Search Results (480)

This qualitative case study explores how preschool teachers enact inclusive pedagogical practices by integrating tangible technologies, low-tech, and no-tech tools within an inquiry-based learning framework. Focusing on teacher decision-making and children’s multimodal engagement, the study examines two questions: (1) How do early childhood teachers use a range of tools to support inclusive, inquiry-driven learning? and (2) How do children engage with these tools to communicate, collaborate, and construct knowledge? Drawing on classroom observations, teacher-created storyboards, child artifacts, and educator reflections, findings illustrate how programmable robots, recycled materials, and hands-on resources support accessibility and creative expression for diverse learners. Children used alternative modalities such as coding, drawing, building, and storytelling to represent their ideas and engage in problem-solving across a range of developmental and linguistic needs. Teachers are positioned as pedagogical designers who scaffold inclusive participation through flexible environments, intentional provocations, and responsive guidance. Rather than treating technology as a standalone innovation, the study emphasizes how its integration, when grounded in play, inquiry, and real-world relevance, can promote equity and engagement. These findings contribute to research on Universal Design for Learning (UDL), early STEM education, and inclusive instructional design in early childhood classrooms. Full article

Computational Thinking (CT) has been an important concept for the computer science education community in the last 20 years. In this work we performed a systematic review of the literature regarding the computational thinking of children from kindergarten to primary school. We compiled a large dataset of one hundred and twenty (120) studies from the literature. Through analysis of these studies, we tried to reveal important insights and draw interesting and valid conclusions. We analyzed various qualitative and quantitative aspects of the studies, including the sample size, the year of publication, the country of origin, the studies’ design and duration, the computational tools used, and so on. An important aspect of the work is to highlight differences between different study designs. We identified a total of 120 studies, with more than half of them (>50%) originating from Asian countries. Most studies (82.5%) conducted some form of intervention, aiming to improve their computational thinking in students. A smaller proportion (17.5%) were assessment studies in which the authors conducted assessments regarding the children’s computational thinking. On average, intervention studies had a smaller number of participants, but differences in duration could not be identified. There was also a lack of large-scale longitudinal studies. Block-based coding (i.e., Scratch) and Plugged and Unplugged activities were observed in high numbers in both categories of studies. CT assessment tools showed great variability. Efforts for standardization and reaching a consensus are needed in this regard. Finally, robotic systems have been found to play a major role in interventions over the last years. Full article

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

Background/Objectives: While robot-assisted radical cystectomy (RARC) has shown potential benefits over open radical cystectomy (ORC), such as reduced blood loss and quicker recovery, its adoption has been limited because of its complexity and long learning curve, especially for urinary diversion. We assessed whether a RARC program with fully intracorporeal urinary diversion could be safely implemented in a hospital with no prior experience in RARC, but with expertise in ORC and other robotic surgeries. We also compared perioperative outcomes and complications between RARC and ORC during the implementation phase. Methods: This retrospective comparative study included 50 consecutive patients who underwent RARC between June 2023 and January 2025 and 50 patients previously treated with ORC. All RARC cases were performed with intracorporeal urinary diversion. A structured proctoring program guided two surgeons through a stepwise training approach by an expert RARC surgeon. Perioperative outcomes and 90-day complications were compared. Results: All RARC procedures were completed fully intracorporeally with no conversions to open surgery. Compared with ORC, RARC was associated with significantly shorter operative times (for ileal conduit diversion) and hospital stays, lower estimated blood loss, and fewer postoperative complications. There were no differences in intraoperative complications. Worst single grade ≥ 3 complications were significantly less frequent in the RARC than the ORC group (11 [11%] versus 21 [21%], p = 0.045). On multivariable analysis, the robotic approach independently predicted fewer any-grade complications (odds ratio 0.81, 95% confidence intervals 0.65–0.95, p = 0.01). Conclusions: A RARC program can be safely and effectively implemented in a previously RARC-naïve centre with existing surgical expertise. The robotic approach offers clear perioperative benefits and may represent a favourable alternative to open surgery. Full article

The education sector in Tanzania faces significant challenges, especially in public primary schools. Unmanageably large classes and critical teacher–pupil ratios hinder the provision of tailored tutoring, impeding pupils’ educational growth. However, artificial intelligence (AI) could provide a way forward. Advances in generative AI can be leveraged to create interactive and effective intelligent tutoring systems, which have recently been built into embodied systems such as social robots. Motivated by the pivotal influence of teachers’ attitudes on the adoption of educational technologies, this study undertakes a qualitative investigation of Tanzanian primary school mathematics teachers’ perceptions of contextualised intelligent social robots. Thirteen teachers from six schools in both rural and urban settings observed pupils learning with a social robot. They reported their views during qualitative interviews. The results, analysed thematically, reveal a generally positive attitude towards using social robots in schools. While commended for their effective teaching and suitability for one-to-one tutoring, concerns were raised about incorrect and inconsistent feedback, language code-switching, response latency, and the lack of support infrastructure. We suggest actionable steps towards adopting tutoring systems and social robots in schools in Tanzania and similar low-resource countries, paving the way for their adoption to redress teachers’ workloads and improve educational outcomes. Full article

This paper presents a novel, multi-stage modelling approach for integrating Generative AI (GenAI) tools into design-based STEM education, promoting sustainability and 21st-century problem-solving skills. The proposed methodology includes (i) a conceptual model that defines structural aspects of the domain at a high abstraction level; (ii) a contextual model for defining the internal context; (iii) a GenAI-based model for solving the STEM task, which consists of a generic model for integrating GenAI tools into STEM-driven education and a process model, presenting learning/design processes using those tools. A case study involving the design of an autonomous folkrace robot illustrates the implementation of the approach. Based on Likert-scale evaluations, quantitative results demonstrate a significant impact of GenAI tools in enhancing critical thinking, conceptual understanding, creativity, and engineering practices, particularly during the prototyping and testing phases. This paper concludes that the structured integration of GenAI tools supports personalized, inquiry-based, and sustainable STEM education, while also raising new challenges in prompt engineering and ethical use. This approach provides educators with a systematic pathway for leveraging AI to develop STEM-based skills essential for future sustainable development. Full article

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

This work presents the development of a functional real-time SLAM system designed to enhance the perception capabilities of an Automated Guided Vehicle (AGV) using only a 2D LiDAR sensor. The proposal aims to address recurring gaps in the literature, such as the need for low-complexity solutions that are independent of auxiliary sensors and capable of operating on embedded platforms with limited computational resources. The system integrates scan alignment techniques based on the Iterative Closest Point (ICP) algorithm. Experimental validation in a controlled environment indicated better performance using Gauss–Newton optimization and the point-to-plane metric, achieving pose estimation accuracy of 99.42%, 99.6%, and 99.99% in the position ($x$, $y$) and orientation ($\theta$) components, respectively. Subsequently, the system was adapted for operation with data from the onboard sensor, integrating a lightweight graphical interface for real-time visualization of scans, estimated pose, and the evolving map. Despite the moderate update rate, the system proved effective for robotic applications, enabling coherent localization and progressive environment mapping. The modular architecture developed allows for future extensions such as trajectory planning and control. The proposed solution provides a robust and adaptable foundation for mobile platforms, with potential applications in industrial automation, academic research, and education in mobile robotics. Full article

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers. Full article

Personality is a fundamental psychological trait that exerts a long-term influence on human behavior patterns and social interactions. Automatic personality recognition (APR) has exhibited increasing importance across various domains, including Human–Robot Interaction (HRI), personalized services, and psychological assessments. In this study, we propose a multimodal personality recognition model that classifies the Big Five personality traits by extracting features from three heterogeneous sources: audio processed using Wav2Vec2, video represented as Skeleton Landmark time series, and text encoded through Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec embeddings. Each modality is handled through an independent Self-Attention block that highlights salient temporal information, and these representations are then summarized and integrated using a late fusion approach to effectively reflect both the inter-modal complementarity and cross-modal interactions. Compared to traditional recurrent neural network (RNN)-based multimodal models and unimodal classifiers, the proposed model achieves an improvement of up to 12 percent in the F1-score. It also maintains a high prediction accuracy and robustness under limited input conditions. Furthermore, a visualization based on t-distributed Stochastic Neighbor Embedding (t-SNE) demonstrates clear distributional separation across the personality classes, enhancing the interpretability of the model and providing insights into the structural characteristics of its latent representations. To support real-time deployment, a lightweight thread-based processing architecture is implemented, ensuring computational efficiency. By leveraging deep learning-based feature extraction and the Self-Attention mechanism, we present a novel personality recognition framework that balances performance with interpretability. The proposed approach establishes a strong foundation for practical applications in HRI, counseling, education, and other interactive systems that require personalized adaptation. Full article

As artificial intelligence (AI) becomes ubiquitous across various fields, understanding people’s acceptance and trust in AI systems becomes essential. This review aims to identify quantitative measures used to measure trust in AI and the associated studied elements. Following the PRISMA guidelines, three databases were consulted, selecting articles published before December 2023. Ultimately, 45 articles out of 1283 were selected. Articles were included if they were peer-reviewed journal publications in English reporting empirical studies measuring trust in AI systems with multi-item questionnaires. Studies were analyzed through the lenses of cognitive and affective trust. We investigated trust definitions, questionnaires employed, types of AI systems, and trust-related constructs. Results reveal diverse trust conceptualizations and measurements. In addition, the studies covered a wide range of AI system types, including virtual assistants, content detection tools, chatbots, medical AI, robots, and educational AI. Overall, the studies show compatibility of cognitive or affective trust focus between theorization, items, experimental stimuli, and level of anthropomorphism of the systems. The review underlines the need to adapt measurement of trust in the specific characteristics of human–AI interaction, accounting for both the cognitive and affective sides. Trust definitions and measurement could be chosen depending also on the level of anthropomorphism of the systems and the context of application. Full article

Robotic arms are used in a wide range of industrial and medical applications. However, for research and education, users often face a trade-off between costly commercial solutions with no adaptability and open-source alternatives that lack usability and functionality. In education, this problem is exacerbated by the prohibitive cost of commercial systems or simplifications that distort learning. Thus, we present HELENE, an open-source robot with six degrees of freedom, closed-loop position control, and robot operating system (ROS) integration. The modular design of the robot, printed on a commercial 3D printer, and its integrated custom electronics allow for easy customization for research purposes. The joints are driven by standard stepper motors with closed-loop position control using absolute encoders. The ROS integration guarantees widespread control options and integration into existing environments. Our prototype, tested in accordance with ISO 9283, has a small positional accuracy error of 8.4 mm and a repeatability error of only 0.87 mm with a load capacity of 500 g at a reach of 432 mm. Ten prototypes were built and used in various research and education applications, demonstrating the versatile applicability of this open-source robot, closing the gap between reliable commercial systems and flexible open-source solutions. Full article

This study presents the development of a software solution for processing, analyzing, and visualizing sensor data collected by an educational mobile robot. The focus is on statistical analysis and identifying correlations between diverse datasets. The research utilized the PlatypOUs mobile robot platform, equipped with odometry and inertial measurement units (IMUs), to gather comprehensive motion data. To enhance the reliability and interpretability of the data, advanced data processing techniques—such as moving averages, correlation analysis, and exponential smoothing—were employed. Python-based tools, including Matplotlib and Visual Studio Code, were used for data visualization and analysis. The analysis provided key insights into the robot’s motion dynamics; specifically, its stability during linear movements and variability during turns. By applying moving average filtering and exponential smoothing, noise in the sensor data was significantly reduced, enabling clearer identification of motion patterns. Correlation analysis revealed meaningful relationships between velocity and acceleration during various motion states. These findings underscore the value of advanced data processing techniques in improving the performance and reliability of educational mobile robots. The insights gained in this pilot project contribute to the optimization of navigation algorithms and motion control systems, enhancing the robot’s future potential in STEM education applications. Full article

The developed omnidirectional mobile manipulator is an educational omnidirectional mobile manipulator that utilizes the Raspberry Pi Pico W and is programmed in Python. It is designed to enhance STEM education by providing an interactive environment for studying robotics, sensor integration, and programming techniques. The robot is built on an off-the-shelf chassis equipped with Mecanum wheels and a robotic arm actuated by servo motors. As part of this project, the control electronics were designed and implemented to enable seamless operation. While the platform allows students to program the robot as part of the STEM curriculum, our base software solution, developed in Python, provides control of both the mobile base and the robotic arm via a web interface accessible through the robot’s Wi-Fi hotspot. Full article

This paper explores the potential of the Ozobot EVO mobile robot as an educational tool for teaching Python programming. While the robot is currently designed for younger students through color and block programming, it is not yet widely utilized for teaching text-based coding. The Ozobot’s compatibility with Python presents a valuable opportunity for students to visualize their programming concepts through the robot’s actions, offering a more engaging alternative to console-based learning. The increasing use of the Raspberry Pi, a single-board computer programmed in Python, has necessitated the inclusion of Python in the curriculum. However, students often find learning Python challenging and demotivating. To enhance STEM education and student motivation, this paper proposes sample Python code for the Ozobot EVO, aiming to encourage educators to integrate the robot into their teaching. I suggest some Python code examples for the Ozobot EVO. This is to help educators see how they can use the robot in their lessons. Specifically, code examples for controlling motion, sound, light, and combinations of these functionalities are presented. When students see the robot react immediately to their code, they can understand programming ideas much better than just seeing text in the Python console. The Ozobot EVO mobile robot offers a solid foundation for learning Python programming. Full article