ABSTRACT The rise of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is accelerating the integration of social robots into education. These technologies enhance robots' abilities in natural language interaction, adaptive behaviour, and personalised learning support. To advance real-world implementation, it is essential to identify the main challenges and opportunities in this field. We conducted a two-round Delphi study with 16 experts in human-robot interaction and educational technology. In the first round, participants outlined opportunities, challenges, and potential robot roles expected in the short term (1 year) and medium term (5 years). Content analysis revealed 8 opportunities, 10 challenges and 10 roles. In the second round, experts ranked their importance and feasibility across both time horizons. The results show that the most critical opportunities and challenges are also the least feasible to achieve in practice. Conversely, the proposed roles of educational robots demonstrated alignment between importance and feasibility. Experts highlighted three promising roles for robots in the GenAI era: supporting teachers in boosting learner engagement, serving as conversational interfaces for students to access knowledge and assisting teachers in supporting disadvantaged learners. These findings provide a roadmap for prioritising feasible innovations in educational robotics.

ABSTRACT Traditional university-enterprise partnerships face challenges of fragmented oversight and skill mismatches. This study addresses these issues by incorporating industry associations as third-party mediators with dual public-private attributes for quality assurance in interdisciplinary postgraduate education. Grounded in Stakeholder Theory, the framework resolves systemic challenges through three core innovations: Institutionalizing recursive feedback mechanisms connecting policy interpretation, competency validation, and innovation diffusion; Operationalizing the PDCA cycle to synchronize educational objectives with industrial advancements; and Establishing bidirectional accountability channels among universities, enterprises, and associations to align educational outputs with market demands. A robotics education case study demonstrates empirical validation of measurable outcomes: graduates achieved salaries 29% above provincial averages, secured 139 provincial/national innovation awards, and obtained $1.37 million in collaborative R&D funding. These achievements validate the model's effectiveness in bridging academia and industry sectors via policy alignment, resource integration, and dynamic feedback mechanisms. Association-led coordination effectively cultivates interdisciplinary talent while driving technological progress. This paradigm provides a replicable blueprint for enhancing postgraduate education, with implications for aligning higher education globally with industrial modernization.

The integration of artificial intelligence (AI) in educational robotics has the potential to enhance learning experiences and foster engagement in school environments. However, the acceptance of AI-driven educational robots by students and educators remains underexplored. This study investigates the perceptions and acceptance of AI-enhanced educational robots in educational settings through a structured survey targeting both students and teachers. Data were collected on factors such as perceived usefulness, ease of use, and willingness to adopt AI-driven robots in classroom activities. The results provide insights into key determinants influencing acceptance and highlight potential challenges and opportunities for implementing AI-based robotics in education. Findings from this research contribute to understanding how AI-driven educational technologies can be effectively integrated into school curricula, supporting informed decision-making for educators and policy-makers.

The advancement of digital technology has driven a transformation in elementary education toward more interactive and collaborative learning aligned with 21st-century competencies. This study conducts a systematic literature review of publications from 2020 to 2025 to map the implementation of digital-based STEM in elementary schools, the learning models employed, the impacts on student outcomes, and the challenges encountered. Article searches were conducted in Google Scholar and the SINTA-indexed database, following the PRISMA 2020 guidelines, yielding 10 studies for thematic analysis. The review indicates the use of interactive e-modules, digital videos, learning applications, virtual laboratories, and educational robotics, which generally enhance learning outcomes, creativity, numeracy literacy, critical thinking, and computational thinking. Key challenges include limited infrastructure, teacher readiness, and inconsistent quality of digital media. The findings underscore the need for policy support, improved teacher competencies, and higher-quality digital learning resources to optimize the integration of digital-based STEM in elementary education.

Addressing the issues of insufficient behavioral stability in Braitenberg vehicles within complex light fields and obstacle-laden environments, this paper adopts tabular Q-learning to learn phototaxis and obstacle avoidance strategies. The vehicle constructs discrete states based on the intensity of left and right light sensors and collision flags, utilizing actions including moving forward, turning left, turning right, and stopping. Reward shaping is employed to encourage approaching the light source while penalizing collisions and ineffective idling. The strategy employs an-greedy approach, with decay applied to both the learning rate and exploration rate during training, while a discount factor balances long-term returns. Moderate domain randomization is used during training to close the gap between simulation and reality, and safety shielding is used in the early stages to keep high-risk actions to a minimum. Experiments conducted in a two-dimensional grid environment with random obstacles demonstrate that the algorithm converges within thousands of steps, significantly reducing the average number of collisions per episode and markedly improving the success rate of phototaxis. This paper provides a minimal reproducible implementation and key hyperparameter settings, offering a concise and effective baseline for low-cost mobile robot teaching and Braitenberg behavioral research.

This research presents the documentation and design of a project that seeks to collaborate with socially vulnerable institutions at the basic education level to enable children and adolescents to interact with technology through educational robotics [STEM knowledge]. It is approached using a mixed methodology, with case studies and field diary data collection instruments. Three phases of the project are described: the pedagogical framework for the educational robotics workshop, sample mobile robotics classes for elementary schools, and the introduction of robotics competitions as a motivational strategy. It concludes that the children who attended RoboMatrix saw themselves reflected in the children from the participating elementary schools, and the challenge of competing in upcoming events with their own robots becomes more familiar to them and a challenge to overcome. It was an important experience for approaching technology through robotics.

This article presents the educational experience developed since 2018 in robotics courses for children aged 3 to 15 in Tehuacán, Puebla. With a playful and age-segmented approach, accessible materials such as Arduino Nano, 3D printing, and sensors are used, and practical learning through competitions is prioritized. The adapted projects include Escornabot for the youngest, Bluetooth-controlled mobile robots for middle ages, and app controlled boats for older participants. This experience has proven sustainable, motivating, and effective in promoting STEM skills, creativity, and teamwork.

The integration of Artificial Intelligence (AI) into early childhood education presents new opportunities and challenges in fostering cognitive, social, and emotional development. This theoretical discussion synthesizes recent research on AI’s role in personalized learning, educational robotics, gamified learning, and social-emotional development. The study explores theoretical frameworks such as Vygotsky’s Sociocultural Theory, Distributed Cognition, and the Five Big Ideas Framework to understand AI’s impact on young learners. AI-powered personalized learning platforms enhance engagement and adaptability, while robotics and gamification foster problem-solving and collaboration. Additionally, AI tools support children with disabilities, promoting inclusivity and accessibility. However, ethical concerns related to privacy, bias, and teacher preparedness pose challenges to effective AI integration. Furthermore, the long-term effects of AI on children’s social skills and emotional intelligence require further investigation. This theoretical discussion emphasizes the need for interdisciplinary collaboration to develop AI-driven educational strategies that prioritize developmental appropriateness, equity, and ethical considerations. The findings highlight AI’s potential as a transformative educational tool, provided it is implemented thoughtfully and responsibly. The paper aims to address the following research question: How can artificial intelligence (AI) be meaningfully and ethically integrated into early childhood education to enhance learning, while preserving developmental and relational values?

The need to strengthen digital literacy in early childhood education requires teachers to have an adequate understanding of practical coding learning. However, limited competencies and facilities remain an obstacle in many schools. This workshop program aims to improve the knowledge and skills of teachers at Melati Purwakarta Kindergarten through an introduction to simple robotics based on Scribble Bot. The research used a qualitative descriptive method through the stages of material delivery, mentoring, and direct practice. Data was collected through observation, interviews, and documentation, then analyzed using thematic analysis through the processes of coding, categorization, and theme extraction. The findings show that: (1) all participants were able to assemble the Scribble Bot independently, indicating an improvement in technical skills; (2) pedagogical understanding of the role of coding in children's cognitive, motor, and social development increased significantly; and (3) a commitment to sustainable implementation emerged in the form of plans to integrate simple robotics into monthly learning. This program has implications for increasing teachers' readiness to adopt more innovative coding learning and emphasizes the need for facility support and further training so that the integration of robotics in early childhood education can be sustainable.

This study aims to develop an educational robot medium as a learning tool for coding among early childhood using a tangible programming approach that aligns with the cognitive and motor development characteristics of children. The development addresses the need for interactive, safe coding learning media capable of integrating foundational skills such as sequencing, pattern recognition, and problem solving. The research employs a research and development (R&D) method, adapting the Borg & Gall model integrated with the Waterfall Model for hardware and software development of the robot. The research process encompasses needs analysis, product design, prototype development, limited trial, revision, field trial, and validation by content and media experts. The results indicate that the developed robot exhibits excellent performance based on black-box testing, with all input-output functions operating consistently, responsively, and safely for early childhood use. Content expert validation yields a feasibility level of 89.3%, whereas media expert validation achieves 90.4%, both categorized as highly feasible. Furthermore, the robot enhances children's engagement and provides concrete learning experiences in understanding basic coding concepts. Thus, this robot medium is deemed effective and feasible for use as a supplementary tool in coding instruction within early childhood education (PAUD) settings, with potential to bolster digital literacy and computational thinking skills from an early age.

ABSTRACT Designing successful professional development requires an understanding of perceived obstacles to integrating robotics in classroom; however, there are currently few validated assessment instruments and analyses of subgroup differences across educator career stages. This study looked at how pre-service teachers (PSTs) and teacher educators perceive barriers differently and validated a Perceived Implementation Barriers Composite (PIBC). From robotics education modules in African teacher education institutions, 109 educators in all (50 teacher educators and 59 PSTs) were enlisted. While a 2 × 2 × 2 Bayesian factorial regression with regional fixed effects looked at heterogeneity across position, gender, and training subgroups, confirmatory factor analysis (CFA) with WLSMV estimation and polychoric reliability assessment was used to evaluate the PIBC. Although single-factor CFA results showed limited model fit, the PIBC showed strong reliability (polychoric α = 0.882 ). Multigroup CFA suggested caution in mean comparisons by supporting configural and metric invariance but not full scalar invariance. Compared to PSTs, teacher educators reported fewer obstacles ( effect = − 0.223 , 95 % CrI : [ − 0.421 , − 0.025 ] ). Although credible intervals overlapped, interaction analyses suggested potential differences by gender and prior training. The results highlight the need for tailored, context-sensitive professional development in robotics education by indicating that career stage may moderate the impact of gender and training on barrier perceptions.

ABSTRACT Background Educational robotics has emerged as a valuable approach to promoting STEM learning and problem-solving skills in early childhood education. However, there is limited empirical evidence on how structured, project-based approaches—particularly capstone projects—can influence young children’s engagement, confidence, and continued interest in technology and robotics. Purpose This study aims to investigate how Kindergarten and Grade 1 students participate in capstone projects within an early childhood robotics program, and how these projects impact their confidence in building and programming robots as well as their motivation to pursue further studies in technology and robotics. Sample The study involved a sample of 243 children aged 5 to 7 years, all of whom participated in an educational robotics and technology course that formed a mandatory part of their curriculum. Design and Methods A mixed-methods design was employed, combining both quantitative and qualitative analyses. Data were collected through individual and group interviews that explored children’s experiences, levels of confidence, and attitudes toward robotics before and after participating in the capstone project. The qualitative component examined patterns of engagement, collaboration, and creative problem-solving exhibited during project work. Results Findings indicate that implementing a capstone project in early childhood robotics education significantly enhances children’s confidence in building and programming robots. The project-based learning approach also increased students’ enjoyment and sense of achievement, motivating them to take on challenges beyond what is typically considered age-appropriate. The results demonstrate that even young learners can effectively engage in complex, creative, and collaborative STEM tasks when provided with structured, hands-on learning experiences. Conclusion Integrating a final capstone project into early childhood robotics programs provides a comprehensive and enriching learning experience. It fosters confidence, creativity, and sustained interest in technology, underscoring the value of incorporating project-based learning early in the educational journey.

This study explores the opportunities and challenges of integrating robotics technologies into science-hub libraries in Zanzibar, focusing on three hubs located on Unguja Island. The study population included staff working in the hubs and stakeholders dealing with robotics technologies in the country. A case study research design with a qualitative research approach was employed to collect, analyze, and interpret the data. Qualitative data was gathered through focus group discussions to gauge the respondents’ perceptions and experiences regarding educational robotics technologies in the hub settings. The findings highlight promising opportunities, including government support through infrastructure investments; grants for science, technology, engineering, and mathematics education; a competence-based curriculum; and access to professional development such as online courses. However, key challenges persist, including limited robotics infrastructure, insufficient librarian training, and financial constraints. To address these issues, the study recommends securing long-term funding, advocating for the inclusion of robotics in Zanzibar's education curriculum, and introducing affordable mobile robotics kits.

The Use of Educational Robotics in Greek Education: Trends, Challenges, and Future Prospects

This study presents the design and implementation of a low-cost, open-source, 3D-printed drone platform for university-level STEM education in mechatronics, robotics, control theory, and artificial intelligence. The platform addresses key limitations of existing educational drones, such as high cost, the proprietary nature of systems, and limited customizability, by integrating accessible materials, Arduino-compatible microcontrollers, and modular design principles, with all design files and instructional materials openly available. This work introduces technical improvements, including enhanced safety features and greater modularity, alongside pedagogical advancements such as structured lesson plans, a workflow bridging simulation, and hardware implementation. Educational impact was evaluated through a case study in a postgraduate course with 39 students participating in project-based activities involving 3D modeling, electronics integration, programming, and flight testing. Data collected via a Technology Acceptance Model-based survey and researcher observations showed high student engagement and satisfaction, with average scores of 4.49/5 for overall experience, 4.31/5 for perceived usefulness, and 4.38/5 for intention to use the drone in future activities. These results suggest the platform is a practical and innovative teaching tool for academic settings. Future work will extend its educational evaluation and application across broader contexts.

ABSTRACT Coding and robotics is an essential competency in the twenty-first century. In South Africa, coding and robotics is a mandatory subject in the Foundation Phase. However, successful integration into these early learning environments requires more than curricular inclusion, it also demands responsive support for educators navigating complex contextual realities. This study presents a framework, derived from a literature synthesis and participatory action research; to guide coding and robotics in early childhood education through playful learning using thematic data analysis. Co-constructed with ten Grade R educators and one external participant over a nine-month period in the Tshwane South district, data generation involved semi-structured interviews, observations, focus groups and a systematising expert interview. The framework, synthesised as NELO, comprises four interrelated components: Needs, External factors, the Learning process, and Outcomes. Findings indicate that educators require professional development, resources and collaboration initiatives to implement coding and robotics. The learning process of coding and robotics is progressive and embeds play-based, developmentally appropriate pedagogies while foregrounding localised and context-sensitive strategies. By bridging theoretical knowledge with practical realities, NELO offers a sustainable and scalable approach for the integration of coding and robotics in diverse early childhood education settings.

This study examines the effect of robot-assisted problem-based learning (PBL) on learning outcomes in higher education. A quasi-experimental design was adopted, and the application was carried out over 16 weeks (48 hours) with two parallel branches. In the experimental group, a question-and-answer-focused educational robot was integrated into the project-based learning process, while in the control group, the same content was applied without robots. Measurements encompassed self-directed learning (including self-management), problem-solving, and comprehension/discovery dimensions. Bayesian comparisons showed that the level of evidence in favor of the experiment was in the medium-strong range for most variables; a significant increase was observed particularly in self-management. Problem-solving and comprehension/discovery scores were also higher in the experimental condition. Figures and tables report posterior effect sizes and 95% confidence intervals. The results indicate that robot-assisted PBL, which offers timely and elucidative feedback, improves the metacognitive processes associated with PBL and aids students in adhering to their study goals more consistently. In conclusion, robot-assisted PBL can significantly improve self-management, problem-solving, and comprehension outcomes, provided that appropriate feedback design and application fidelity are maintained. Future research should examine its effects across various fields by integrating long-term follow-up periods and behavioral performance assessments. The study enhances the reproducibility of results by evaluating the strength of evidence through open Bayesian reporting. This method makes design choices more transparent and establishes clear guidelines on how educational technologies should be used responsibly.

This work investigates how a low-cost embedded platform can achieve reliable sensor fusion and multitask integration for autonomous navigation. A Raspberry Pi serves as the central controller, coordinating heterogeneous modules for line following, traffic-light recognition, ultrasonic-based stopping, audio feedback, and geometric symbol detection. Two key technical issues are examined: (i) the accuracy and reliability of ultrasonic ranging across distance, and (ii) the selection of image preprocessing parametersparticularly Gaussian kernel size and binarization thresholdto ensure robust vision under variable illumination and noise. Experimental results confirm that the bidirectional level shifter achieved stable 3.35 V translation, the ultrasonic sensor maintained reliable accuracy within 07 m with errors increasing at longer ranges, and optimized preprocessing improved trajectory extraction and template recognition. A discrete PID controller minimized path deviation, enabling smooth motion through curves and intersections. Overall, the findings demonstrate that compact, low-cost platforms can reliably integrate heterogeneous sensors for multitask robotic navigation, providing practical insights for robotics education and broader implications for robust sensor fusion in complex environments.

Robotics is increasingly essential in 21st-century education, fostering skills like critical thinking, creativity, and collaboration while integrating STEM subjects through systems and computational thinking. However, many K -12 STEM programs lack engineering components and rely heavily on teachers for implementation, limiting direct learner engagement. This paper argues that incorporating robotics into curricula shifts the focus to learners, emphasizing systems thinking and engineering design elements often missing in high school programs. It highlights the role of robotics in STEM skill development, critiques teacher-centric models, and advocates for learner-centric approaches. Additionally, it outlines a future trajectory for robotics curricula in developing nations, emphasizing the need to align with Industry 4.0 advancements. By analyzing robotics' potential to integrate STEM disciplines, the paper aims to empower curriculum planners, educators, and learners to harness technological innovations effectively. Insights from literature are used to support this proposition across four sections: robotics and STEM skills development, challenges of teacher-centric models, robotics as a learner-centric tool, and the future of robotics education. This approach addresses gaps in current STEM integration and equips learners with the skills needed to thrive in a technology-driven world.

Abstract The ability to solve problems collaboratively has become a crucial skill for success in an increasingly complex world. This study explores pair interactions and the emergence of collaborative roles among primary school pupils engaged in problem-solving tasks using modular robotics. We identify three distinct cooperative forms: co-construction, acquiescent co-elaboration, and one-sided manipulation. Our findings show that pairs who relied on one-sided manipulation or had little interaction struggled more with task resolution (RQ1). In terms of roles, participants initially acted as explorers but gradually adopted more defined roles such as proposer, critic, hand, or follower. We found that pairs with complementary and balanced roles showed better coordination and outcomes (RQ2). Finally, we identified four main collaboration challenges (competitive behaviors, lack of leadership, communication gaps, and task misinterpretation) impacting efficiency (RQ4). These findings highlight the need for structured support in educational robotics to foster equitable collaboration, clarify task understanding, and promote effective role-sharing among young learners.