Introduction

Recent developments in robots have significantly impacted education. Educational robotics, encompassing various types of robots like social robots, has emerged as a powerful tool for enhancing learning experiences across different age groups, from preschool to high school students (Di Lieto et al., 2017; Pina & Rubio, 2017; Scaradozzi et al., 2015; W. Wang, 2016). The global educational robot market size is expected to grow from USD 1.4 billion in 2022 to USD 3.2 billion by 2027, at a CAGR of 17.3% during the forecasted period. The major drivers for the growth of educational robot market include increase in deployment of robots in manufacturing industries to promote new job opportunities, surging demand for collaborative robots in educational and industrial sector, and growing research and product development of humanoid robots to transform service sector (Mehra, 2022). Robots are being utilized in educational settings as teachers, teaching assistants, learning tools, and platforms, enhancing the overall learning process and engaging students in interactive and interdisciplinary activities (Pai et al., 2024). Moreover, the responsible design and use of social robots in primary education have been a subject of research, leading to the development of guidelines to ensure their ethical and effective implementation in classrooms.

Studies on robots in education have explored various aspects of their impact on learners and educators (Ali et al., 2022; Belpaeme et al., 2018; Kasuk & Virkus, 2024; Nagy & Holik, 2023; Zhang et al., 2024). These studies have delved into the effectiveness of educational robots in enhancing academic achievement, fostering creativity, and improving motivation among K-12 students. Research has shown that robots play a significant role in teaching mathematical problems (Ouyang & Xu, 2024), language skills (Wu & Li, 2024), analytical thinking (Fakaruddin et al., 2024), and communication skills (Lorenzo Lledó et al., 2024). Moreover, the use of social robots as teaching assistants has been found to positively influence children’s social-emotional development (Songkram et al., 2024; Xiong et al., 2024), although some concerns exist regarding excessive attachment to robots (Hyde et al., 2024; Maroto-Gómez et al., 2024). Furthermore, robots in education have begun to change the way education is delivered, integrating technology that improves the way learning and teaching are done (Rahiman & Kodikal, 2024). From the use of NAO’s robotic platform that increases the attention span of elementary school children to the application of augmented reality technology in teaching programming, these advanced technologies facilitate a more interactive and engaging learning environment (Rossi et al., 2024). In addition, the application of robotics in vocational training represents an important transition in the preparation of the future workforce, in line with the demands of modern industry (Herzog & Mina, 2024).

However, despite this growing body of literature, the field lacks a consolidated, large-scale synthesis that maps how educational robotics research has evolved over time, where research activity is concentrated, and how these developments relate to global educational priorities such as the Sustainable Development Goals (SDGs). Most prior reviews focus on pedagogical outcomes or specific case studies, rather than offering a macro-level, quantitative overview of the intellectual landscape of this research area.

To fill this gap, this study presents a comprehensive bibliometric analysis of educational robotics over the past decade. It provides a multi-dimensional mapping of the field using three visualization tools and examines trends, author networks, thematic developments, and research productivity across countries and journals. This study’s novelty lies in its integration of bibliometric insights with an SDG-aligned framework, offering a unique and future-oriented understanding of the field’s contribution to both scholarship and global educational goals. To achieve these aims, this study addresses the following key research questions:

  1. 1.

    What are the trends in the development of robot research and education over the past 10 years?

  2. 2.

    Which countries, authors, journals, affiliations, and citations are most prolific?

  3. 3.

    What are the trends in the development of research based on keywords and what are the future research opportunities related to this topic?

  4. 4.

    What types of documents, research areas and research domains are the most productive, and how do they relate to the Sustainable Development Goals?

Method

This study employs a bibliometric analysis to evaluate the evolution of robotics in education by analyzing documents indexed in the Social Sciences Citation Index (SSCI) from the Web of Science Core Collection. Since this research involved secondary data and did not engage human participants, no ethics approval was required. The methodological framework follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Moher, 2009), to ensure a structured and replicable screening and selection process (Fig. 1).

Fig. 1: Diagram PRISMA (preferred reporting items for systematic reviews and meta-analyses).
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This figure illustrates the PRISMA flow of the systematic review process for selecting literature on robots in education from the SSCI (Web of Science) database.

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Figure 1 outlines the PRISMA flowchart used to guide the screening process. From the initial records retrieved through the keyword search, duplicates were removed before proceeding to the title and abstract screening stage. Articles were then assessed for eligibility based on the inclusion and exclusion criteria detailed in Table 1. This process ensured that only peer-reviewed, English-language studies focused on robotics in education were included. The flowchart illustrates the number of records excluded at each stage and the final dataset used in the bibliometric analysis.

Table 1 Inclusion and exclusion criteria used in this study.
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Literature search

A comprehensive literature search was conducted using the SSCI database on April 7, 2024. The keywords used were: “Robots in education” OR “Robot education” OR “Robotics education”, searched in the Topic field (including title, abstract, and keywords). These terms were selected to broadly capture the intersection of robotics and educational applications across disciplines such as computer science, pedagogy, cognitive science, and engineering. This approach ensures wide coverage of empirical research on robotics in formal and informal educational settings, particularly studies relevant to learning outcomes, teaching practices, and curriculum innovation.

Publication identification and inclusion criteria

To ensure replicability and relevance, this study applied clearly defined inclusion and exclusion criteria for selecting publications (Atkinson et al., 2015). These criteria help standardize the selection process, ensuring that included studies are methodologically sound and aligned with the research objectives (Higgins & Altman, 2008; Higgins & Deeks, 2008). Exclusion criteria were equally essential to eliminate studies with insufficient data, poor methodology, or lacking direct relevance to the topic, thereby preserving data quality and analytical validity (Liberati et al., 2009). To operate the selection process, this study defined clear inclusion and exclusion criteria (Table 1). These criteria were applied during the screening phases detailed in the PRISMA flowchart to ensure consistency and rigor in study selection.

To ensure the consistency and reliability of the bibliometric data, several validation steps were conducted. Duplicate records were automatically filtered by the Web of Science database and then manually rechecked to avoid redundancy. Author names and institutional affiliations were standardized to correct variations due to spelling inconsistencies, abbreviation differences, and name disambiguation issues.

Final sample of publications and data extraction

After applying the inclusion and exclusion criteria outlined in Table 1, a total of 1120 records were retained for bibliometric analysis. This rigorous selection process ensured that only high-quality and relevant studies were analyzed, enhancing the validity and generalizability of the findings. A total of 173 records were excluded for not meeting the required standards. The methodological approach in this study builds upon the bibliometric framework previously developed by Nuryana (Nuryana et al., 2023), which utilized the Scopus database and Bibliometrix tool, but is expanded in this study through the use of the SSCI Web of Science database and the integration of three bibliometric tools. Detailed modifications, as illustrated in Fig. 2.

Fig. 2: The research design includes research questions, research methods, and tools.
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This figure outlines the overall research design used in the bibliometric study. It begins with the selection of the Web of Science Core Collection, specifically the Social Science Citation Index (SSCI), as the data source. The design is structured around four research questions (RQ1 to RQ4), each linked to specific bibliometric methods: bibliographic coupling, co-author analysis, co-occurrence analysis, and conceptual analysis. Corresponding tools employed for analysis include VosViewer, bibliometric.com, and Biblioshiny.

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Figure 2 provides a detailed and methodological blueprint for executing a bibliometric analysis using the SSCI Web of Science database (Clarivate, n.d.). These databases were selected due to their extensive coverage of peer-reviewed academic literature, ensuring access to a broad and credible array of articles relevant to the study of robotics in education. The initial step of selecting these databases is crucial as it sets the foundational stage for gathering diverse and pertinent data, essential for a comprehensive analysis over the specified ten-year period. Following the database selection, the research framework is carefully laid out to align with four distinct research questions, each aimed at exploring different facets of robotics research in education. Each research question is paired with a specific methodological approach and corresponding analytical tools to optimize data extraction and analysis. The application of these specific tools bibliometric.com (Guang et al., n.d.), VosViewer (van Eck & Waltman, 2010), and Biblioshiny (Aria & Cuccurullo, 2017) enables the handling of large datasets and complex analyses, facilitating a thorough and insightful examination of the data. These tools are integral to mapping out the bibliometric landscape, allowing researchers to visualize data connections and extract meaningful patterns and trends.

Results and discussion

Trends in robotics research and education over the past decade

Over the past decade, robotics research and education have seen transformative growth and evolution (Lin et al., 2012), as depicted in the comprehensive data from Fig. 3. Figure 3 illustrates the annual trends in the number of publications and citations related to robotics in education from 2015 to 2024. A closer analysis of these trends provides a deeper understanding of the dynamics within this burgeoning field. A ten-year period (2015–2024) offers a robust time frame to capture significant shifts in robotics education, particularly given the rapid rise of technologies like AI and automation.

Fig. 3: Trends in article distribution in terms of publications and citations 2015–2024.
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This figure presents the annual distribution of publications (light purple bars) and citations (dark blue line) related to robots in education over a ten-year period from 2015 to 2024. The number of publications shows a consistent increase from 2015, peaking at 225 in 2021, while citations sharply rise, reaching the highest point in 2023 with 6794 citations. A noticeable decline in both publications and citations is observed in 2024, possibly due to the partial year data.

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Starting in 2015, the number of publications and citations was modest, with only 13 publications and 26 citations. Over the next few years, both metrics saw a gradual increase. By 2018, the publications had increased significantly to 64, alongside a notable rise in citations to 228. The period from 2019 to 2021 marks a significant surge in activity, with publications rising sharply from 110 in 2019 to a peak of 225 in 2021. Citations mirrored this growth, escalating dramatically to reach 3,564 in 2021. The prolific rise in citations indicates that the research being published was not only more abundant but also of significant influence and relevance to the field. However, the year 2022 shows a surprising trend with a steep decline in citations to 3,355 despite the number of publications holding relatively stable at 193. This could suggest a saturation point in research themes or possibly a shift in research quality or novelty, which might affect the impact of citations. Despite this, the number of publications remained robust, indicating ongoing interest and continuous output in robotics education research.

The year 2023 continued to see a high level of publications at 194, although the citations dramatically decreased to 1459. Entering 2024, the data shows a significant reduction in both publications and citations, with only 46 publications and 679 citations. The potential for an increase in the number of documents and citations in 2024 is definite, as the year has not yet concluded. This will certainly result in a rise in both articles and citations. Understanding these trends is crucial for predicting future developments in robotics education and aligning educational strategies to harness the full potential of robotics in enhancing learning outcomes. This decade-long journey underscores the dynamic and evolving nature of robotics research in educational settings, highlighting its impact and the shifting landscapes of academic focus and technological integration.

Meanwhile, this study also analyzes trends in robotics research using bibliographic coupling analysis with documents as the unit of analysis. Of the 1120 documents, a minimum of 53 citations per document was set as the threshold, resulting in 100 documents meeting this criterion. The density visualization created using VOSViewer (Fig. 4) highlights influential authors and collaboration patterns within the field of robotics education.

Fig. 4: Density visualization of authors in robotics education using VOSViewer.
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This figure illustrates the density of influential authors in the field of robotics education, generated using VOSviewer. The color gradient from blue to yellow indicates the intensity of co-citation links, with yellow areas representing authors with higher citation density and stronger relevance in the research field.

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In Fig. 4, authors like Belpaeme, Shute, Potkonjak, Sheridan, and Hsu appear in dense yellow zones, indicating both strong citation records and centrality in collaboration networks. In contrast, names like Lun (Lun & Zhao, 2015) and Ivanov (Ivanov et al., 2019) appear in less dense areas, suggesting more niche or emerging research trajectories. This spatial distribution helps identify established scholars as well as areas ripe for further investigation. While the density visualization highlights influential authors and clusters of collaboration, a deeper interpretation reveals structural and pedagogical implications. The dominance of certain author networks, primarily from North America, Western Europe, and East Asia, reflects not only academic productivity but also disparities in access to research funding, robotics infrastructure, and institutional support. This mirrors global imbalances in educational technology adoption and innovation investment, especially between the Global North and South (S. M. Lee & Trimi, 2021).

Key contributors in robotics education research

In the ever-evolving field of robotics in education, identifying the key contributors, be it countries, authors, journals, or academic affiliations, plays a crucial role in understanding the landscape of research and its global impact. This study analysis seeks to uncover which entities are the most prolific in terms of publishing, citation, and influence within this specialized domain. By examining the volume of publications, the frequency of citations, and the breadth of distribution across various journals, we can pinpoint the leaders in the field and assess their contributions to the advancement of robotics education. This study's focus extends to discerning the geographic distribution of research output, which reveals the global reach and regional strengths in robotics education research. By analyzing the countries and institutions that produce the most research, this study gains insight into where significant funding, resources, and academic interest are concentrated. Additionally, understanding which journals frequently publish this research helps identify the platforms that are most influential in disseminating knowledge in this area. Moreover, by delving into citation metrics and authorship patterns, this study can identify which authors and affiliations are at the forefront of this field. Citations serve as a proxy for the impact and recognition that particular studies receive from the wider academic community, whereas prolific authors and affiliations highlight the centers of academic excellence and innovation in robotics education.

The international collaboration network in robotics education is illustrated in Fig. 5. This visualization illustrates how different countries are connected through lines that represent research collaborations. Each country is represented by a segment on the circle, and the size of these segments indicates the level of scientific participation or output of that country in the analyzed field. The colored lines indicate the country of origin of the collaboration. For example, a line connecting the USA and Germany indicates a research collaboration between these two countries. This shows that countries with larger segments such as the USA, Germany, and China have extensive collaboration networks and actively participate in joint research with many other countries, reflecting their roles as centers of excellence and innovation in the robotic and education research. Moreover, visualization also highlights less visible inter-country relationships; countries with smaller segments may not be as active as the larger ones but still play a significant role in the global collaboration network. These connections underscore the importance of international cooperation and how knowledge and expertise are shared across borders. Through such visualizations, we can gain a better understanding of the global dynamics in research and development and assess the potential for future collaborative initiatives.

Fig. 5: International collaboration network in robotics education research.
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This figure presents a circular collaboration map illustrating the international networks in robotics education research. Each segment represents a country, with connecting lines indicating collaborative relationships.

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Meanwhile, Table 2 outlines significant data regarding the top 10 countries and institutions contributing to a certain field, as evidenced by their total publications (TP) and total citations (TC). The USA leads among the countries with an impressive total of 252 publications, which have amassed 7207 citations, highlighting its dominant role in this scholarly domain. Following the USA, China holds the second position with 176 publications and 2138 citations, indicating substantial contributions as well. Other countries listed include England, Spain, and Taiwan, each showcasing considerable academic output and impact in terms of citations received. On the institutional side, the Education University of Hong Kong stands out with 25 publications and 328 citations, indicating a strong influence within the Hong Kong academic scene in robotic and education. Another notable institution is the National Taiwan Normal University, also with 25 publications but a higher citation counts of 502, underscoring its significant impact. The University of Hong Kong and Beijing Normal University are also highlighted, with respective contributions to the field that underscore the importance of these educational institutions in advancing research on robotic and education.

Table 2 Top 10 Contributing Countries and Institutions.
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The dominance of countries such as the USA, China, and Germany in robotics education research can be attributed to their substantial investments in STEM education, robust university research ecosystems, and strong industrial ties to technological innovation. As highlighted by Fan et al., countries like China have rapidly increased the quantity and quality of international collaborations due to national policies prioritizing global research visibility and infrastructure funding (Fan et al., 2022). Moreover, collaboration patterns shown in Fig. 5 reflect both geopolitical relationships and research capacity. Nations with strong academic institutions often serve as hubs, while others participate through regional or bilateral ties. For instance, international research partnerships, particularly between universities in developed and developing countries, often emerge to compensate for limitations in local infrastructure, gain visibility, and access cutting-edge tools (Wyne, 2015). These findings imply that future research planning, especially for developing countries, should foster policies supporting research mobility, digital infrastructure, and joint funding schemes to encourage more equitable collaboration networks.

Furthermore, the analysis of journals that contribute to the publication of research on robotics in education is presented in Table 3. The diversity and range of journals contributing to robotics-related educational research highlight the interdisciplinary and dynamic nature of this field. Publications from these journals are not only numerous but also widely cited, indicating their crucial role in advancing the understanding and implementation of robotics in educational settings. Table 3 provides a detailed overview of the academic journals that are most influential in publishing articles related to robotics in education. The journal “Education and Information Technologies” leads the list with a total of 71 publications, accumulating 570 citations and a total link strength of 315, which suggests a strong relevance and connectivity within related literature. Following closely, “Sustainability” showcases its interdisciplinary nature by contributing 54 publications specifically to the field of robotics in education, garnering 579 citations, although with a significantly lower link strength of 123. The “International Journal of Social Robotics” stands out not just for its volume of publications, with 46 papers, but more notably for its high citation count of 1067. This indicates that the work published in this journal has a profound impact on the field, emphasizing the importance of the social dimensions of robotics in educational contexts. Meanwhile, “Interactive Learning Environments” and “Computers & Education” also make significant contributions, with “Computers & Education” having an exceptionally high citation count of 2650, which suggests that its publications are highly influential and foundational within educational technology research.

Table 3 Journals contributing to robotics-related educational publications.
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Other journals such as “Frontiers in Psychology” and “Journal of Educational Computing Research” show diverse contributions, with total publications of 33 and 31, respectively. Their respective citations and link strengths further illustrate their specific roles and influence within the academic community studying robotics in education. “Frontiers in Psychology” explores the psychological aspects of education technology, while “Journal of Educational Computing Research” focuses on computing and its application in educational settings. Additional journals like “International Journal of Technology and Design Education” and “Computers in Human Behavior” underline the interdisciplinary nature of robotics in education, blending technology design, human interaction, and educational outcomes. The former has published 28 articles with a solid citation count and link strength, suggesting a robust integration of technology design principles in educational contexts, whereas the latter, despite fewer publications, has garnered a high number of citations, reflecting significant influence.

The influence of certain journals, such as Computers & Education and International Journal of Social Robotics, suggests that robotics in education is no longer viewed as a niche topic but as an interdisciplinary area attracting attention from both educational researchers and computer scientists. The high citation count in these journals underscores the field’s maturation and its relevance to broader pedagogical and technological discussions.

Furthermore, it is also important to analyze citations. Citation analysis is an examination of the frequency, patterns, and graphs of citations in articles. Table 4 lists the most globally cited documents on robotics in education, focusing on their impact through citation metrics. At the top of the list is Belpaeme, an article titled “Social robots for education: A review,” which leads with 601 total citations and a high citation rate per year, indicating its significant influence and relevance in the field. The article notably has a high normalized citation count, underscoring its broad acceptance and applicability in academic discussions around social robots’ role in educational environments. This paper, along with others on the list, highlights the growing interest and pivotal research on how robots can enhance educational processes.

Table 4 Most global cited documents on robotics in education.
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Following Belpaeme, Shute VJ's article “Demystifying computational thinking” has garnered 455 citations, reflecting its crucial role in clarifying the concepts and applications of computational thinking within educational settings. This paper has an exceptionally highly normalized citation count, suggesting it has been extremely influential per year since its publication. Similarly, Sheridan article from 2016 discusses the status and challenges of human-robot interaction, accumulating significant citation metrics that reflect ongoing interest and the evolving nature of robotic integration in educational contexts. Further down the list, articles like Atmatzidou S and Chen G explore specific educational outcomes linked to robotics and computational thinking, particularly focusing on demographic factors such as age and gender, as well as practical applications in elementary education. These studies have fewer citations than the top-ranked articles but still hold substantial academic value, evidenced by their positive citation rates per year and normalized citations, indicating specific niches of influential research within the broader field.

Expanding on the citation analysis in Table 4, the most influential articles in the field have significantly shaped research directions in educational robotics over the past decade. Belpaeme et al., with their review on social robots in education, introduced a paradigm shift by highlighting how physical embodiment in robots enhances both cognitive and emotional learning outcomes. Their emphasis on the practical, ethical, and pedagogical limitations of integrating robots into classrooms has since spurred critical inquiries into the role of robots not just as tools, but as active agents in the educational process. This foundational work continues to inform studies that explore human-robot interaction, teacher-robot collaboration, and the social dynamics of robot-assisted learning.

In parallel, Shute et al. have been instrumental in defining and operationalizing computational thinking (CT) as a core educational objective. Their structured model of CT—decomposition, abstraction, algorithm design, debugging, iteration, and generalization—has become a reference point for assessing and embedding CT into robotics curricula. Follow-up studies, such as those by Atmatzidou and Demetriadis and Chen et al., have built on this foundation to examine age, gender, and assessment modalities in CT development through robotics. Meanwhile, Sheridan’s discussion of human–robot interaction (HRI) challenges introduced broader system design questions, influencing both technical implementations and ethical standards in robotics education. Collectively, these works have catalyzed a more critical, interdisciplinary, and learner-centered research agenda, positioning educational robotics as a dynamic field at the intersection of pedagogy, technology, and social science.

Keyword trends and future research opportunities in robotics education

In the dynamic field of robotics education, understanding the evolution of research through keyword analysis is essential for mapping out both current trends and future opportunities. This discussion aims to dissect the trajectory of keyword usage within scholarly articles, revealing how certain themes have gained prominence over time and predicting potential new areas of investigation. The analysis of keyword trends not only serves to illustrate the historical development of robotics education but also aids in identifying burgeoning topics that could shape the future of the field. Such insights are invaluable for researchers, educators, and policymakers as they strategize on how to best channel their efforts and resources to foster innovation and address emerging educational needs. By examining the keywords associated with high-impact research and the most discussed topics, this discussion can forecast the evolution of robotics education and its potential integration with other interdisciplinary areas. Analyzing the thematic evolution in the field of robotics education is crucial for several reasons, particularly when observing the transitions and developments over specific time points 2025–2024. The analysis in Fig. 6 uses author keywords with a number of Cutting Points of 3, 2017, 2019, and 2023.

Fig. 6: Thematic analysis with author keywords.
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This figure displays the evolution of research themes in robotics education from 2015 to 2024 through a Sankey diagram based on author keywords. The connecting lines illustrate the continuity and transition of research topics over time, showing how earlier themes evolved or converged into newer areas of interest. The color coding highlights different thematic groupings.

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The evolution of themes basic terms like “programming” and “robotics” in the period (2015–2017) to more complex and specialized themes such as “computational thinking” and “artificial intelligence” in later years (2020–2023) highlights the rapid integration of advanced technologies into educational systems. This shift indicates not only advancements in the technology itself but also an increasing emphasis on developing higher-order thinking skills among students through robotics education. Over time, the focus areas within robotics education have expanded to include “early childhood education” and “higher education”, showcasing a broadening of the application of robotics from primary elementary and secondary education to encompassing all levels of learning. This suggests a recognition of the importance of introducing robotics concepts early in educational curricula and continuing this exposure throughout a student’s academic journey, supporting lifelong learning and adaptability.

By the latest phase from 2020 to 2024, the focus expands significantly to include themes such as “meta-analysis,” “development,” and “higher education.” This expansion signals a reflective and evaluative approach to robotics education, where the outcomes and methodologies are critically analyzed. The emergence of “creativity” alongside ongoing themes like “computational thinking” and “human-robot interaction” suggests a synthesis of technical skills with soft skills, emphasizing a holistic approach to education. The keywords in this phase reflect a sophisticated understanding of the role of robotics in education, focusing not only on how these tools can enhance learning but also on how they can foster a broader set of cognitive and interpersonal skills. This evolution points to an increasingly complex and integrated view of robotics in educational research and practice, underlining its potential to significantly impact educational methods and outcomes.

Thematic evolution analysis using author keywords aids in predicting future trends, planning educational content, and aligning educational strategies with the latest technological capabilities and societal needs. Pedagogically, the concentration of themes such as computational thinking, collaborative learning, and early childhood robotics reflects the growing integration of constructivist and inquiry-based frameworks in educational systems. Robotics is increasingly positioned not only as a technical skill but also as a medium for developing problem-solving, creativity, and teamwork, skills prioritized by modern education agendas (Eguchi, 2022).

Meanwhile, from the co-occurrence analysis using author keywords with a minimum number of occurrences set at 5, out of the 3134 keywords, 161 met the threshold as shown in Fig. 7. Figure 7 organizes a diverse set of keywords related to robotics and education into four distinct clusters, each representing a unique focus within the interdisciplinary study of robotics in educational contexts. Cluster 1 (red color) emphasizes cutting-edge technologies and their educational applications. Keywords such as “artificial intelligence,” “augmented reality,” and “machine learning” suggest a focus on integrating advanced technological tools into learning environments. This cluster also includes terms like “child-robot interaction” and “educational robots,” indicating a direct application of robotics to facilitate interactive and personalized learning experiences. The incorporation of “emerging technologies” alongside “educational theory” such as “activity theory” illustrates the blend of theoretical and practical aspects of education technology.

Fig. 7: Co-occurrence network of study themes based on author keywords.
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This figure illustrates the co-occurrence network of keywords in robotics education research, as visualized using VOSviewer. The network is divided into four thematic clusters: Cluster 1 (red); Cluster 2 (green); Cluster 3 (blue); and Cluster 4 (yellow). Node size represents keyword frequency, while the proximity and links between nodes indicate the strength of co-occurrence relationships.

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Meanwhile, cluster 2 (green color) centers on educational methodologies and collaborative learning frameworks that utilize robotics. Keywords like “collaborative learning,” “educational robotics,” and “interactive learning environments” reflect the emphasis on using robotics to enhance student engagement and teamwork. There is also a strong focus on “21st-century skills,” highlighting the importance of preparing students for the modern workforce with skills like problem-solving and digital literacy. On the other hand, cluster 3 (blue color) focuses on the systematic implementation and evaluation of educational technologies. It includes “systematic review,” “teacher training,” and “evaluation methodologies,” indicating an emphasis on assessing the effectiveness of robotics in education. This cluster also explores the broader implications of technology in education with terms like “computing education” and “STEM education,” showing a commitment to integrating these technologies into general educational curricula. Finally, cluster 4 (yellow color) brings attention to basic educational concepts and early learning. It features terms like “early childhood education,” “primary school,” and “preschool education,” focusing on the introduction of robotics and programming at the earliest stages of education. Keywords such as “problem solving” and “coding” are crucial for developing foundational skills that support later academic and professional success.

The connections between these clusters are evident as they collectively emphasize a comprehensive approach to integrating robotics into educational settings from high-tech applications and pedagogical strategies to systematic assessments and foundational educational implementations. Looking to the future, research might explore the development of inclusive technologies that adapt to diverse learning needs, ensuring accessibility across different educational levels and settings. Another promising area could be the convergence of artificial intelligence with personalized learning paths in robotics education, potentially leading to more adaptive and responsive educational environments. These advancements could significantly enhance both teaching strategies and learning outcomes, making education more effective and engaging in the digital age.

Furthermore, the overlay visualization analysis is explained in Fig. 8. As technology continues to evolve, the intersection of robots and education presents a plethora of opportunities and challenges. Key concepts emerging in this context include Employment, Automation, Artificial Intelligence, Adoption, Scratch, and Curriculum (Fig. 8). Each of these keywords plays a pivotal role in shaping the future of education and how robotics can enhance learning experiences, develop essential skills, and prepare students for a rapidly changing technological landscape. The emergence of robotics education keywords in robots and education research represented by Employment keywords is interesting. The inclusion of employment as a keyword reflects the growing importance of equipping students with skills relevant to the future job market. Robotics education plays a critical role in preparing students for careers in fields such as automation, artificial intelligence, and advanced manufacturing (Pankratova et al., 2021; Sánchez-Rivas et al., 2023). By learning about robotics, students gain technical skills and competencies that are highly valued in the job market, enhancing their employability in a technology-driven economy (Curto & Moreno, 2016). This connection ensures that students are not only knowledgeable about current technologies but also prepared to adapt to future advancements.

Fig. 8: Overlay visualization analysis of study themes based on all keywords.
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This figure displays an overlay visualization of keywords in robotics education research using a time-gradient color scheme. Nodes represent keywords, and their size corresponds to frequency of occurrence, while links indicate co-occurrence relationships.

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On other hand, automation is closely linked to robotics and is an essential aspect of modern industry and manufacturing. Introducing concepts of automation in education through robotics allows students to understand how automated systems work and their applications in real-world scenarios. This knowledge is crucial for future engineers and technologists who will design and manage automated processes in various sectors, from manufacturing to healthcare. The integration of automation in the curriculum helps students comprehend the efficiency and innovation that robotics brings to industrial processes (Madaev et al., 2023). Moreover, it prepares them for the evolving job market by developing critical technical skills and competencies needed for modern industrial environments (Khan et al., 2022). Meanwhile, Artificial intelligence (AI) intersects significantly with robotics, especially in educational settings (Naqvi et al., 2023; L. Wang et al., 2023). Incorporating AI with robotics helps students understand the integration of machine learning, perception, and decision-making processes in robots (Barakina et al., 2021). This synergy provides students with a comprehensive understanding of how robots can perform complex tasks autonomously, preparing them for future advancements in AI and robotics industries. Learning about AI in the context of robotics equips students with a dual understanding of two cutting-edge fields that are shaping the future of technology.

Moreover, the rise of keywords such as artificial intelligence, augmented reality, and automation points to a convergence of robotics with frontier technologies. This trend underscores the urgency of equipping teachers with adequate training and of rethinking curriculum policies to prevent technological elitism and ensure equitable access to these innovations (Katyeudo & de Souza, 2022). Collectively, these findings imply that beyond descriptive mapping, educational robotics research must now contend with broader systemic questions: Who produces and accesses this knowledge? How can robotics support inclusive learning? And what policy frameworks are needed to ensure that robotics enhances, not deepens, educational inequities?

Furthermore, the keyword adoption suggests the increasing acceptance and integration of robotics in educational curricula (El-Hamamsy et al., 2021). The adoption of robotics in classrooms indicates a shift towards more interactive and technology-enhanced learning environments (Rao & Jalil, 2021). This integration helps in fostering an interest in STEM fields among students and promotes hands-on learning experiences, making abstract concepts more tangible and understandable (Govender & Govender, 2023). The widespread adoption of robotics in education signifies a growing recognition of its benefits in enhancing student engagement and learning outcomes. Afterward, Scratch is a visual programming language commonly used in educational settings to teach coding and computational thinking (Y.-D. Lee et al., 2016). Its relevance to robotics education lies in its ability to simplify the programming of robots, making it accessible to younger students. By using Scratch to program robots, students can develop their problem-solving and logical thinking skills in a user-friendly environment (Hsiao et al., 2022), which is crucial for early STEM education (Dúo-Terrón, 2023). Scratch’s intuitive interface allows students to grasp fundamental programming concepts, fostering a strong foundation for more advanced robotics and coding knowledge. This integration is particularly effective in enhancing students’ engagement and motivation in learning STEM subjects (Plaza et al., 2019).

On the other hand, the relationship between robots in education and the curriculum. The inclusion of curriculum as a keyword highlights the structured integration of robotics into educational programs (Schina et al., 2021). Developing a curriculum that includes robotics ensures that students receive a well-rounded education that encompasses both theoretical and practical aspects of technology and engineering (Bers, 2010). A comprehensive robotics curriculum prepares students for higher education and careers in STEM fields by providing them with the necessary skills and knowledge from an early age (Coxon et al., 2018; Oyebola Olusola Ayeni et al., 2024). A well-designed curriculum ensures that robotics education is not sporadic but an integral part of the learning experience, promoting sustained interest and proficiency in the subject. Moreover, this exploration opens doors to identifying unexplored or under-researched areas, setting the stage for future studies that could fill these gaps and advance our understanding of robotics in educational contexts. It prompts a proactive approach to research planning, allowing stakeholders to anticipate and react to changes in technology and educational demands effectively. Thus, this keyword-based analysis is not just a reflection on the past but a forward-looking tool that can help drive the next wave of educational innovations involving robotics.

Productive research domains and their alignment with sustainable development goals

In the pursuit of advancing education through technology, understanding the productive research domains within robotics and their alignment with Sustainable Development Goals (SDGs) is crucial. Identifying the types of documents, research areas, and research domains that are the most productive can provide valuable insights into how robotics in education contribute to broader global objectives. This analysis aims to explore these productive research domains and their relevance to the SDGs, offering a comprehensive overview of the intersection between educational robotics and sustainable development. By examining the relationship between innovative research in robotics and its potential to support sustainable development, we can better understand the contributions of educational robotics to achieving global sustainability targets.

Figure 9 presents a pie chart depicting the number of publications related to robotics in education, categorized by document types. The data indicate a significant variation in the volume of different types of documents produced in this research area. The majority of publications are articles, with a total of 968 documents. This dominance suggests that original research articles are the primary mode of dissemination for findings in robotics education. The high number of articles highlights the active and ongoing research efforts to explore various aspects of integrating robotics into educational settings. These articles cover a wide range of topics, including practical implementations, case studies, theoretical frameworks, and empirical studies on the effectiveness of robotics in enhancing learning outcomes. The second-largest category is review articles, with 144 publications. Review articles play a crucial role in summarizing and synthesizing existing research, providing comprehensive overviews of the current state of knowledge. The substantial number of review articles indicates a growing interest in consolidating findings from diverse studies to identify trends, gaps, and future research directions in robotics education. These reviews are essential for educators, researchers, and policymakers to make informed decisions based on accumulated evidence. With only 8 publications, proceedings papers represent a small fraction of the total publications. Proceedings papers typically originate from conferences and workshops, where preliminary findings and innovative ideas are presented. The low number of proceedings papers might suggest that while there are conferences addressing robotics in education, the bulk of detailed and peer-reviewed research is being published in journals rather than conference proceedings.

Fig. 9: The pie chart shows the number of publications produced according to the document types.
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This pie chart illustrates the number of publications classified by document type within the dataset. The majority are research articles (blue) totaling 968 publications, followed by review articles (yellow) with 144 publications. A small portion consists of proceedings papers (orange), indicating limited use of conference outputs in the analyzed body of literature. The chart visually emphasizes the dominance of original research articles in the field.

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Understanding the alignment of productive research domains with the SDGs provides valuable insights into the impact of robotics in education on global objectives. By examining the focus areas of these publications, we can better appreciate the role of robotics in addressing key challenges and promoting sustainable development in education and beyond. Figure 10 showing 500 out of 12 entries 338 record(s) (29.597%) do not contain data in the field being analyzed. Quality Education (SDG 4): The data reveal that the majority of publications, totaling 588, are aligned with SDG 4, Quality Education. This substantial number underscores the critical role that robotics plays in enhancing educational outcomes (Coeckelbergh, 2022). By incorporating robotics into education, students experience more interactive and hands-on learning, which fosters essential skills such as critical thinking, creativity, and problem-solving (Jurado et al., 2020; Wedeward & Bruder, 2002). The emphasis on Quality Education highlights a global commitment to leveraging technology to improve educational accessibility and quality.

Fig. 10: Publications related to robotics in education and their alignment with Sustainable Development Goals (SDGs).
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This bar chart displays the number of publications addressing robotics in education in relation to specific SDGs.

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Good Health and Well-Being (SDG 3): The second highest number of publications, with 131 documents, aligns with SDG 3, Good Health, and Well-Being. This connection suggests significant research into how robotics can assist in healthcare education (Iqbal et al., 2021) and promote well-being (Axelsson et al., 2024). Robotics applications in this domain include therapeutic robots (Hong et al., 2024), simulation tools for medical training (Mallela et al., 2022), and educational programs that use robotic technologies to teach health-related topics (Pham et al., 2022). These innovations enhance the training of healthcare professionals and improve patient care through advanced educational tools. Furthermore, there are 54 publications addressing SDG 9 (Industry Innovation and Infrastructure), which focuses on Industry Innovation and Infrastructure. Research in this area explores the role of robotics in industrial training (Liu et al., 2022) and fostering innovation within educational settings (Nagy & Holik, 2022). By integrating robotics into curricula, educational institutions can prepare students for careers in advanced manufacturing and industrial automation, thereby driving innovation and supporting the development of sustainable infrastructure.

Furthermore, Decent Work and Economic Growth (SDG 8): With 39 publications, SDG 8 emphasizes promoting Decent Work and Economic Growth. This research examines how robotics education equips students with skills relevant to the future job market (Paolillo et al., 2022), enhancing their employability and supporting economic growth (Kolmykova et al., 2020). Through robotics education, students develop technical competencies and digital literacy, which are essential for participating in a technology-driven economy and achieving sustainable economic development. On other hand, SDgs 12, Responsible Consumption and Production (SDG 12): Eighteen publications focus on SDG 12, which promotes Responsible Consumption and Production. This research explores how robotics can be utilized to educate students about sustainable practices and responsible resource management (Alam, 2021). Robotics projects that incorporate themes of sustainability encourage students to develop solutions that promote efficient and responsible use of resources, aligning educational outcomes with global sustainability efforts. Further, Reduced Inequality (SDG 10) and Other SDGs: Thirteen publications address SDG 10, which aims to Reduce Inequality. This research investigates how robotics can help bridge educational gaps and provide equal learning opportunities for all students, including those in underserved and marginalized communities (Sullivan & Umashi Bers, 2019). Additionally, there are smaller numbers of publications related to other SDGs such as Sustainable Cities and Communities (SDG 11), Zero Hunger (SDG 2), and No Poverty (SDG 1), showcasing the diverse applications of robotics in addressing various global challenges.

Finally, the conceptual synthesis illustrated in Fig. 11 successfully integrates four major thematic clusters, cutting-edge technologies, collaborative learning frameworks, systematic evaluation of educational technologies, and foundational learning concepts, mapped across a timeline from 2015 to 2024. Figure 11 also visualizes how key concepts such as “automation,” “curriculum,” and “artificial intelligence” are linked to global objectives, particularly SDG. Moving forward, it is essential to foster interdisciplinary strategies in educational robotics that incorporate technical, pedagogical, and socio-cultural dimensions. Emphasizing accessible, teacher-friendly frameworks grounded in real-world learning experiences will be key to sustaining robotics integration across education levels. By leveraging bibliometric insights and explicitly aligning future research with global educational priorities, educational robotics can serve as a strategic tool to amplify equity, innovation, and lifelong learning on a global scale.

Fig. 11: Conceptual synthesis model linking thematic clusters, temporal trends, and Sustainable Development Goals (SDGs) in educational robotics research.
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This figure illustrates the central role of educational robotics by mapping its thematic domains, temporal keyword evolution from 2015 to 2024, and its contributions to four SDGs, including Quality Education (SDG 4) and Industry, Innovation, and Infrastructure (SDG 9).

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Limitations and future study

Despite the comprehensive analysis conducted in this study using bibliometric analysis, there are limitations due to restricted information access. The study relied solely on the SSCI Web of Science database to identify publications for bibliometric analysis. Utilizing other databases, such as Scopus, ERIC, and IEEE Xplore Digital Library, could have offered different perspectives and potentially yielded varying results. However, relying exclusively on the SSCI Web of Science may introduce sampling bias. The SSCI tends to favor English-language and Western-published journals, potentially underrepresenting valuable research from non-Western or non-English-speaking countries (Alonso-Álvarez, 2024). This may lead to a skewed picture of global contributions to educational robotics, particularly overlooking scholarship published in regional databases or conference proceedings. To address this, future studies are encouraged to triangulate findings using additional databases such as Scopus, which has broader journal coverage, and IEEE Xplore, which is particularly strong in engineering and robotics research. Combining multiple sources can reduce disciplinary and linguistic bias and yield a more comprehensive understanding of the field (Chansanam & Li, 2025).

The findings of this bibliometric analysis will be instrumental for policymakers, educators, and researchers by providing a consolidated view of the educational robotics landscape. It will pinpoint the leading contributors to the field and highlight the most impactful studies, thereby offering a benchmark for academic and practical applications. Furthermore, the identification of key research areas and gaps will assist in directing future studies towards areas that are poised for significant developments but are currently under-researched. Ultimately, this research aims to enhance the understanding of how robotics is shaping educational practices and outcomes. By mapping the evolution of the field and its current status through bibliometric indicators, this study will contribute to a more structured and focused development of educational robotics. This will not only foster innovation in teaching and learning practices but also ensure that the deployment of robots in educational settings aligns with ethical standards and maximizes benefits for student engagement and learning.

Conclusion

Recent advancements in robotics have profoundly influenced education, making educational robotics a powerful tool for enhancing learning experiences across various age groups. The global educational robot market is expected to grow substantially, driven by factors such as increased deployment in manufacturing, demand for collaborative robots, and advancements in humanoid robots. Over the past decade, the integration of technologies like artificial intelligence, programming, and automation into curricula has expanded the scope of research and transformed teaching methods, creating a more engaging and effective learning environment. Key contributors, including prolific authors, journals, and institutions, have driven innovation and collaboration, significantly influencing the development of robotics education. Furthermore, keyword trends and themes analysis has highlighted emerging research opportunities and gaps, aligning research outputs with broader societal and developmental goals. Understanding the alignment of this research with the Sustainable Development Goals (SDGs) provides valuable insights into its global impact. Most publications align with SDG 4, which underscores the role of robotics in enhancing educational outcomes through interactive and hands-on learning. Other significant SDGs addressed include SDG 3, SDG 9, and SDG 8. Robotics education also promotes SDG 12 and reduces inequality (SDG 10) by fostering sustainable practices and providing equal learning opportunities. The diverse applications of robotics in addressing global challenges highlight the importance of continued research and innovation in educational robotics, guiding future efforts to harness its full potential in enhancing educational outcomes and promoting sustainable global progress.