disabilities (refer to Table 1 ).

Business model canvas (BMC) framework signifying the triad framework.

***Our Impact of this Current Research is not just on the K-12 School System but also on the Robotic Companies.

Table 1 highlights the key partners of the BMC Framework, including the Public School System (primarily middle and high schools), ASD students and teachers, and robotic companies. Our first value proposition is to increase the engagement of ASD students through social robotics. Our second value proposition is to increase robotic companies’ revenue through potential partnerships with K-12 schools. Our third value proposition is to help minimize the time for engagement of ASD students in schools and universities through our recommended technology/robotics. The potential impact would extend beyond robotics companies to the entire K-12 school system. The customer segments include public schools, ASD students and teachers, parents, associations, technology heads (as Influencers), and robotic companies (e.g., RobotLAB from San Francisco, CA) as economic buyers and partners.

Application of Business Model Canvas (BMC) in Human-Robot Interaction (HRI) Design. BMC methodology has been used in previous research related to robotics. Metelskaia et al. (2018) examined a specialized BMC for AI solutions in the context of robotics and AI. This framework is instrumental in aligning AI engineering, including HRI design, with broader business strategies. The study emphasized the importance of integrating technical development with market-oriented approaches, a highly applicable principle to HRI design ( Metelskaia et al., 2018 ). This research presented BMC as a useful tool for creating and analyzing robotic and AI solutions. Exploring the dynamic aspects of BMC, Romero et al. (2015) presented an enriched BMC design using system dynamics. This approach offered a more nuanced understanding of the complexities involved in HRI design, emphasizing the flow network and the potential for identifying and testing changes in the business model. This modified approach showed additional benefits that can be obtained with its application.

Zec et al. (2014) discussed the strengths and limitations of the BMC approach in collaborative environments. Their analysis provided insights into how software support can enhance collaborative design and evaluation of business models, a concept that can be extrapolated to collaborative HRI design processes. Bätz and Siegfried (2022) critically examined BMC’s use in entrepreneurial contexts, suggesting that it might oversimplify the multifaceted nature of business environments, such as those in robotics. This critique is crucial in assessing BMC’s applicability in the intersecting domains of technology, human interaction, and business goals. Joyce and Paquin (2016) introduced a triple-layered business model canvas, adding environmental and social layers to the traditional BMC. This extension is particularly relevant for HRI design, underscoring the need for sustainable and socially responsible robotics solutions.

Despite the above shortcomings, BMC methodology offers a viable and effective framework to understand the applicability of social robotics for students with ASD. Even though the research on BMC and HRI environments is limited, the application of BMC in HRI design offers a comprehensive framework for aligning robotic technology with strategic business objectives. Previous research highlights the versatility of BMC in addressing diverse aspects of HRI design, from enhancing learning environments to ensuring sustainability and social responsibility. The convergence of BMC and HRI design has the potential to pave the way for more integrated, effective, and responsible robotic solutions in various sectors. Our research directly applies BMC methodology aided by customer discovery interviews to develop responsive robotics education for high school students with ASD.

Business Model Canvas (BMC) and Social Motivation Theory of Autism (SMT). When considering the connection between SMT and BMC, it is important to integrate the understanding of social motivation challenges faced by ASD individuals into the various elements of the business model. For instance, in the customer segments and customer relationships sections, businesses can consider how to adapt their approaches to account for the social motivation difficulties of ASD individuals. This may involve creating inclusive and accessible customer experiences and communication strategies.

Furthermore, in the key activities and resources sections of the BMC framework, businesses can explore how to support employees with ASD by providing appropriate accommodations that consider their social motivation challenges. This may involve tailored training programs, workspace adjustments, and communication support. By integrating the principles of the SMT into BMC, businesses can work towards creating more inclusive environments for ASD individuals, thereby tapping into a potentially underutilized talent pool, and better serving a diverse customer base. For the value propositions section of BMC, we need to develop a deeper understanding of SMT that resonates with individuals with ASD, such as creating environments or products that are less overwhelming and more accommodating to sensory sensitivities. SMT can influence the choice of channels used to reach out to ASD individuals, opting for those that are more aligned with their social preferences and comfort zones. Adapting a business model to cater to ASD individuals might involve unique cost considerations. Still, it could also open up new revenue streams by tapping into an often underserved market.

3.2 Customer discovery interviews

Customer discovery interviews are a crucial component of the business model canvas (BMC) research methodology, particularly in the field of social robotics and human-robot interaction (HRI) ( Arora et al., 2023 ). These interviews involve engaging with potential customers to understand their needs, preferences, and problems (or pain points), which can then be used to inform the development of a business model. In the context of social robotics and HRI, customer discovery interviews can provide valuable insights into the specific use cases and applications of robots in various industries, such as hospitality and tourism ( Tung and Au, 2018 ; de Kervenoael et al., 2020 ). For example, Tung and Au’s (2018) study on consumer experiences with robotics in hospitality highlights the influence of robotic embodiment and human-oriented perceptions on consumer experiences, which can offer valuable insights for businesses in this sector. Similarly, de Kervenoael’s et al. (2020) work on visitors’ intentions to use social robots in hospitality services underscores the importance of perceived value, empathy, and information sharing in driving these intentions, providing further guidance for businesses in this field.

The BMC methodology incorporates insights gathered from customer discovery interviews, ensuring that user needs and preferences are considered during the design and development process ( Arora et al., 2023 ). These customer discovery insights can then be integrated into the BMC methodology to develop a sustainable and effective business model for social robotics and HRI. BMC’s visual representation (refer to Table 1 ) encourages collaboration among team members, providing a clear and concise representation of the social robot’s components and their interrelationships. BMC’s modular structure enables researchers and developers to easily modify and update different aspects of the social robot as new insights or technological advancements emerge ( Alves-Oliveira et al., 2022 ; Arora et al., 2023 ). By utilizing the BMC, researchers can create a visual representation of the various components that help in a successful social robot interaction and implementation, including customer segments, value propositions, channels, and revenue streams ( Arora et al., 2023 ). By combining customer discovery interviews with the BMC methodology, robotic creators, developers, and researchers can create social robots that are not only technologically advanced but also user-centric, maximizing user experience and ultimately leading to more successful and effective HRI implementation ( Alves-Oliveira et al., 2022 ).

We conducted two studies utilizing customer discovery interviews. Study one engages ASD students and their teachers, whereby a total of 25 customer discovery interviews were conducted. On the other hand, Study two involves other stakeholders from schools (e.g., school principals, technology heads, etc. ) in addition to the industry professionals from robotic companies, whereby a total of 35 customer discovery interviews were conducted. Study one is described below. Study two is described later, under Section 5.3 .

Study 1: Participants and Educational Settings . We conducted sixteen customer discovery interviews with ASD students from high schools after they interacted with social robots during HRI field experiments or social robotic intervention sessions. We also conducted nine interviews with their teachers, who had interacted with both robots and students. These interviews were conducted at three different public high schools of a large metropolitan federal district in the United States. We used Individualized Educational Plans (IEPs) to recruit students in consultation with the school counselors and teachers. A student’s IEP confirmed the recruited participant had a professional diagnosis of ASD and that the teachers interviewed were aware of the students’ ASD diagnosis. The high school students were 15–17 years old, with 11 males and five females. We used two kinds of social robots: NAO and Pepper. Both robots easily create an empathetic link with students, teachers, and researchers through their eye-catching appearances, moderate sizes, and humanoid behaviors. 1 Our research proposal, including its objectives, methodologies, and participant engagement strategies, was approved by the Institutional Review Board.

Procedure. Five sessions (1 hour each) were conducted using the social robots with 16 ASD high school students (see Supplementary Appendix S1 ). At the end of the session, researchers filled out an evaluation form with five variables (e.g., focused attention, following instructions, physical and verbal imitation, emotional response, and performance). Parents were informed of the study with their due consent taken before the study. The inclusion criteria for student selection were: (a) high school students diagnosed with ASD, (b) between 15 and 17 years of age, (c) obtained ‘informed consent’ signed by their parents, and (d) selected by high school counselors for HRI experiments with social robots according to their respective IEPs. The exclusion criteria were: (a) high school students who did not meet the age criteria (15–17 years of age), (b) did not obtain ‘informed consent’ from their parents, and (c) students with hearing, speech, and vision deficits, with abnormal eye movements and comorbidities such as Fragile X Syndrome or Down’s Syndrome, and/or students diagnosed with other learning disorders.

Supplementary Appendix S1 provides information on the demographics of ASD students and teachers, and Supplementary Appendix S2 provides more detail about the interview procedure and questions. Both sets of interviews included questions dealing with task accomplishment according to curriculum development for social-emotional skills targeting ASD students, and the interpersonal/people dimension of the task focused on social-emotional skills development. Multiple robotic intervention sessions were conducted with these 16 ASD students. The curriculum-related, educational-ethical robotic intervention scenarios focused on social-emotional learning (SEL) skills--comfort zone, conflict resolution, and job search -- were developed as a part of the current research. These newly developed curriculum-related robotic intervention scenarios include:

• • Comfort Zone: This human-robot activity introduces humans (ASD individuals) to the concept of a comfort zone through the social robot and explains its benefit to the ASD individual.
• • Conflict Resolution: This human-robot activity explores the skill of conflict resolution, such that it involves helping someone resolve a conflict within themselves or between others: communicate, compromise, ask for help, apologize, and write a Pro and Con list through the social robot.
• • Job Search: This activity explores the skill of job search (explained to the human/ASD individual through the robot) and making use of available job opportunities: talking with others, business signs, community support, the internet, and/or newspapers.

Lessons on moral values and ethics were integrated for the ASD students in each SEL skill. These human-robot activities were developed as case-based, ethical curriculum-related robotic intervention scenarios and individual lesson plans for students with ASD and other learning disabilities 2 (see Arora et al., 2022 ).

Interviews have been used in previous research across disciplines as a robust method for formulating research propositions. Specifically, van Doorn et al. (2023) leverage the power of qualitative interviews to explore significant relationships at the intersection of consumers, autonomous technology, and workers. This approach is pivotal in developing their Consumer–Autonomous Technology–Worker (CAW) framework, which sheds light on the evolving landscape of organizational frontlines in the digital age. By engaging directly with workers co-working with robots and consumers interacting with these human-robot teams, van Doorn et al. (2023) gather rich, contextually nuanced insights that are critical for framing their research propositions.

All interviews were transcribed by the researchers to ensure that the rich, qualitative data contained within could be analyzed. Our methodology was informed by the principles and practices suggested by van Doorn et al. (2023) , tailored to explore the specific nuances and dynamics observed in HRI settings. Our analytical process involved a detailed examination of the transcriptions to identify key patterns, behaviors, and insights related to the interaction between students and social robots. This involved an iterative process of coding the data (first order codes, second order codes, and aggregate dimension) as per Gioia approach ( Gioia et al., 2013 ), discussing emergent patterns among the research team, and refining our understanding of the data considering the broader literature and the specific objectives of our research. In Table 2 , we provide an example of our coding.

Coding example ( Gioia et al., 2013 ; van Doorn et al., 2023 ).

In line with the approach taken by van Doorn et al. (2023) , we use customer discovery interviews in our study to develop impactful research propositions. By engaging in direct conversations with students, teachers, and robotic professionals from the industry, we gain access to their lived experiences and insights, which are crucial for grounding our research propositions in real-world contexts. This approach aligns with the qualitative research tradition, where the depth and richness of data gathered through interviews offer a robust foundation for developing meaningful and relevant research propositions. Thus, the methodology employed in our study, inspired by van Doorn et al.’s (2023) work, stands on solid academic ground, demonstrating that customer discovery interviews are powerful instruments for generating deep insights essential for scholarly research.

4 Conceptual framework: ASD student–teacher–robot triad framework

A social robot can be designed along a spectrum of autonomy—ranging from non-autonomous or Wizard-of-Oz designed, semi-autonomous, to fully autonomous. Autonomous technology is defined as “machines capable of performing actions without (or with minimal) human intervention that can change their behavior in response to unanticipated events ( Watson and Scheidt, 2005 ) … developed remarkably over recent decades and has become a top priority of both researchers and managers” ( van Doorn et al., 2023 , p. 2). Much research is available on consumer-facing AT (e.g., chatbots or digital voice assistants such as Alexa and Siri) that help consumers select the right goods and services ( Guha et al., 2021 ). In this research, embodied robots (e.g., Pepper) guiding consumers in a store, a building, or school premises are considered consumer-facing AT. Employee (or worker)-facing AT can be considered a medical AI assisting hospital doctors ( Longoni et al., 2019 ). In our research scenario, teachers working and collaborating with NAO and Pepper robots to teach ASD students can be considered employee-facing AT.

Utilizing van Doorn et al.’s (2023) framework for consumer-facing AT and worker-facing AT, we propose our ASD student–teacher (co-working with social robot)–social robot triad framework (refer to Figure 1 ) with eight core research propositions. Figure 1 illustrates the relationships between ASD students and their teachers and how these relations change when social robots are integrated into curriculum planning for ASD students. We acknowledge that reality may be more complex than these portrayed relationships between ASD students and their teachers.

Relationships within the Triad [ASD Student–Teacher (co-working with social robot)–Social Robot] Framework (Adapted from van Doorn et al., 2023 ).

In each quadrant of Figure 1 , we provide anecdotal evidence from a series of interviews conducted during human-robot interaction (HRI) field experiments or social robotic intervention sessions between the robots and the ASD students. Both ASD students’ and teachers’ interviews focused on social robotic interventions (or HRI field experiments). They emphasized the need for collaboration between the teacher and their ASD student in a way that the ASD student–teacher (co-working with social robot)–social robot triad is considered as a whole.

Utilizing the Business Model Canvas (BMC) framework to signify the triad of ASD student–teacher (co-working with social robot)–social robot (as seen in Figure 1 ), we develop eight research propositions in the next section. We start by focusing on ASD student–social robot and teacher (co-working with social robot)–social robot dyads. Each quadrant of the ASD student–teacher (co-working with social robot)–social robot triad framework ( Figure 1 ) is explained in detail in the following section. Thereafter, we bring the three actors together in the triad framework by offering eight core research propositions.

5 Development of research propositions

5.1 teacher–student relationship through the social robot: how does the presence of a ‘social robot’ change (a) the way asd students relate to their teachers, and (b) the way teachers interact and relate to asd students.

This sub-section focuses on the top-left and bottom-left quadrants of the ASD student–teacher (co-working with social robot)–social robot triad framework (see Figure 1 ). Top-left quadrant focuses on the relationship between ASD student and teacher (co-working with robot) through the presence and use of a social robot. The presence of a social robot helps both ASD students and their teachers focus on social-emotional skills and provides more time for teachers and counselors to engage in interaction, feedback, and future curriculum planning. Social robots can help ASD students engage with the learning material by providing a novel and interactive learning experience ( Belpaeme and Tanaka, 2021 ). This aspect can be particularly beneficial for students with ASD, who may struggle with traditional teaching methods, and indicates that social robots can play a supportive role in strengthening the ASD student-teacher relationship in the context of ASD education (refer to the top-left quadrant of Figure 1 ).

The bottom-left quadrant focuses on the relationship between teacher (co-working with robot) and ASD student through the presence and use of a social robot. When ASD students are exposed to social robotic interactions through their teachers, it may affect how they relate to their teachers and how their teachers interact and relate to the students. Teachers may frequently switch between different perspectives when interacting with social robots, such as viewing the robot as a didactic tool or a social actor ( Ekström and Pareto, 2022 ). This flexibility could potentially help teachers adapt their teaching strategies to support ASD students better. Teachers may try to create an inclusive approach, encourage collaboration, and establish mutual trust between the actors in their assigned roles ( Ekström and Pareto, 2022 ). This could lead to a more supportive and inclusive learning environment for ASD students created by their teachers through social robots (refer to the bottom-left quadrant of Figure 1 ).

Social-educational robotics is an innovative and viable platform for:

• • teaching and learning science, technology, engineering, and mathematics (STEM) and STEM-related curricula across diverse disciplines;
• • developing broad learning skills such as scientific inquiry, engineering design, problem-solving, creative thinking, and teamwork; and
• • fostering students’ motivation to engage in science and technology while reducing psychological and cultural barriers for minority students from underprivileged communities.

Robotics education can drive students to behave as co-constructors of learning rather than passive knowledge receivers or technology consumers. Broadening involvement in STEM education is essential for providing equitable learning opportunities for students with varying needs and diverse backgrounds ( Lee and Buxton, 2010 ; Bellas and Sousa, 2023 ). Social-educational robots may serve as social mediators, encouraging prosocial behaviors in interactions with individuals. These behaviors include orienting the eyes and head, initiating physical contact, and pointing to shared interests ( Dautenhahn et al., 2003 ; Diehl et al., 2012 ). Thus, our central hypothesis revolves around applying the social motivation theory of ASD to social-educational robotics. Our research focuses on students diagnosed with ASD, referred to as ASD students. It aims to understand how these ASD individuals positively react to sensory rewards delivered by a social robot, indicating their interest and satisfaction exposed to these stimuli ( Kostrubiec and Kruck, 2020 ; Bellas and Sousa, 2023 ).

During our interviews with ASD students, we found that students enjoyed interacting with robots more than their teachers or counselors. However, when teachers incorporated social robots into the classroom curriculum, ASD students enjoyed interacting with their teachers and the robots. One teacher interviewee articulated this by stating that “ While social robots take care of the routine task (lesson plan) accomplishment of teaching social, emotional skills (e.g., comfort zones, conflict resolution, preparing for college and job applications, etc.), teachers have extra time to focus on an individual student’s progress and growth, thus improving their relationships with their students. ” Consequently, we propose the following.

Research Proposition 1: Teachers are more likely to forge stronger relationships with ASD students when social robots focus on routine tasks (e.g., teaching lesson plans), and teachers focus on creative, relational tasks, leading to overall student development and growth.

While interviewing teachers and ASD students, it was evident that some teachers had concerns about robots replacing them ( Bellas and Sousa, 2023 ). Not all teachers are comfortable working with robots; some need training to collaborate with robots and students effectively. However, when teachers overcome their fears and view robots as valuable aids rather than their replacements, they tend to relate better with their students. One teacher interviewee emphasized this by saying, “ When my student interacts with NAO or Pepper directly, of course, s/he enjoys the interaction. However, when I bring the robot with me, I find my students happier than them enjoying the interaction without me. ” Thus, we have the following research proposition.

Research Proposition 2: Teachers are more likely to forge weaker relationships with their ASD students when teachers (a) are not provided with adequate training to work and collaborate with robots, and (b) they fear robots replacing them in their jobs.

5.2 Student-Teacher Relationship through the Social Robot: How does the presence of (a) teachers collaborating with social robots affect the way ASD students relate to their teachers and robots, and (b) ASD students interacting with social robots change the way teachers relate to their ASD students and robots?

In this sub-section, we focus on the top right and bottom right quadrants of the ASD student–teacher (co-working with social robot)–social robot triad framework (see Figure 1 ). The top-right quadrant focuses on the relationship between an ASD student and a social robot through the presence of a teacher (co-working with robot). When a teacher is present alongside the social robot, the focus is on how the teacher engages the ASD student in various activities and learning scenarios through the social robot. Teachers can provide personalized and engaging experiences that cater to the specific needs of ASD students. They can offer a range of stimuli that help ASD students improve their social interaction, communication skills, and emotional recognition. The social robot serves as a consistent and predictable assistant to the teacher, which can be especially comforting for ASD students, who often prefer structured and routine interactions. This quadrant highlights the potential of social robots as complementary tools for facilitating learning and social interaction in ASD students ( Belpaeme and Tanaka, 2021 ).

The bottom-right quadrant of the ASD student–teacher (co-working with social robot)–social robot triad framework (see Figure 1 ) explores the relationship between the teacher (co-working with robot) and the social robot when the ASD student is present. During robotic interventions with ASD students, the teacher (co-working with social robot) utilizes the robot as a teaching aid, and the subsequent collaboration aids and influences the educational process. Teachers can leverage the capabilities of social robots to enhance their teaching methods, using them as tools to demonstrate concepts or as interactive elements that add novelty and engagement to lessons. Social robots also allow teachers to observe and understand how ASD students interact with technology, providing valuable insights that can be used to tailor educational approaches ( Ekström and Pareto, 2022 ). This quadrant underscores the collaborative potential between human educators and robotic technologies in creating a more effective and inclusive educational environment for ASD students.

Social-educational robotics has proven to be successful with ASD students. However, the potential of robotics in teaching has been debated with little regard for different types of students ( Alimisis, 2013 ). Previous research focused on what robotics concepts and skills ASD and typically developing (TD) students can learn (e.g., Bers et al., 2014 ; Atmatzidou and Demetriadis, 2016 ) rather than how they learn. When teachers use social robots by focusing on student learning outcomes, detailed descriptions of robotic kits and curricula for instructional approaches receive much attention. Still, there is insufficient explanation for how different students interacted with the robots and participated in the activities. According to Johnson (2003) , “the universality of the robotics phenomenon” (p. 16) implies that robotics education is effective for all students regardless of their unique learning styles and diverse backgrounds. It is critical to investigate how children with varying needs and abilities engage in robotics to develop and implement responsive educational programs ( Alimisis, 2013 ; Apraiz et al., 2023 ) to meet their needs.

We interviewed the educational technology head and four of their associates for the entire public school system. We inquired about the technology assistance for students with learning disabilities. Evidently, the school system grants autonomy to individual schools to make their own technology choices. Assistive Technology is utilized through computers and/or gaming targeted at students with learning disabilities. Ensuring the safety of technology and safeguarding the privacy of students’ data is of utmost importance when engaging students with different learning disorders. In fact, the public schools’ system provides autonomous robotic technology (Sphero robots–please see Figure 2 ) to be used by each system school for all students. Additionally, special education teachers and counselors can request a set of 20 Sphero robots for their classrooms free of cost.

Sphero Robots (autonomous technology) in classrooms.

One of the ASD student interviewees (Interviewee #3) mentioned: “ I wait to meet my NAO on a regular basis. Initially, there were university professors who introduced us to NAO. Now, when my teacher uses NAO and brings it with her, I feel very happy. I feel that robots are our common interests. ” Students enjoy the overall learning experience with social robots when it is facilitated by their teachers. Thus, we have the following research proposition.

Research Proposition 3: ASD students are more likely to forge stronger relationships with their teachers when teachers co-work and collaborate with social robots for teaching purposes due to shared humanness, interests, and engagement.

However, in a different situation, if the teacher relies heavily on social robots without involving herself/himself in the class and engaging students, students do not enjoy the overall learning experience. One student interviewee (Interviewee #6) said, “ My teacher brings in the robot and then leaves. University researchers make us interact with the robot, but I miss my teacher’s voice. I wish she can be present when the robot interacts with us. ” Another mentioned: “ It was my first time interacting with Pepper, and my teacher left me with Pepper and other university professors/researchers. I was intimidated and immediately left the room. ” Thus, we have the following research proposition.

Research Proposition 4: ASD students are more likely to have weaker relationships with their teachers when student-robot interaction is (a) not mediated by their teachers, and (b) when teachers are not a part of the overall social robotic intervention process between ASD students and robots.

5.3 Social robots’ and robotic companies’ perspectives

Having examined the perspectives of teachers co-working with robots and ASD students interacting with social robots in the above sections, we now focus on the robotic companies’ perspectives. Critics of educational-social robotics have argued that emotional bonding created between humans and anthropomorphic robots can make people vulnerable to emotional manipulation ( Zhang et al., 2019 ; Arora, Arora, Jentjens et al., 2022 ; Arora et al., 2022 ; Sammonds et al., 2022 ; Yepez et al., 2022 ; Apraiz et al., 2023 ; Roesler, 2023 ) and can create ethical challenges. Regulation and ethics are interrelated and are essential to regulate robotic frameworks.

Earlier standards in robotics technology separated robots from human operators for safety concerns through the European legislative via the Machine Directive 2006/42/EC, ISO 10218 (robots and robotic devices, and safety requirements for industrial robots Part 1 and 2), among other laws ( Danks and London, 2017 ; Apraiz et al., 2023 ). European harmonized standards do not cover robots in educational-social spheres, autonomous vehicles, and/or additive manufacturing. Only industrial robots are a part of these standards/laws.

ISO 13482:2014 standard focuses on human-robot interaction situations of voice-controlled robotic wheelchairs, exoskeletons, and other social robots, where minimum safety requirements of HRI are defined in terms of design factors dealing with, but not limited to, robot shape, robot storage, robot motion, and other design considerations ( Danks and London, 2017 ). Since there is a lack of standardization in incorporating safety laws and standards in robot design worldwide, there is a growing potential for developing these standards for educational-social and collaborative robotics, including service, healthcare, medical, personal care, and/or therapeutic robotics. Some of these standards deal with moral hazards associated with robots.

For example, the British Standard BS 8611:2016 on Robots and Robotic Devices enables roboticists to perform an ethical risk assessment of artificial agents. The USA-based standard, IEEE Ethically Aligned Design, mandates that roboticists and engineers should be empowered to take control of ethical design considerations in the development of robots. During the customer discovery interviews, a robotics company professional interviewee mentioned: “We have always wanted to design better ethical robots by working directly with high schools and university researchers. We want to make our robots HIPAA and FERPA compliant for use by vulnerable populations, e.g., ASD students at high schools.” Thus, we have the following research proposition.

Research Proposition 5: Robot-based ethical interactive intervention scenarios based on school curricula will enhance learning by ASD students.

Through the customer discovery interviews with 16 ASD students and nine teachers, we analyzed the curriculum-related, educational-ethical robotic intervention scenarios. These scenarios focused on social-emotional learning (SEL) skills, including comfort zone, conflict resolution, and job search. Pictures and videos of multiple robotic intervention sessions with some of these high school students can be found at: https://photos.app.goo.gl/9EXAW9fBfdkG5Ca5A . 13 out of 16 ASD students showed interest in interacting with robots through three lesson plans–comfort zone, conflict resolution, and job search skills. All the human-robot activities or SEL skills developed as three robotic intervention scenarios were completed in approximately 30–45-min sessions over five instances each.

ASD students with high cognitive and low social skills addressed the robot as ‘he’ or a ‘human companion.’ Conversely, ASD students with high social and low cognitive skills addressed the robot as ‘it’ or a ‘technology/tool.’ These interesting findings relate to the fact that ASD students learn by focusing on skills they lack more than the skills they possess. ASD students lacking social skills found robots to be their companions, while ASD students lacking cognitive skills found robots to be their tutors or technology tools for education. Analyzing the overall performance, we found that most ASD students understood the concepts associated with SEL skills through robotic interventions. Thus, we have the following research proposition.

Research Proposition 6: ASD students with high cognitive and low social skills are more likely to address the robot as a ‘human’ companion. In contrast, ASD students with low cognitive and high social skills are more likely to address the robot as ‘it’ or a ‘technology/tool.’

5.4 Other stakeholders’ perspectives

Study 2: Participants and Educational Settings . In a separate study, we conducted thirty-five (35) more customer discovery interviews with schools and robotic companies. We interviewed principals, special education counselors, technology heads, and PTAs (parent-teacher associations) at three high schools in the public school system (a total of 20 interviews). Further, we interviewed 15 robotics company professionals.

Procedure . Supplementary Appendix S3 includes the interview questions from 35 customer discovery interviews (20 interviews with school professionals and 15 interviews with robotics company professionals) using BMC methodology. In response to the customer discovery interviews conducted at schools, a well-known middle school in the public school system (with a high focus on education and technology) reported learning disabilities and disorders (e.g., anxiety, autism spectrum disorder or ASD, attention deficit hyperactivity disorder or ADHD, learning disabilities linked to diabetes or physical medical condition) for about 20% students (259 out of 1,524 students). At least six customer discovery interviews were conducted with different stakeholders at the school: the principal, PTA president, technology head, and two special needs counselors. ASD was identified as a critical issue, and communication and educational support (CES) services were identified and provided. Two special education programs, 14 special education counselors, three social workers (one per grade level from sixth to eighth grades), and a behavioral analyst worked closely with special needs students. The school had a good focus on technology, and Assistive Technology was already in use for ASD students. Reader Pens (which read to students through iPads) and special hi-tech chairs/furniture were in use. The school system has a policy of ‘laptops for every student,’ and the educational system was found to be technology savvy (having funds/resources to use for learning and assistive technology through federal government initiatives/programs).

A highly ranked high school was selected to conduct six customer discovery interviews with school stakeholders. Of the 1800+ students, about 200 were identified with special needs and learning disabilities (Autism Spectrum with a combination of high/low social/cognitive skills). The school was using a robotic arm for educational purposes. A science teacher organized a gaming club (supported by donations from local businesses). As a part of the gaming club activities, ASD and typically developing (TD) students built gaming computers, conducted gaming activities, worked on flight simulators, and used Xbox for gaming focused on cyber security. We conducted four customer discovery interviews with the principal, technology head, and special educational counselors at another (smaller) high school with 110 students in the Engineering program. Of those students, 20–25 students were identified with learning disabilities. The school had two special needs teachers or counselors. Assistive Technology was utilized through computers.

Through the above 20 customer discovery interviews with school administrators, teachers, parents, and technology heads, we discussed the short-term and long-term impacts of human-robot interaction on ASD students, and the potential pitfalls of over-exposure, over-engagement, and over-attachment with ASD students. We received consensus about the teachers’ role and engagement in the overall social robotic intervention process with ASD students. One of the parent interviewees stated: “ I think teachers do a fabulous job in avoiding any potential negative effects of ASD students indulging with social robots. ” In another instance, a high school principal noted: “ A teacher’s presence in the classroom ensures that technology is not seen as intrusive and there is no over-indulgence with social robots. ” Teachers were found to be effective in avoiding any potentially adverse effects on ASD students indulging in social robots. Teacher engagement and collaboration with social robots help in successful human-robot interaction (HRI) implementation over the long term. Thus, we have the following proposition.

Research Proposition 7: Teacher (co-working with the robot) during student-robot interaction (a) helps ASD students relate more to the social robot in the short term, and (b) decreases any potential over-attachment or over-involvement (or other potential negative consequences) with the social robot over the long term.

Technology heads at schools believe that technology helps all students, but it should be safe, secure, and ethical (with privacy considerations) when engaging students with ASD and other learning disabilities. Customer discovery interviews with 15 robotic company professionals/roboticists confirm these findings. Robotic companies were keen to work with the public school system. They were open to using academic support, especially where academic researchers can act as ‘mediators’ between schools and robotic companies for designing ethical curricula for students with learning disabilities. The robotic companies were focused on HIPAA and FERPA compliances for helping students with ASD and learning disabilities. The technology head of the school system stated: “ We need to build safeguards with robotic technology. Technology (in any form) should be safe, secure, and ethical (with privacy considerations), especially while engaging students with ASD and other learning disabilities. ” School technology heads and robotic companies were happy to integrate security and privacy considerations in the robots and robotic systems through web-enabled platforms. Thus, we have the following research proposition.

Research Proposition 8: Ethical technological interactions will lead to better (enhanced) learning for ASD students with learning disabilities.

Table 3 highlights our eight core research propositions. Future research must empirically test these research propositions, highlighting the ASD student–teacher (co-working with social robot)–social robotic interactions triad framework. The propositions deal with the following:

• • How teachers co-working with robots can forge stronger relationships with their ASD students;
• • How ASD students can forge stronger or weaker relationships with their teachers;
• • How social robots can impact the social and cognitive skills of ASD students through robotic interactions and interventions; and
• • How can stakeholders (other than students and teachers) impact the overall triad framework?

Research propositions within the ASD student–teacher (co-working with social robot)–social robotic interactions triad framework.

6 Discussion

6.1 implications of research.

The human factor plays a significant role in a successful ASD student-social robot interaction mediated by a teacher’s presence. This research draws a parallel between ASD student-social robot interaction and van Doorn et al.’s (2023) research highlighting autonomous technology interaction with frontline workers and consumers, examining consumer-AT and worker-AT dyads. Our current research explored the ASD student–teacher–social robot interactions triad framework by considering the social context in which robots operate with ASD students and teachers co-collaborating with social robots and robotic technology. Building on previous literature and customer discovery interviews derived from the business model canvas (BMC) and social motivation theory of autism, we provided eight core research propositions highlighting avenues for research in the triad framework. Robotic interactions and collaborations between humans (ASD students and teachers co-working with robots to help students with ASD) and social robots help in the education (service) sector by bridging the fields of education, artificial intelligence (AI), human-robot interaction (HRI), and consumer behavior. The complex interactions between humans (ASD students, teachers) and social robots need to be studied simultaneously to understand the utilization of social robotics in the education sector. Some industry examples that could potentially work with teachers and ASD students, based on the ASD student–teacher–social robot interactions triad framework are as follows:

• • Education Technology (EdTech) Companies: These companies develop and provide tools and platforms for educational purposes, including those that can be adapted for students with ASD. They may collaborate with teachers to create tailored solutions that address the unique needs of ASD students. Examples of such companies include Coursera, Blackboard, and 2U.
• • Robotics Companies: Companies that specialize in developing social robots can work with teachers and ASD students to create robotic interactions that help students focus on social-emotional skills and provide more time for teachers to plan and provide feedback. Examples include iRobot, Boston Dynamics, and SoftBank Robotics.
• • Special Education Service Providers: These organizations offer specialized services and support for students with ASD. They may collaborate with teachers to integrate social robots into their educational programs, helping students improve their social skills and engagement. Some examples are the May Institute, the Center for Autism and Related Disorders (CARD), and the Autism Society of America.
• • Research Institutions: Universities and research centers may conduct studies and develop new technologies to enhance the education of students with ASD. They can collaborate with teachers to understand the impact of social robots on ASD students’ learning experiences and develop effective interventions.

We highlighted the relevance of the Business Model Canvas (BMC) framework, signifying the triad: ASD student–teacher (co-working with social robot)–social robots. We also conducted a series of customer discovery interviews in high schools with ASD students along with their teachers/counselors (co-working with robots to help ASD students), parents, technology heads, and robotic company professionals. Through this research, we illustrate how the field of social robotics is helping to shape a sustainable future involving neurodivergent ASD individuals, which is far beyond the mere replacement of human workers. While robotic anthropomorphism has been studied extensively, we predicted that its negative impact of over-involvement can be reduced by the presence of a human (i.e., teacher during HRI).

Through the interdisciplinary fields of consumer behavior research, AI, social robotics, and human-robot interaction (HRI), we illustrated the relevance of social robotics and how it changes the relationships between the various actors depending on a series of factors. Division of labor between social robot and teacher ensures a successful HRI for ASD students whereby technology (i.e., robot) augments HRI instead of replacing the teacher ( Tsai et al., 2022 ; Engwall et al., 2023 ; van Doorn et al., 2023 ). Human leadership and human factors through the presence of a teacher who is comfortable collaborating with the robot help strengthen the HRI impact in the short term for the ASD student and avoid the potential pitfalls of over-exposure and over-attachment of robots with ASD students. This aligns with the social presence theory ( He et al., 2012 ).

Our research is the first to integrate the research domains of social robotics and human-robot interaction (HRI), the BMC framework, and learning and education (as depicted in Figure 3 ). Through the current research, we aimed to: (a) develop responsive robotics education through the Business Model Canvas (BMC) to engage all stakeholders in the robotic interventions process with ASD students, (b) create the ASD student–teacher–social robot interactions triad framework by conducting HRI field experiments with ASD students in public schools, employing the BMC, and customer discovery process, and (c) investigate how educational-social robotic interventions, specifically involving humanoid robots, contribute to the progress of high school students diagnosed with ASD and other learning/cognitive disabilities. The research involved the active participation of various stakeholders such as ASD students, teachers collaborating with robots, parents, school technology heads, and robotics company professionals.

Current research: Intersection of social robotics and HRI, BMC, and learning and education.

6.2 Future research directions

As previously mentioned, our research aims to address two of the 2030 Sustainable Development Goals (SDGs): SDG three focuses on good health and wellbeing (ensuring healthy lives and wellbeing at all ages), and SDG 4 centers on ensuring inclusive and equitable quality education as well as promoting lifelong learning opportunities for all. There is a significant lack of awareness and understanding regarding the SDGs within the robotics community and among decision-makers. This knowledge gap is an obstacle to leveraging the contributions of robotics and AI towards achieving the SDGs ( Mai et al., 2022 ). To overcome this challenge, future researchers should prioritize the integration of the SDGs with robotics. Additionally, there should be a stronger emphasis on interdisciplinary, human-centered, systemic thinking to highlight the benefits and relevance of social robots and robotic interventions in the context of the SDGs ( Mai et al., 2022 ).

We acknowledge the modest sample size utilized in our study given the constraints of the novel nature of our research question, the absence of prior research in this domain, and the contextualization of our in-depth customer discovery interviews to a specific field of social robotics and HRI. Despite addressing a timely and relevant issue related to HRI, due to the modest sample size, we note that our findings must be considered as preliminary and not extrapolated beyond our research setting. However, our findings do provide some valuable insights that advance both our knowledge and practice. Furthermore, our results serve as a strong foundation for subsequent research employing larger sample sizes and examining diverse application scenarios. Future research efforts can further validate our findings and delineate boundary conditions governing our findings.

Preparing ASD students for the future is a challenging endeavor. Schools and universities are working with ASD students; however, the current effort is insufficient ( Engwall et al., 2023 ). To address this gap, there is a growing need for more technological support (through robotics) to facilitate the development of SEL and other essential life skills like critical thinking, problem-solving, decision-making, and creative solutions. Integrating these SEL and life skills into our current educational landscape is a complex undertaking. Such integration may be achieved through HRI field experiments and building curriculum-related robotic intervention scenarios focused on life skills needed to excel in the future.

Furthermore, it is unclear whether social robots forge stronger or weaker ASD student-teacher relationships. Jackson et al. (2020) predicted stronger relationships between humans and robots where interhuman differences based on race and religion are not relevant. On the other hand, some studies show weakened interhuman relationships because humans (i.e., teachers collaborating with robots) can be potentially dehumanized ( Herak et al., 2020 ). Future researchers should investigate the ASD student–teacher–social robot interactions triad framework provided in our study and its implications for the relationships involved.

One of the major limitations of our study is that in our ASD student–teacher–social robot interactions triad framework. We primarily examined interactions between ASD students and social robots, as well as between teachers and social robots, in individual settings. We did not explore group dynamics of decision-making within these interactions. Future researchers should investigate a broader range of research contexts.

In our research, we utilized social robots deployed by the school system (i.e., business context). However, it is important to acknowledge that in different settings, such as when robots are deployed directly by families of ASD students (a consumer context), the outcome may be different. Further, our research focused on external stakeholders. Future researchers can concentrate on robotic companies and their influence on the education sector to further advance the research enterprise. Similarly, future research should delineate the usage of social robots for neurodivergent and neurotypical employees in organizations and how social robots can impact human capital and corporate culture ( van Doorn et al., 2023 ). Furthermore, we did not investigate our framework for its relevance to robotic companies’ suppliers, competitors, and policymakers. Future research may explore complex configurations of our ASD student–teacher–social robot interactions triad framework in diverse research contexts for different stakeholders.

We hope that our research propositions hold promise for advancing research and practice in social robotics and HRI domain, and using robotic technology to address learning disabilities in the digital age. Such progress is both timely and relevant to create a positive impact on society.

Acknowledgments

The authors thank the robotic companies - Movia Robotics and RobotLAB–for their support of the research.

Funding Statement

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study received funding from the National Science Foundation (NSF) Research Initiation Award (RIA) #2100934 - Social Motivation Approach for Rehabilitation Through Educational Robotics (SMARTER) research, and the Center for International Business Education and Research (CIBER) at the Georgia Institute of Technology, Atlanta, GA, United States.

1 https://www.aldebaran.com/en/pepper-and-nao-robots-education

2 https://www.udc.edu/sbpa/lit/social-robotics-research/

Data availability statement

Ethics statement.

The studies involving humans were approved by IRB Reference# 1648673-1 for NSF-Funded Social-Educational Robotics Research. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

AA: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing–original draft, Writing–review and editing. AmA: Data curation, Investigation, Methodology, Supervision, Validation, Writing–original draft, Writing–review and editing. KS: Conceptualization, Project administration, Validation, Writing–original draft, Writing–review and editing. JMcI: Data curation, Funding acquisition, Investigation, Resources, Writing–review and editing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frobt.2024.1328467/full#supplementary-material

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More From Forbes

How the robotics business model is shifting to meet manufacturers where they’re at.

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When I was a kid, the idea of a real-life robot felt foreign. Earth was for humans. The Jetsons and their robot maid, Rosie, existed only on television.

Now I work in manufacturing and frequently find myself advising companies that, you know that job you can’t find the right person to fill? That process you haven’t perfected? For that role, perhaps you should consider something a bit more metal.

The pushback, and there is plenty, tends to sound a lot alike. It’s too expensive. Or, it takes too long to set up. Or, we just don’t have the tech skills to handle that sort of thing.

But what if I told you that you don’t need any special skills. That there are folks who will do the setup—and even the maintenance—for you. That it may not be as pricey as you think, particularly when you weigh a robot’s productivity against a human being— or beings — in the same role.

For manufacturers, the fear of robots is real. Manufacturers fear their investments won’t amount to anything, that they’ll be bogged down by maintenance and end up even worse off.

But I say fear not, because these days there are business models that cater to small- and medium-sized manufacturers who don’t have the in-house staff to manage robots on their own.

Make Technology More Affordable with Robotics as a Service

One such model is used by Rapid Robotics, a San Francisco-based company that provides robotics as a service. CEO and co-founder Jordan Kretchmer saw the high costs falling on manufacturers installing robots. Industrial robots could cost anywhere from $40,000 to $500,000, the company says. Even with rent-a-robot programs popping up to lower the hardware burden, manufacturers were stuck with implementation costs often soaring into the six-figures.

As an alternative, Rapid provides both ends of that equation — the physical robot and the implementation, plus ongoing maintenance and reprogramming — at a flat, monthly fee. Their low-risk model shields customers from high startup costs and ensures they’ll be able to adjust and recalibrate the robot as needs evolve. That happens often when it comes to things like injection mold operations, CNC loading, palletizing, packing and labeling boxes, and other tasks that are otherwise ripe for automation. “Those tasks tend to change over a lot,” Kretchmer says. “Meaning if you automate it with a very specific kind of machine and a very specifically programmed robot, the chances that the operation is going to remain the same for the next two or three years is really, really slim these days.”

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Rapid’s service is not necessarily cheap. For a basic robotics arm work cell, it costs $35,000 per year — paid monthly — plus a one-time onboarding fee of an additional $35,000. But Kretchmer says those numbers stack up favorably versus the cost of talent, providing companies with positive ROI faster than they’d experience with a large initial investment. Depending on how many shifts you’re running it, the cost of the cobot — or collaborative robot — breaks down to about $6.50 or $7 an hour after year one. The company also offers larger cobots which, at the high end, are priced at $50,000 for a yearly subscription plus $50,000 in one-time onboarding costs. But those machines can replace tasks that usually take two or three people to complete.

Kretchmer ultimately believes RaaS can help companies begin to reshore parts of their operation to build resiliency. “Those little bits and bobs that were so easy to outsource, but that are critical components to really important products,” he says. “Those need to be made locally.”

Get Expert Help to Customize and Find the Right Solution

Rapid Robotics has created a promising model. But it’s not the only way to institute a high-quality cobot into your manufacturing firm.

Just ask the school lunch-maker Innovative Food Services. Founded in 2022, the company grew from a modest restaurant catering business serving 200 or 300 meals a day to a school-lunch focused business that pumps out 9,000 meals daily. IFS has had to scale their operations quickly.

Along the way, one job on the assembly line was constantly creating problems for CEO Thomas Lane’s staff. It was a position at the end of the line in which an employee would take the meal trays and stack them inside large plastic totes, with a lot of twisting and turning and bending along the way. The role was a back killer, and even rotating various employees in an out of it during each day was causing inordinate amounts of strain. So, one day, IFS reached out to MAGNET, the nonprofit manufacturing consultancy I run out of Cleveland.

We worked with a robotics provider to build and deploy a custom cobot that took the place of that difficult, end-of-line job. “Switching over to the cobot, they’re able to line up six meals, pick up all six, move them over and drop them down into totes,” Lane says. “Not only has it increased the speed and the efficiency of our line, but also our worker satisfaction. It was a win all the way around.”

IFS would end up purchasing two cobots, one for each of their assembly lines. Within 60 days of building, plus a couple more to train staff for regular maintenance, the company was up and running. All in all, Lane says the cobots were less expensive than expected, paying for themselves within about 18 months.

“I just encourage other manufacturers to really take that leap if they’re on the fence about it,” Lane says.

The Future of Robotics

It’s difficult to say what’s coming next for robotics. Business models like the one Rapid Robotics deploys are attractive in their simplified, all-in-one approach. But it remains to be seen whether manufacturers are willing to trade-out one-time investments — however large they may be — for the subscription model.

Regardless, there’s a lot to be encouraged about when it comes to robotics deployment, from the largest manufacturers to some of the smallest. Robotics companies are beginning to realize that the average manufacturer needs help implementing their technology, and they’re getting creative and finding implementation partners to provide that expertise. The reward will be sizable: investments in robots contributed 10 percent of the GDP growth in OECD countries from 1993 to 2016, according to one study. And robotics capabilities have only continued to improve.

We may be yet to build the world envisioned on the Jetsons, but robots have arrived and are here to stay — and that’s a good thing for manufacturers.

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How much does it cost to start a robotics education business?

By henry sheykin, robotics education for children bundle.

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Startup Costs

Introduction.

Welcome to our blog post on the exciting world of robotics education for children! In today's rapidly evolving technological landscape, it is crucial to equip our young ones with the skills and knowledge that will enable them to thrive in the future. As the demand for STEM (Science, Technology, Engineering, and Mathematics) professionals continues to grow, robotics education has emerged as a fundamental component in nurturing critical thinking and problem-solving abilities among children.

The robotics education industry has witnessed remarkable growth in recent years. According to a survey by Market Research Future, the global robotics education market is projected to reach a value of $30 billion by 2027, with a compound annual growth rate (CAGR) of 18.2% . This significant growth is driven by the increasing recognition of the immense value robotics education offers in fostering creativity, innovation, and technical skills in children.

At our robotics education center, we aim to provide children with a comprehensive learning experience that combines education and technology. Our courses and workshops are designed to instill critical thinking, problem-solving, and creative skills through hands-on experience with robotics. By offering a safe and engaging environment, we encourage children to explore various robotic kits, learn programming languages, and participate in team projects and competitions.

By investing in the future of our children, we are shaping the next generation of innovators and preparing them for exciting careers in STEM fields. Through our robotics education center, we strive to bridge the gap between theoretical knowledge and real-world applications, empowering children to become confident problem solvers and creators in an increasingly technology-driven world.

Starting a robotics education center for children requires a significant investment to ensure a high-quality learning environment and the necessary resources for effective instruction. Here are the estimated startup costs for opening a robotics education center:

Please note that the above estimates are approximate and can vary based on factors such as location, size of the facility, and the specific needs and goals of the robotics education center. It is recommended to conduct thorough research and create a detailed business plan to accurately assess the startup costs for your specific venture.

Despite the initial investment required, establishing a robotics education center for children can be a highly rewarding and profitable business that contributes to the development of young minds and prepares them for the future of technology.

1. Facility renovation and setup costs

When it comes to opening a robotics education center for children, one of the significant considerations is the facility renovation and setup costs. A suitable space is crucial for accommodating various robotics kits, providing a safe environment, and creating an engaging learning atmosphere.

The costs associated with facility renovation and setup can vary depending on factors such as location, size, and condition of the space. On average, the expenses for renovating and setting up a robotics education center range from $30,000 to $100,000 . This includes costs for partitioning the area into classrooms, equipping them with necessary furniture and technology, installing safety measures, and creating a visually appealing environment to stimulate children's curiosity.

In addition to the general setup, specific equipment and materials are required for robotics education. These may include robotics kits, programming tools, computers, 3D printers, soldering stations, and other technology-related resources. The approximate cost for acquiring these essentials can range from $10,000 to $50,000 depending on the scope and quantity of materials required.

Moreover, it is essential to invest in a robust internet connection and networking infrastructure to facilitate effective teaching and learning. The cost of setting up a reliable network, including routers, switches, and cabling, can range from $2,000 to $10,000 .

In summary, the facility renovation and setup costs for a robotics education center can range from approximately $30,000 to $100,000 for general renovations, $10,000 to $50,000 for essential equipment and materials, and $2,000 to $10,000 for networking infrastructure. Taking these factors into account is crucial for ensuring a conducive learning environment and providing children with the necessary resources for their robotics education journey.

2. Purchase of robotic kits and equipment

When establishing a robotics education center for children, one of the crucial aspects is the purchase of robotic kits and equipment. These essential resources are the building blocks for hands-on experience and practical learning in the field of robotics.

Currently, the costs of robotic kits and equipment can vary depending on the complexity and functionality offered by different manufacturers. On average, the expenditure for a basic robotic kit suitable for beginner-level courses can range from $100 to $200. These kits often include components like motors, sensors, microcontrollers, and basic programming interfaces.

For more advanced courses and workshops, the costs of robotic kits and equipment can be higher, typically ranging from $300 to $500. These kits often provide additional features such as more sensors, advanced microcontrollers, and specialized components for specific robotic applications.

It is important to note that the variety of robotic kits available in the market is continuously expanding, offering a wide range of options to suit different educational objectives and budgets. Educators can choose from established brands like LEGO Mindstorms, VEX Robotics, or Arduino-based kits, or opt for more affordable alternatives that still offer quality learning experiences.

In addition to robotic kits, other equipment requirements include laptops or computers for programming, tools for assembly and maintenance, and safety gear. The costs of these supplementary items can range from $500 to $1000 or more, depending on the number of students and the quality of the equipment selected.

When deciding on the robotic kits and equipment to purchase for a robotics education center, it is essential to consider the specific goals, budget, and target age group of the center. It is also advisable to plan for future growth and upgrades, as robotics technology continues to evolve rapidly.

• Example 1: A robotics education center catering to young children aged 6-10 might invest in a number of LEGO WeDo 2.0 kits, which are priced at approximately $160 each. These kits provide a playful introduction to robotics and programming, incorporating colorful LEGO elements and drag-and-drop coding interfaces.
• Example 2: A robotics center targeting older children and teenagers might opt for the VEX IQ Super Kit, priced at around $400. This comprehensive kit includes a variety of structural components, sensors, motors, and a programmable brain, allowing students to create more complex robotic systems.
• Example 3: For a larger-scale robotics education center with multiple classrooms and a focus on advanced robotics, the costs for robotic kits and equipment could range from $10,000 to $20,000 or more. This investment would include high-quality kits from brands like LEGO Mindstorms EV3 or VEX Robotics V5, as well as additional tools and accessories to support a larger student population.

3. Technology infrastructure investment

A vital aspect of establishing a robotics education center for children is the investment required for technology infrastructure. This includes the purchase of robotics kits, programming tools, software licenses, and other necessary equipment to create a robust learning environment.

The cost of technology infrastructure can vary depending on the scale and scope of the robotics education center. According to recent statistics, the average investment for setting up such a center ranges from $50,000 to $100,000. This amount covers the initial purchase of robotics kits, computer systems, software, and other essentials.

For example, if you plan to cater to a larger number of students and offer advanced courses, the investment may lean towards the higher end of the spectrum. On the other hand, a smaller center with fewer resources and courses could require a lower investment.

It is important to consider additional expenses when planning for technology infrastructure investment. These expenses may include maintenance costs, regular updates to software and hardware, and potential repairs of robotics kits due to wear and tear. Allocating a portion of the initial investment for future upgrades and replacements is a wise strategy.

Furthermore, it is essential to choose the right suppliers and vendors when purchasing technology infrastructure for the robotics education center. Ensuring the quality and reliability of the equipment is crucial as it directly impacts the learning experience of the children.

• Research and compare prices from multiple suppliers to find the best deals while maintaining quality standards.
• Consider seeking discounts or special offers from vendors that cater specifically to educational institutions or robotics education centers.
• Take advantage of grants or funding opportunities that may be available to support the establishment of robotics education centers.

In conclusion, when opening a robotics education center, investing in technology infrastructure is a significant aspect to consider. By allocating a suitable budget, choosing reliable suppliers, and planning for future expenses, you can build a strong foundation for an engaging and successful learning environment for children interested in robotics.

4. Classroom furniture and supplies

When setting up a robotics education center for children, it is essential to allocate a budget for classroom furniture and supplies. The cost of these items can vary depending on the number of classrooms and the specific needs of the center.

Furniture: Classroom furniture such as tables, chairs, and storage cabinets are necessary to create a conducive learning environment for children. On average, the cost of furnishing one classroom can range from $1,000 to $5,000, considering the quality and durability of the furniture.

Robotics Kits: Robotics kits are an essential component of the learning experience, as children can learn hands-on skills through assembling and programming robots. The cost of robotics kits can range from $50 to $500 per kit, depending on the complexity and features of the kit.

Computers and Laptops: Computers or laptops are crucial for programming and coding activities. The cost of computers or laptops can vary greatly depending on the specifications, brand, and quantity needed. On average, a basic computer or laptop can cost around $500 to $1,500.

Other Supplies: In addition to furniture and robotics kits, there are other necessary supplies such as whiteboards, markers, projectors, and teaching aids. These items can collectively cost around $500 to $2,000, depending on the size and requirements of the classrooms.

• Example 1: A robotics education center with four classrooms may need to allocate a budget of approximately $20,000 for furniture and supplies.
• Example 2: If the center plans to accommodate a larger number of students, additional furniture and supplies may be required, increasing the overall cost accordingly.

It is crucial to note that the cost estimates provided are rough averages and can vary based on geographical location, suppliers, and quality of the products. Therefore, it is recommended to research and compare prices from different vendors to ensure the best value for money while maintaining the necessary quality standards.

5. Hiring and training of robotics instructors

Hiring and training qualified robotics instructors is a crucial component of establishing a successful robotics education center for children. In order to provide the best education and guidance, it is important to hire instructors who possess a strong background in robotics, programming, and teaching.

The cost of hiring robotics instructors varies depending on factors such as qualifications, experience, and location. On average, the hourly rate for robotics instructors in the United States ranges from $20 to $50 per hour. However, more experienced and specialized instructors may charge higher rates, reaching up to $100 per hour.

Training the robotics instructors is an ongoing process that helps them stay updated with the latest advancements and methodologies in the field. The cost of training can vary based on the resources and programs chosen. Workshops and training courses specifically tailored for robotics education can range from $500 to $2,000 per instructor. Additionally, the cost of providing training materials, such as textbooks, software licenses, and robotic kits, should also be considered.

It is important to invest in the professional development of robotics instructors, as their expertise and ability to effectively teach children will directly impact the quality of education provided at the center. By hiring qualified instructors and providing them with continuous training opportunities, the robotics education center can ensure that children receive the best educational experience possible.

• A robotics education center in a metropolitan area hires two robotics instructors, each with a rate of $40 per hour. Assuming a 20-hour workweek per instructor, the monthly cost for instructors' salaries would be $3,200.
• The center invests in a professional development program for its instructors, which includes attending robotics workshops and purchasing training materials. The total cost for the program is estimated at $1,500 per instructor.
• Therefore, the monthly cost of hiring and training robotics instructors for this center would amount to $8,400.

6. Marketing and advertising expenses

When considering the cost of marketing and advertising for a robotics education center, it is important to understand that these expenses will play a significant role in reaching potential customers and creating awareness about the business. According to recent statistics, the average cost of marketing and advertising for education-related businesses in the United States ranges from $10,000 to $50,000 per year.

One of the primary marketing expenses is creating a professional and engaging website. This can cost between $2,000 to $10,000, depending on the complexity and features required. Additionally, search engine optimization (SEO) services, which help improve website visibility on search engines, can range from $500 to $5,000 per month.

Advertising through various channels is another aspect of marketing expenses. It is important to consider the costs of online advertising platforms such as Google Ads, Facebook Ads, or Instagram ads. Depending on the target audience and campaign goals, costs can range from $500 to $10,000 per month. Offline advertising methods, such as flyers, brochures, and newspaper ads, may also incur additional expenses.

An effective marketing strategy should also include social media marketing, which allows businesses to engage with their target audience directly. Hiring a social media manager or outsourcing social media services can cost between $500 to $3,000 per month. Additionally, hosting events, workshops, or participating in trade shows and exhibitions can require a budget of $1,000 to $10,000, depending on the scale and location.

Lastly, utilizing email marketing campaigns to reach potential customers and keep existing ones informed about new courses and promotions can cost between $500 to $2,000 per month, depending on the size of the email list and the frequency of sending newsletters.

In conclusion, the cost of marketing and advertising expenses for opening a robotics education center can range from $10,000 to $50,000 per year. It is crucial to allocate a sufficient budget for these activities to ensure successful outreach and create awareness among potential customers.

7. Licensing and certification fees

When starting a robotics education center for children, one important aspect to consider is the licensing and certification fees. These costs are necessary to ensure that your business complies with legal requirements and meets the standards set by relevant educational authorities.

According to recent statistical information, the licensing fees for opening a robotics education center can range from $500 to $5,000, depending on various factors such as location, size of the facility, and the specific regulations of your jurisdiction. It is important to research and understand the licensing requirements in your area to budget for these expenses accordingly.

In addition to licensing fees, certification fees may also be applicable if you plan to offer specialized robotics courses or programs. For example, if you aim to provide certifications for specific robotics platforms or software, you may need to pay an additional fee for becoming an authorized certification center.

The cost of certification fees can vary considerably depending on the specific certifications you plan to offer. For instance, certification fees for popular robotics platforms like Lego Mindstorms or VEX Robotics can range from $100 to $500 per certification level. These certifications showcase the expertise and knowledge of your center, providing added value to the educational services you provide.

It is essential to carefully assess the licensing and certification fees in your business plan and consider them as part of your initial investment. Remember to factor in these costs while determining the pricing of your courses and workshops. Consider offering bundled packages that include certification fees within the overall price, making it more convenient and appealing for parents and potential customers.

In conclusion, when establishing a robotics education center for children, be prepared for the licensing and certification fees that are necessary to ensure compliance and credibility. Take into account the range of costs associated with licensing and explore the potential for offering specialized certifications that align with popular robotics platforms. These fees are an investment in the quality and legitimacy of your center, allowing you to provide a valuable educational experience for children interested in robotics.

8. Insurance coverage for the center

When starting a robotics education center for children, it is essential to consider insurance coverage to protect your business, staff, and students. The costs associated with obtaining the right insurance policies can vary based on several factors, including the size of the center, the number of employees, and the coverage limits required.

One of the foundational insurance policies to consider for your robotics education center is General Liability Insurance. This coverage protects your business from third-party claims of bodily injury, property damage, or personal damage that may occur on your premises. The cost for this type of insurance can range from $500 to $2,000 per year, depending on the size and scope of your center.

As an educational institution offering robotics courses, Professional Liability Insurance is crucial to protect against claims of negligence or errors in the services provided. This coverage can help cover legal costs and settlements resulting from lawsuits if a student or their parents allege inadequate instruction or supervision. The cost for professional liability insurance can range from $1,000 to $3,000 per year, depending on the coverage limits and the number of instructors employed.

Property Insurance is another important consideration to protect your center's physical assets, including equipment, furniture, and supplies. This coverage can provide financial assistance in case of damage or loss due to incidents like fire, theft, or vandalism. The cost of property insurance can vary based on the value of your center's assets, with an estimated annual premium ranging from $500 to $2,500.

If your robotics education center has employees, it is crucial to have Workers' Compensation Insurance to provide coverage for workplace injuries or illnesses. This coverage can help cover medical expenses, lost wages, and disability benefits for employees who suffer work-related injuries. The cost of this insurance varies based on factors such as the number of employees and the level of risk associated with the work performed.

Other coverage to consider may include cyber liability insurance to protect against data breaches or electronic theft, as well as business interruption insurance to cover lost income in case of an unforeseen event, such as a natural disaster. The costs for these additional policies can vary based on the specific coverage requirements of your center.

It is important to consult with an insurance agent or broker experienced in working with educational institutions to ensure you have the appropriate coverage for your robotics education center. They can help assess your specific needs and provide accurate cost estimates based on your unique circumstances.

9. Administrative and Operational Software Systems Investment

Investing in administrative and operational software systems is crucial for the smooth functioning of a robotics education center for children. These systems play a vital role in managing various aspects of the business, including scheduling classes, tracking student progress, handling payments, and streamlining communication with parents.

The cost of administrative and operational software systems can vary depending on the specific needs and scale of the robotics education center. On average, the investment can range from $5,000 to $20,000, including both initial setup and ongoing maintenance fees. Factors such as the number of students, complexity of courses, and additional features required can influence the overall cost.

Examples of administrative and operational software systems:

• A student management system that allows efficient registration, enrollment, and class scheduling. This software can help automate administrative tasks and provide real-time data on student attendance and progress.
• A payment processing system that enables secure online transactions and financial record keeping. This software ensures smooth payment collection for course fees and other services offered by the robotics education center.
• A learning management system that offers a digital platform for delivering course materials, organizing assessments, and providing interactive learning experiences. This software can enhance the educational experience and support collaboration among students and instructors.
• A communication system that facilitates effective communication between the robotics education center, parents, and students. This software may include features like messaging, email notifications, and event management.

Investing in the right administrative and operational software systems is essential for ensuring efficient operations, enhancing the learning experience, and maintaining strong communication with all stakeholders involved in the robotics education center. While the upfront investment may seem significant, the long-term benefits and streamlined processes outweigh the costs.

Launching a robotics education center for children requires careful planning and financial investment. From facility renovation and setup costs to purchasing robotic kits and equipment, there are various expenses to consider. Additionally, investing in technology infrastructure, classroom furniture, and supplies, as well as hiring and training robotics instructors, are essential for providing a high-quality learning experience.

Marketing and advertising expenses, licensing and certification fees, insurance coverage, and administrative and operational software systems also contribute to the overall cost of starting a robotics education center. It is crucial to budget for these expenses to ensure the center's smooth operations and compliance with industry standards.

Despite the initial financial investment, the robotics education industry is experiencing significant growth, with the global market projected to reach a value of $30 billion by 2027. This growth is driven by the recognition of the invaluable skills and knowledge robotics education offers in preparing children for the future.

By establishing a robotics education center, we are shaping the next generation of innovators and equipping them with the tools they need to succeed in STEM fields. Through comprehensive courses and hands-on experiences, we foster critical thinking, problem-solving, and creativity skills, preparing children to become confident problem solvers and creators in an increasingly technology-driven world.

As we embrace the potential of robotics education for children, let us invest in their future and provide them with the opportunities they need to thrive. By bridging the gap between theoretical knowledge and real-world applications, we empower children to become the innovators and leaders of tomorrow.

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Importance of Robotics in Education

Education is evolving rapidly, and innovative approaches are being integrated into classrooms to prepare students for the future. One such approach that has gained prominence is robotics education. By combining technology, engineering, and creativity, robotics education offers numerous benefits to students. In this article, we will explore the importance of robotics in education, its benefits, integration into the curriculum, and the various ways it promotes critical thinking, problem-solving skills, collaboration, and STEM education.

Introduction to Robotics for Education

Robotic technology has advanced significantly in recent years, and it has become an integral part of various industries. Recognizing the potential of robotics, educators have embraced its integration into the education system. Robotics for education involves the use of robots, programmable devices, and related technologies to enhance learning experiences and engage students in interactive and hands-on activities.

Robotics education holds immense importance in preparing students for the future. With rapid advancements in automation and artificial intelligence, robotics education equips students with essential skills that are in high demand in today’s job market. By engaging students in robotics, we nurture their problem-solving abilities, critical thinking skills, creativity, and innovation.

Benefits of Robotics Education

The benefits of robotics education are manifold. Let’s explore some key advantages:

1. Integration of the Curriculum: Robotics can be integrated into various subjects, including science, mathematics, engineering, and technology. By incorporating robotics into the curriculum, educators can provide interdisciplinary learning opportunities, making education more engaging and relevant.

2. Hands-on Learning: Robotics education promotes hands-on learning, allowing students to experiment, build, and program robots. This experiential approach enhances their understanding of abstract concepts and encourages them to apply theoretical knowledge to practical situations.

3. Developing Critical Thinking and Problem-Solving Skills: Robotics activities require students to analyze problems, break them down into smaller components, and develop solutions. Through trial and error, students learn to think critically, identify patterns, and troubleshoot issues, fostering valuable problem-solving skills.

4. Fostering Creativity and Innovation: Robotics education nurtures creativity and innovation by encouraging students to design and build their robots. It provides them with a platform to explore their imaginations, think outside the box, and come up with unique solutions to challenges.

5. Building Collaboration and Teamwork: Robotics projects often involve teamwork, requiring students to collaborate, communicate, and delegate tasks. By working together, students develop essential collaboration and teamwork skills that are vital in real-world scenarios.

6. Promoting STEM Education: Robotics is an excellent tool for promoting STEM (Science, Technology, Engineering, and Mathematics) education. It integrates these disciplines seamlessly, giving students a holistic understanding of how they work together in practical applications.

Integration of Robotics in the Curriculum

The integration of robotics in the curriculum can be tailored to different educational levels and subjects. Here are some approaches:

1. STEM-focused Robotics Courses: Schools can offer dedicated robotics courses that provide in-depth knowledge and hands-on experience in robotics and related technologies. These courses can cover topics such as programming, mechanical design, and sensor integration.

2. Robotics as a Teaching Tool: Robotics can be used as a teaching tool to enhance the learning experience in various subjects. For example, in mathematics, robots can be programmed to solve complex equations, making the subject more tangible and engaging.

3. Cross-disciplinary Projects: Schools can encourage cross-disciplinary projects that incorporate robotics into multiple subjects. For instance, students can design and program robots to simulate environmental scenarios, integrating concepts from science, mathematics, and technology.

By integrating robotics into the curriculum, educators can create an environment that fosters innovation, critical thinking, and practical application of knowledge.

Hands-on Learning with Robotics

One of the key aspects of robotics education is hands-on learning. Unlike traditional teaching methods that rely on textbooks and lectures, robotics allows students to actively engage with technology and apply theoretical concepts in practical ways. This hands-on approach promotes a deeper understanding of abstract ideas and encourages students to explore their creativity.

When students work with robots, they become active participants in the learning process. They learn by doing, experimenting, and making mistakes. Through trial and error, they gain a profound understanding of concepts such as programming, circuitry, mechanics, and problem-solving.

Hands-on learning with robotics also instills a sense of ownership and pride in students. As they design, build, and program robots, they see their creations come to life. This tangible outcome boosts their confidence and motivates them to explore further.

Developing Critical Thinking and Problem-Solving Skills

Critical thinking and problem-solving skills are essential in today’s complex world. Robotics education provides an ideal platform for developing these skills. When students engage with robots, they encounter challenges that require analytical thinking, logical reasoning, and creative problem-solving.

Robotic projects involve breaking down complex problems into smaller, manageable parts. Students learn to identify patterns, analyze data, and develop step-by-step solutions. They experiment with different approaches, evaluate their effectiveness, and iterate on their designs.

Through these experiences, students develop critical thinking skills that extend beyond robotics. They learn to approach problems with a systematic mindset, consider multiple perspectives, and evaluate the pros and cons of different solutions. These skills are transferable to various areas of their lives, from academics to future careers.

Fostering Creativity and Innovation

Robotics education provides an excellent platform for fostering creativity and innovation. By designing, building, and programming robots, students have the freedom to explore their imaginations and bring their ideas to life. They are encouraged to think outside the box, experiment with different approaches, and push the boundaries of what is possible.

Robotic projects often involve open-ended challenges, where there are multiple solutions. This encourages students to think creatively, consider alternative perspectives, and come up with unique solutions to problems. They learn that there is no single “right” answer and that innovation lies in exploring different possibilities.

In addition to fostering creativity, robotics education also nurtures innovation. Students are encouraged to identify real-world problems and develop robots that address these challenges. By applying their technical skills, critical thinking, and creativity, they develop innovative solutions that have the potential to make a positive impact in society.

Building Collaboration and Teamwork

Collaboration and teamwork are essential skills in today’s interconnected world. Robotics education provides an ideal platform for students to develop these skills. Many robotics projects are designed to be completed in teams, where students work together to achieve a common goal.

When working in teams, students learn to communicate effectively, listen to others’ ideas, and delegate tasks based on each team member’s strengths. They learn the importance of coordination, cooperation, and compromise. They also learn to value diverse perspectives and leverage the strengths of each team member.

Collaborative robotics projects teach students the significance of collective problem-solving. They understand that combining different ideas and skills can lead to more robust and innovative solutions. These collaborative experiences prepare students for future careers, where teamwork and collaboration are often essential for success.

Promoting STEM Education

One of the significant advantages of robotics education is its ability to promote STEM education. STEM subjects play a crucial role in preparing students for the jobs of the future, and robotics provides a practical and engaging way to integrate these disciplines.

By working with robots, students develop an understanding of science, as they explore concepts such as motion, sensors, and energy. They apply mathematical concepts in programming and data analysis. They engage in engineering by designing and building robots, considering factors such as structure, stability, and efficiency. Lastly, they develop technological literacy by using software, sensors, and programming languages.

Robotics education exposes students to the interconnectedness of STEM fields and highlights the relevance of these subjects in real-world applications. It helps them see the practical applications of their theoretical knowledge and fosters a passion for STEM subjects.

Overcoming Challenges in Implementing Robotics Education

While robotics education offers numerous benefits, there are challenges that educators may face in its implementation. It is important to address these challenges to ensure the effective integration of robotics in education. Some common challenges and potential solutions include:

1. Cost and Resources: Robotics kits and equipment can be expensive, making it difficult for some schools to afford them. One solution is to seek partnerships with local businesses, universities, and community organizations that may provide funding or equipment. Grants and sponsorships can also be explored. Additionally, open-source and low-cost robotics platforms can be utilized.

2. Teacher Training and Support: Many educators may have limited experience with robotics and may require training and support. Professional development opportunities, workshops, and online resources can help teachers build the necessary skills and confidence to incorporate robotics into their classrooms. Collaboration among teachers and sharing best practices can also be beneficial.

3. Curriculum Integration: Integrating robotics into the curriculum can be a challenge due to time constraints and standardized testing requirements. By identifying areas where robotics can enhance existing curriculum objectives, educators can find ways to integrate robotics seamlessly. Cross-curricular projects and interdisciplinary collaboration can also facilitate integration.

4. Inclusion and Diversity: It is crucial to ensure that robotics education is accessible to all students, regardless of gender, race, or socioeconomic background. Efforts should be made to create an inclusive and welcoming environment. Providing diverse role models and incorporating culturally relevant content can help foster a sense of belonging and promote diversity in robotics.

Professional Development for Educators

To successfully integrate robotics education into classrooms, it is essential to provide educators with the necessary training and professional development opportunities. Robotics is a rapidly evolving field, and teachers need to stay updated with the latest technologies and pedagogical approaches.

Professional development programs can offer training in areas such as robotics programming, hardware setup, troubleshooting, and curriculum integration. These programs can be conducted through workshops, online courses, conferences, and collaboration with experts in the field.

Collaboration among educators is also valuable. Creating communities of practice where teachers can share resources, lesson plans, and best practices fosters a supportive network that can enhance the effectiveness of robotics education.

By investing in the professional development of educators, schools can ensure that they are well-equipped to deliver quality robotics education and inspire their students.

Robotics Competitions and Challenges

Robotics competitions and challenges provide opportunities for students to apply their robotics skills, test their problem-solving abilities, and showcase their creativity. These events foster a sense of excitement and motivate students to excel in robotics.

There are various robotics competitions at different levels, ranging from local and regional events to international competitions. Some popular competitions include World Robot Olympiad, FIRST LEGO League, FIRST Robotics Competition, etc. These competitions often require teams to design, build, and program robots to complete specific tasks or solve complex problems.

Participating in robotics competitions not only allows students to showcase their skills but also exposes them to a broader community of like-minded individuals. They can interact with other teams, learn from their approaches, and build lasting connections.

Robotics Kits and Platforms for Education

A wide range of robotics kits and platforms are available for educational purposes. These kits typically include components such as microcontrollers, sensors, motors, and programming software. They provide a hands-on learning experience and allow students to build and program their robots.

Popular robotics kits include LEGO Mindstorms, Arduino, Raspberry Pi, and VEX Robotics. Each kit has its unique features, capabilities, and programming languages, providing educators with options to choose from based on their specific needs and goals.

When selecting robotics kits, it is essential to consider factors such as ease of use, compatibility with existing technology, availability of educational resources, and scalability for different grade levels. Kits that support open-source programming languages and offer a diverse range of projects and activities are often preferred for educational settings.

Robotics in Special Education

Robotics education can be particularly beneficial for students with special educational needs. The hands-on nature of robotics engages students with different learning styles and provides them with a unique platform to explore and express their ideas.

For students with physical disabilities, robotics can offer opportunities for mobility and independence. Robotic prosthetics and exoskeletons can assist students with mobility impairments, allowing them to participate fully in activities.

For students with autism spectrum disorders, robotics can provide a structured and predictable learning environment. Robots can be programmed to deliver social cues, facilitate communication, and support social interaction and emotional regulation.

Robotics also promotes inclusive learning environments, where students with diverse abilities work collaboratively. By fostering teamwork and collaboration, robotics education helps break down barriers and encourages empathy and understanding among students.

Robotics education plays a crucial role in preparing students for the future. By integrating robotics into the curriculum, educators can create engaging and dynamic learning experiences that promote critical thinking, problem-solving skills, collaboration, and innovation. Robotics education fosters creativity, builds essential STEM skills, and prepares students for the challenges of a rapidly evolving technological landscape. With the right support, resources, and professional development, robotics education has the potential to revolutionize the way we educate and inspire the next generation of innovators and problem solvers.

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Transforming Learning: The Impact of Robotics on Education

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• Key Takeaways

Gartner predicts a 30% increase in the integration of robotics in education by 2024. 

Statista reports a 25% improvement in student engagement with the integration of robotics in classrooms.

Ethical considerations such as privacy and data security must be addressed in robotics education.

The future of robotics in education includes personalized learning experiences and increased integration into curriculums.

In today’s rapidly evolving educational landscape, robotics has emerged as a transformative force, reshaping the way students learn and educators teach. The integration of robotics in education brings forth a plethora of opportunities and challenges, from fostering critical thinking and problem-solving skills to addressing ethical and social implications.

As we delve deeper into the realm of robotics in education, a captivating question arises: How will the fusion of technology and learning reshape the future of education, and what impact will it have on students’ skills and experiences?

Introduction to Robotics in Education

• Definition of Robotics in Education

Robotics in education refers to the integration of robotic technologies and tools into learning environments to enhance teaching and learning experiences.

It involves the use of programmable machines, sensors, and other robotic components to facilitate hands-on learning, problem-solving activities, and creative exploration among students.

Robotics in education encompasses a wide range of applications, from robotics kits and platforms designed for educational purposes to advanced robotics programming and engineering courses.

• Historical Background and Evolution

The history of robotics in education can be traced back to the mid-20th century when early educational robots like the Turtle Robot were introduced to teach programming concepts to students. Over the decades, advancements in robotics technology, artificial intelligence (AI), and educational pedagogy have fueled the evolution of robotics in education.

The development of affordable robotics kits, such as LEGO Mindstorms and Arduino-based platforms, has democratized access to robotics education, making it more accessible to students and educators worldwide.

• Importance and Relevance in Modern Education Systems

In today’s rapidly evolving digital age, robotics plays a crucial role in transforming learning experiences and preparing students for future careers. The integration of robotics in modern education systems offers several key benefits, including:

• Enhanced STEM (science, technology, engineering, and mathematics) education: Robotics provides a hands-on approach to learning STEM concepts, making abstract theories more tangible and engaging for students.
• Development of 21st-century skills: Robotics education promotes critical thinking, problem-solving, collaboration, and creativity, which are essential skills for success in the modern workforce.
• Personalized learning experiences: Robotic tools and platforms can be customized to meet individual learning needs, allowing students to progress at their own pace and explore topics of interest.
• Career readiness: Exposure to robotics education equips students with technical skills, programming knowledge, and robotics engineering expertise, preparing them for careers in robotics, AI, automation, and related fields.

Benefits of Robotics in Education

• Enhancing Critical Thinking and Problem-Solving Skills

Robotics in education plays a pivotal role in enhancing critical thinking and problem-solving skills among students. When students engage with robotics systems, they are presented with real-world challenges that require analytical thinking and creative problem-solving strategies.

By programming robots to perform specific tasks or solve problems, students learn to break down complex problems into manageable steps, identify patterns, and devise effective solutions.

This hands-on approach to learning encourages students to think critically, explore different possibilities, and apply their knowledge in practical contexts, thus preparing them for success in various academic and professional endeavors.

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• Fostering Creativity and Innovation

Another significant benefit of robotics in education is its ability to foster creativity and innovation. As students experiment with robotics technologies, they are encouraged to think outside the box, explore new ideas, and develop innovative solutions to problems.

The process of designing, building, and programming robots allows students to unleash their creativity, test new concepts, and refine their ideas through trial and error.

This creative exploration not only nurtures a passion for STEM (Science, Technology, Engineering, and Mathematics) fields but also instills a mindset of curiosity and experimentation that is essential for driving innovation in the digital age.

• Promoting Hands-On Learning Experiences

Robotics in education offers unparalleled opportunities for hands-on learning experiences that go beyond traditional classroom methods. Through robotics projects, students actively engage in the learning process, applying theoretical knowledge to practical tasks and gaining valuable skills through direct experience.

Whether it’s assembling robot components, writing code to control robotic movements, or troubleshooting technical issues, students develop a deeper understanding of STEM concepts and principles through hands-on experimentation.

This hands-on approach not only makes learning more engaging and memorable but also cultivates a sense of confidence and empowerment among students as they see tangible results of their efforts.

• Improving Retention and Engagement Among Students

Furthermore, robotics in education has been shown to improve retention and engagement among students. The interactive and immersive nature of robotics projects captures students’ interest and motivates them to actively participate in learning activities.

By incorporating gamification elements, such as challenges, competitions, and rewards, educators can further enhance student engagement and encourage continuous learning.

The hands-on nature of robotics also helps reinforce learning outcomes as students apply concepts in real-world contexts, leading to better retention of knowledge and skills over time. Overall, robotics in education not only benefits students academically but also nurtures a lifelong passion for learning and exploration.

Integration of Robotics in Curriculum

• Strategies for Incorporating Robotics into Various Subjects

Alignment with STEM Curriculum:

• Integrate robotics activities that align with STEM subjects (Science, Technology, Engineering, Mathematics).
• Use robotics to simulate scientific experiments, demonstrate engineering concepts, and apply mathematical principles.

Interdisciplinary Approach:

• Promote interdisciplinary learning by combining robotics with subjects like history, literature, social studies, etc.
• Encourage students to apply knowledge from different disciplines to solve real-world problems using robotics.

Project-Based Learning:

• Implement project-based learning activities that involve robotics, fostering critical thinking and problem-solving skills.
• Engage students in researching, designing, and presenting solutions related to various subjects using robotics.

Differentiated Instruction:

• Cater to diverse learning needs by offering robotics activities at different levels of complexity.
• Provide opportunities for students to work individually or in groups based on their learning preferences and abilities.
• Examples of Successful Robotics Integration Programs

Robotics Clubs and Competitions:

• Establish robotics clubs or teams where students participate in competitions, design challenges, and collaborative projects.
• Showcase successful examples of student projects and achievements in robotics competitions at local, national, or international levels.

Vocational Education Integration:

• Integrate robotics into vocational education programs, offering training in automation, mechatronics, and robotics engineering.
• Provide hands-on experience with robotics systems to prepare students for careers in robotics-related industries.

Collaborative Partnerships:

• Collaborate with industry partners, universities, or nonprofit organizations to enhance robotics education initiatives.
• Leverage partnerships to access resources, expertise, and funding opportunities for robotics integration programs.
• Challenges and Solutions in Curriculum Integration

Resource Constraints:

• Address challenges related to limited funding, equipment, and technical support for robotics programs.
• Seek grants, sponsorships, or donations to acquire robotics kits, software, and infrastructure needed for curriculum integration.

Teacher Training and Support:

• Provide professional development workshops, training sessions, and resources for teachers to enhance their robotics knowledge and instructional skills.
• Establish mentoring programs or peer networks to support educators in implementing robotics curriculum effectively.
• Develop a scalable framework for curriculum integration that can be adapted across grade levels and subject areas.
• Implement phased approaches, starting with pilot programs and gradually expanding robotics initiatives to ensure long-term sustainability.

Equity and Inclusion:

• Address equity and access issues to ensure that all students have equal opportunities to participate in robotics education.
• Provide support for underrepresented groups, including girls, minorities, and students with disabilities, to engage and excel in robotics programs.

Robotics in Early Childhood Education

• Age-appropriate Robotics Activities and Tools

In early childhood education, the integration of robotics activities and tools is carefully designed to suit the developmental stages of young learners. Age-appropriate robotics activities may include simple coding games, interactive storytelling with robotic characters, and basic programming tasks using visual block-coding interfaces.

These activities are structured to be engaging and intuitive, allowing children to explore fundamental concepts of robotics and technology in a playful and interactive manner.

• Impact on Cognitive Development and Learning Outcomes

The introduction of robotics in early childhood education has shown positive impacts on cognitive development and learning outcomes. Through robotics activities, children develop essential skills such as problem-solving, logical reasoning, and spatial awareness.

Hands-on experiences with robots also enhance fine motor skills and hand-eye coordination. Moreover, engaging with robotics fosters creativity and imagination, encouraging children to think critically and explore innovative solutions to challenges.

• Ethical Considerations and Guidelines for Young Learners

When incorporating robotics in early childhood education, educators and developers must adhere to ethical considerations and guidelines to ensure a safe and positive learning experience for young learners.

This includes prioritizing children’s safety and well-being by using age-appropriate materials and technologies that comply with safety standards.

Additionally, ethical considerations encompass issues such as data privacy, consent, and the responsible use of robotics in educational settings. Educators play a crucial role in guiding children’s interactions with robotics, emphasizing respect, empathy, and digital citizenship from an early age.

Future Trends in Robotics Education

• Advances in Robotics Technology and Implications for Education

The field of robotics is advancing at a rapid pace, with new technologies and innovations constantly emerging. These advancements have significant implications for education, particularly in how robotics can enhance the learning experience.

One key area of advancement is in the development of more sophisticated and versatile robots that are capable of performing complex tasks and interactions. For example, humanoid robots with natural language processing capabilities can engage in meaningful conversations with students, providing personalized learning experiences and feedback.

Additionally, advancements in sensor technology allow robots to interact with the physical world more effectively, opening up new possibilities for hands-on learning and experimentation in educational settings.

• Robotics Competitions and Extracurricular Activities

Robotics competitions and extracurricular activities play a crucial role in engaging students and fostering their interest in robotics and STEM (science, technology, engineering, and mathematics) fields. These competitions provide students with opportunities to apply their knowledge and skills in practical scenarios, collaborate with peers, and showcase their creativity and problem-solving abilities.

For instance, events like robotics tournaments and hackathons challenge students to design and program robots to complete specific tasks or challenges within a given timeframe. Participation in such activities not only enhances students’ technical skills but also nurtures important soft skills such as teamwork, communication, and resilience.

• Predictions for the Future Role of Robotics in Learning Environments

Looking ahead, the future role of robotics in learning environments is poised for significant growth and impact. As robotics technology continues to evolve, we can expect to see more widespread integration of robots across educational institutions, from elementary schools to universities. Robots will not only serve as teaching assistants, providing personalized support and feedback to students, but also as collaborators in research and experimentation.

Furthermore, the use of virtual and augmented reality in conjunction with robotics will create immersive learning experiences, allowing students to explore complex concepts in interactive and engaging ways. Overall, the future of robotics in education holds great promise for transforming the way we teach and learn, empowering students to thrive in a technology-driven world.

In conclusion, the transformative impact of robotics on education is evident in its ability to revolutionize learning experiences, enhance critical skills, and foster a culture of innovation. By integrating robotics into curriculums, educators can create dynamic environments that promote hands-on learning, collaboration, and problem-solving abilities among students.

Furthermore, robotics in education opens doors for inclusive and accessible learning opportunities, particularly for students with diverse needs. As we look towards the future, embracing the potential of robotics in education not only prepares students for the digital age but also paves the way for a more engaging, equitable, and impactful educational journey for all.

• How does robotics benefit education?

Robotics enhances critical thinking, problem-solving, and creativity in students, leading to improved learning outcomes.

• What age group can benefit from robotics in education?

Robotics activities can benefit students of all ages, from early childhood to higher education, by offering age-appropriate learning experiences.

• Are there ethical considerations with robotics in education?

Yes, ethical considerations include privacy concerns, data security, and ensuring equitable access to robotics education for all students.

• What is the future of robotics in education?

The future of robotics in education includes advancements in technology, increased integration into curriculums, and the potential for personalized learning experiences.

• How can educators integrate robotics into their teaching practices?

Educators can integrate robotics into teaching practices through curriculum design, hands-on activities, and leveraging resources such as robotics kits and software.

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Robotics in Education: Enhancing Classroom Engagement and Beyond

This article discusses the development of robots in education, as well as its many advantages, uses in the classroom, and inspirational success stories. Join us as we explore the fascinating world of robotics, where engagement and education smoothly combine.

Introduction

The use of robotics in education has moved beyond the realm of science fiction and into the dynamic reality of a time of fast technological growth. Robotics has become a potent tool for raising student involvement in the classroom and improving the educational process. The use of robots offers a possible answer as instructors look for novel methods to hold students' interest and promote deeper comprehension. This article discusses the development of robots in education, as well as its many advantages, uses in the classroom, and inspirational success stories. Join us as we explore the fascinating world of robotics, where engagement and education smoothly combine.

The Evolution of Robotics in Education

Robotics in education has seen a remarkable transition from its early, unassuming origins to its current popularity. Early efforts used straightforward robotic devices to educate pupils to fundamental programming ideas. As technology developed, complex robot kits allowed for hands-on experimentation and the development of problem-solving abilities. Today, robotics is more than just a gizmo and is integrated into many different topic areas' curriculum. The continuous search of immersive, interactive learning experiences has fueled this progress. Robotics' journey in education reflects its development as a revolutionary force, transforming classrooms into dynamic centres of discovery and participation from early steps to sophisticated integration.

Benefits of Robotics in Education

Numerous advantages that improve both the learning process and outcomes are brought about by the incorporation of robots in education. These benefits go beyond the confines of the conventional classroom, influencing a new paradigm in education:

• Enhanced Engagement and Active Learning: With engaging, hands-on learning opportunities, robotics captivates students and promotes active engagement and in-depth comprehension. Practical activities like designing, creating, and programming robots excite interest and make learning fun.
• Fostering Critical Thinking and Problem-Solving: Students' ability to think critically and creatively is tested through robotics. They are motivated to examine, iterate, and change their techniques while designing solutions for actual situations; this helps them develop priceless problem-solving abilities.
• Bridging STEAM Education: Robotics promotes the development of all skills by smoothly integrating science, technology, engineering, the arts, and mathematics (STEAM). This multidisciplinary approach develops a broad range of skills necessary for contemporary professions.
• Encouraging Collaborative Learning: Teamwork and communication are fostered via collaborative robot construction projects and contests. In order to prepare for collaborative work contexts, students learn to communicate ideas, bargain for solutions, and work together to accomplish goals.
• Adapting to Individual Learning Styles: Robot-assisted training offers tailored learning routes by adjusting to students' learning styles and speed. Each learner will receive unique assistance and challenges thanks to this personalized approach.
• Practical Application of Coding Skills: Learning coding and programming in the context of robotics is practical. Writing code to control robot behavior helps students learn algorithmic thinking as they translate abstract ideas into practical actions.
• Preparation for Future Careers: Robotics exposure fosters abilities that are in great demand in the labor market, such as technology literacy, flexibility, and creative thinking. Students receive knowledge about developing industries like automation and artificial intelligence.
• Inclusivity and Accessibility: Robotics facilitates many learning styles, broadening access to education. It offers a non-judgmental setting where students with different skill levels may experiment, grow, and succeed.
• Inspiring Curiosity and Lifelong Learning: Robotics piques children's interest in technology and its possibilities, encouraging them to seek STEM jobs and cultivating a lifetime love of learning and discovery.
• Connecting with Real-World Contexts: Robotics projects frequently reflect real-world difficulties, bridging academic learning with real-world applications. By bridging the gap between theory and practice, this relationship increases the relevance and significance of learning.

By using robots as a teaching tool, educators and students can create immersive, transforming learning experiences that provide the future generation the skills they need to succeed in a world that is always changing.

Practical Applications of Robotics in the Classroom

Through the practical use of robotics, the classroom is transformed into a dynamic space for exploration. Students become involved in coding and programming, creating algorithms to move robots and overcoming practical problems. Robotics contests and team projects promote innovation and collaboration while increasing problem-solving abilities. Personalized growth is promoted through adaptive learning, which adapts education to student success. These programs engage students in practical exercises that help them understand abstract ideas and get ready for a world driven by technology. Students learn abilities that go well beyond what is typically taught in the classroom thanks to robotics, which turns the classroom into a hive of exploration and invention.

Overcoming Challenges and Considerations

While the use of robotics in education has great promise, there are a number of difficulties that must be overcome. All students, regardless of socioeconomic circumstances, may benefit from robotic learning by ensuring access and fairness. To equip educators to successfully integrate robots into curriculum, thorough teacher training and continued professional development are essential. Care must be taken to balance ethical issues like data privacy and AI ethics. Robotics may continue to be a potent teaching tool that prepares kids for a technologically sophisticated society while fostering inclusiveness, responsible use, and informed decision-making by balancing these difficulties with the advantages.

Case Studies: Success Stories in Robotic Education

Robotic assessment at university of hertfordshire, uk (2016):.

In 2016, the University of Hertfordshire in the UK announced an inventive use of robots in teaching. The university unveiled "Baxter," a cutting-edge robot, and used its skills to transform the evaluation of student assignments. With the help of Baxter's programming, it was able to assess programming projects for a computer science course and provide illuminating comments to improve the understanding and engagement of the students.

Interactive Learning with 'Mitra' at Amity International School, India:

With the launch of "Mitra," Amity International School in Noida, India, began a trailblazing path in education. As an interactive link between teachers and students, this humanoid robot actively engages pupils. Mitra, which is created to respond to inquiries and deliver information, improves the educational process by raising student participation and promoting lively dialogues.

AI-Powered Educator at Indus International School, Bangalore (2022):

By announcing an AI-powered teacher in 2022, the Indus International School in Bangalore created history and made a significant step toward the incorporation of AI. This ground-breaking robot teacher, who is 5 feet 7 inches tall, is an expert at teaching physics, math, and chemistry. It transforms the landscape of traditional education as a trailblazing robotic instructor and personifies the next era of technology-driven learning.

Robotics Training at Woxsen University's AI Research Centre, Hyderabad, India:

The AI Research Centre at Woxsen University is a centre for innovation where prospective engineers may learn useful robotics abilities. Students work on projects like Industrial Arm Robots and Navibot here, fully immersing themselves in the world of robotics. The AI Research Centre enables students to develop and build useful robots via hands-on instruction, influencing the direction of robotics and technology.

Looking Ahead: Future Trends in Robotic Education

The potential for robotic education's future are tremendous. AI and machine learning integration provides tailored, flexible learning opportunities that adjust training to student requirements. Students will be immersed in interactive, simulated worlds thanks to virtual and augmented reality, which will increase engagement and comprehension. Innovative curriculum will be driven by partnerships between educators, researchers, and business professionals, preparing students for changing employment markets. As robots develop, educators must take advantage of these trends to make sure technology fosters critical thinking, enriches learning, and gives kids the skills they need to survive in a constantly changing technological environment.

In summary, the adoption of robotics in education signals a seismic shift in how we educate people. Robotics improves student engagement and skill development via practical application, problem-solving, and interdisciplinary inquiry. Although issues like equality and ethical concerns demand close monitoring, there is much promise for inclusive, dynamic education. The chance to embrace this technological transformation must be seized by educators, policymakers, and stakeholders as robots leads the way for individualized learning, collaborative creativity, and future-ready skills. Robotics in education moves us closer to a better, more technologically advanced future by raising a generation able to navigate the challenges of the future.

References:

• https://issuu.com/uniofherts/docs/headlines_2015
• https://india.postsen.com/sports/865732.html#:~:text=Bangalore%3A%20Indus%20International%20School%2C%20Bangalore,the%20rounds%20on%20social%20media
• https://www.linkedin.com/posts/drhemachandrank_woxseninnovation-futuretech-aiinnovation-activity-7094352550266609664-GeVY
• https://www.linkedin.com/feed/update/urn:li:activity:7087741523366187008

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101+ Robot Ideas For School Project (2024)

School projects have evolved over the years, embracing modern technology to engage students in exciting ways. One such avenue is robotics, which not only captivates young minds but also nurtures essential skills like problem-solving, creativity, and teamwork. In this blog, we’ll delve into various robot ideas for school projects, catering to students of different skill levels and interests.

Types of School Projects Involving Robotics

Table of Contents

Demonstrative Projects

Demonstrative projects are perfect for introducing students to the world of robotics. These projects often involve simple robotics demonstrations or hands-on activities that showcase basic robotic concepts.

For instance, building a small robot that follows a line or avoids obstacles can be both educational and entertaining.

Educational Projects

Educational projects aim to deepen students’ understanding of robotics principles. They may involve using learning kits to build and program robots, or engaging in projects that teach specific concepts such as sensor usage or coding.

These projects offer valuable opportunities for students to apply theoretical knowledge in practical scenarios.

Competitive Projects

Competitive projects add an element of excitement and challenge to robotics education. Participating in robotics competitions like FIRST LEGO League or VEX Robotics motivates students to push their limits, fostering collaboration and innovation.

These projects often require teams to design and build robots to complete specific tasks or challenges, providing a real-world context for learning.

What Is The Criteria for Selecting Robot Ideas For School Projects?

When selecting robot ideas for school projects, several factors should be considered:

• Grade Level: The complexity of the project should be suitable for the students’ grade level.
• Available Resources: Ensure that the necessary materials and tools are accessible within the school’s budget.
• Educational Objectives: Align the project with specific learning objectives, whether it’s introducing basic concepts or advanced problem-solving skills.
• Student Interests and Skills: Consider the interests and skills of the students to keep them engaged and motivated throughout the project.

101+ Robot Ideas For School Project For All Level Students

Beginner level.

• Line-following robot using infrared sensors
• Obstacle avoidance robot with ultrasonic sensors
• Light-seeking robot using LDR sensors
• Simple motorized robot with wheels
• Bristlebot: A tiny vibrating robot made with a toothbrush head
• Scribblebot: A robot that draws random patterns
• Bumper robot: A robot that changes direction upon collision
• Clap-controlled robot using sound sensors
• Handshake robot: A robot that responds to handshakes
• Edge-detecting robot for following edges of tables or walls
• Drawing robot that creates geometric shapes
• Thermobot: A robot that reacts to temperature changes
• DIY robotic insect
• Robotic caterpillar with moving segments
• Ping Pong ball launcher robot
• Shake-activated robot that moves when shaken
• Air-powered balloon car
• Robotic flower that opens and closes
• DIY mini sumo robot for small-scale competitions
• Bubble-blowing robot

Intermediate Level

• Remote-controlled robot with Bluetooth or RF module
• Robotic arm with multiple degrees of freedom
• Maze-solving robot using line following and decision-making algorithms
• Gesture-controlled robot using accelerometers or gyroscopes
• Voice-controlled robot with speech recognition
• Ball-balancing robot using PID control
• Soccer-playing robot with a kicking mechanism
• Arduino-based self-balancing robot
• Arduino-based obstacle course navigation robot
• Wall-following robot
• RC car converted into a robot with added sensors
• Arduino-based line follower with PID control
• Humanoid robot with basic movements
• Arduino-based temperature-controlled fan robot
• Gesture-controlled robotic hand
• Arduino-based robotic arm with servo motors
• Wireless surveillance robot with camera module
• Arduino-based color-sorting robot
• Arduino-based maze-solving robot with AI
• Arduino-based Bluetooth-controlled robot arm

Advanced Level

• Autonomous drone with GPS navigation
• AI-based self-learning robot
• Robotic bartender for serving drinks
• Arduino-based CNC robot
• 3D-printed robotic exoskeleton
• Arduino-based Rubik’s Cube solver
• Robotic pet with interactive features
• Voice-controlled home automation robot
• Arduino-based bipedal walking robot
• Arduino-based self-driving car
• Gesture-controlled robotic wheelchair
• Robotic snake with articulated body segments
• Arduino-based quadcopter with FPV camera
• Arduino-based robotic spider
• Arduino-based robotic vacuum cleaner
• Arduino-based robot for sorting recyclables
• Arduino-based robotic arm with computer vision
• Arduino-based robotic fish with swimming motion
• Robotic hand with tactile sensors
• Arduino-based humanoid robot with facial recognition

Miscellaneous

• Arduino-based weather monitoring robot
• Robotic plant watering system
• Arduino-based soil moisture monitoring robot
• Arduino-based obstacle avoidance robot car
• Arduino-based Bluetooth-controlled home automation system
• Arduino-based fire-fighting robot
• Arduino-based RFID door lock system
• Arduino-based solar tracking robot
• Arduino-based GPS tracker
• Arduino-based ultrasonic radar system
• Arduino-based wireless power transmission system
• Arduino-based automatic plant watering system
• Arduino-based line-following robot car with obstacle detection
• Arduino-based automatic pet feeder
• Arduino-based automated greenhouse system
• Arduino-based water level indicator and controller
• Arduino-based vehicle tracking and theft detection system
• Arduino-based home automation system with voice command
• Arduino-based vehicle accident detection with GPS and GSM
• Arduino-based anti-theft alarm system

Advanced Robotics Kits

• LEGO Mindstorms EV3 Robot Inventor Kit
• VEX Robotics Competition Super Kit
• Makeblock mBot Ranger Transformable STEM Educational Robot Kit
• Robolink CoDrone Pro Programmable Drone Kit
• UBTECH JIMU Robot Builderbots Series: Overdrive Kit
• Sphero RVR: All-Terrain Programmable Robot
• ELEGOO UNO R3 Smart Robot Car Kit V4.0
• Tinkering Labs Electric Motors Catalyst STEM Kit
• OWI Robotic Arm Edge Kit
• 4M Tin Can Robot Kit

DIY Robot Building Platforms

• Arduino-based robotics platform
• Raspberry Pi-based robotics platform
• LEGO Mindstorms robotics platform
• Makeblock robotics platform
• VEX Robotics platform
• Robolink robotics platform
• Tinkering Labs robotics platform
• OWI robotics platform
• ELEGOO robotics platform
• TETRIX robotics platform
• GoPiGo robotics platform
• Meccano robotics platform
• Tello EDU programmable drone
• DJI RoboMaster S1 educational robot
• Anki Cozmo AI robot

How To Implement Robot Ideas For School Projects?

To successfully implement a robotics project in school, follow these steps:

• Planning Phase: Define project objectives, set timelines, allocate roles, and plan resources.
• Building Phase: Gather materials, construct the robot, and test prototypes for functionality and performance.
• Programming Phase: Write code to control the robot’s behaviors, debug and optimize algorithms, and integrate sensors and actuators.
• Presentation and Documentation: Prepare project reports or presentations, demonstrate the robot’s capabilities, and document the design process for future reference.

How Can Robots Improve Education?

Robots have the potential to revolutionize education in numerous ways, offering innovative tools and approaches to enhance learning experiences. Here are several ways in which robots can improve education:

• Interactive Learning: Robots can engage students in interactive learning experiences, providing hands-on opportunities to explore concepts in science, technology, engineering, and mathematics (STEM) subjects. By interacting with robots, students can gain a deeper understanding of theoretical concepts through practical application.
• Personalized Instruction: Robots equipped with artificial intelligence (AI) can adapt to individual student needs, providing personalized instruction and feedback. These AI-driven systems can assess students’ strengths and weaknesses, tailor lessons accordingly, and offer targeted support to optimize learning outcomes.
• Enhanced Creativity: Robots can serve as tools for creative expression, allowing students to design, build, and program their own robotic creations. This hands-on approach encourages creativity, problem-solving, and innovation, as students experiment with different designs and functionalities.
• Real-World Applications: Robots provide a tangible connection to real-world applications of academic concepts. By working on robotics projects, students can see how theoretical knowledge translates into practical solutions, fostering a deeper appreciation for the relevance of their education.
• Collaborative Learning: Robotics projects often require collaboration and teamwork, encouraging students to communicate effectively, share ideas, and work together towards common goals. These collaborative experiences develop important social and interpersonal skills that are valuable both inside and outside the classroom.
• Critical Thinking and Problem-Solving: Robotics challenges students to think critically and solve complex problems. Whether debugging code, troubleshooting mechanical issues, or optimizing robot performance, students develop analytical skills and resilience in the face of challenges.
• Cross-Disciplinary Integration: Robotics projects naturally integrate various academic disciplines, including STEM subjects, computer science, design, and even humanities. This interdisciplinary approach promotes holistic learning and helps students make connections between different areas of knowledge.
• Inclusive Education: Robots can support inclusive education by providing alternative means of communication and interaction for students with diverse learning needs. For example, robots equipped with speech recognition and synthesis capabilities can assist students with speech or language impairments.
• Career Readiness: Exposure to robotics and related technologies prepares students for future careers in fields such as robotics engineering, computer science, automation, and artificial intelligence. By developing relevant skills and knowledge early on, students are better equipped to pursue advanced studies and enter the workforce.
• Inspiration and Motivation: The excitement of working with robots can inspire students to take a greater interest in STEM subjects and pursue further education and careers in related fields. Robotics projects spark curiosity, fueling a lifelong passion for learning and discovery.

Overall, robots have the potential to transform education by offering dynamic, engaging, and effective learning experiences that prepare students for success in a rapidly evolving world.

As educators continue to integrate robotics into their classrooms, they unlock new opportunities to empower students and shape the future of education.

Robotics projects offer a dynamic and engaging way to enhance education and develop essential skills in students. By exploring various robot ideas for school projects, students can unleash their creativity, problem-solving abilities, and teamwork skills while having fun. 

Whether it’s building a simple line-following robot or designing a sophisticated autonomous drone, the possibilities are endless. Encouraging students to embark on robotics projects not only enriches their learning experience but also prepares them for the challenges of the future.

So, let’s embrace the exciting world of robot ideas for school project in education and inspire the next generation of innovators and engineers.

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Systems Thinking Approach to Robotics Curriculum in Schools

• First Online: 12 July 2017

Cite this chapter

• Christina Chalmers 2 &
• Rod Nason 2  

3058 Accesses

9 Citations

This chapter presents a systems thinking approach for the conceptualization, design, and implementation of robotics curriculum to scaffold students’ learning of important Science, Technology, Engineering, and Mathematics (STEM) concepts and processes. This approach perceives the curriculum as a system of integrated elements and allows for the investigation of the interdependencies amongst the elements and the dynamics of the curriculum as a whole. Through this approach, we believe that students can be provided with robotics curriculum units that facilitate the learning of STEM “Big Ideas” of and about STEM. A STEM “Big Idea” is central to the understanding and application of STEM across a wide range of fields, one that links numerous STEM discipline understandings. Robotics is a rich context in which students can establish deep knowledge and robust understanding of STEM “Big Ideas”. Curriculum units based on this systems thinking approach can do much to ensure that students engaged in robotics activities focus not only on the completion of robotics tasks but also on the social construction of integrated networks of authentic STEM knowledge centred around “Big Ideas” of and about STEM.

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Elementary School Student Development of STEM Attitudes and Perceived Learning in a STEM Integrated Robotics Curriculum

Developing an Integrative STEM Curriculum for Robotics Education Through Educational Design Research

Between the social and the technical: negotiation of human-centered robotics design in a middle school classroom.

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Chalmers, C., Nason, R. (2017). Systems Thinking Approach to Robotics Curriculum in Schools. In: Khine, M. (eds) Robotics in STEM Education. Springer, Cham. https://doi.org/10.1007/978-3-319-57786-9_2

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