Search Results (430)

Artificial intelligence (AI) is one of the most promising technological advancements that have revolutionized the healthcare sector (medicine and dentistry). AI and its subsets, such as machine learning (ML), artificial neural networks (ANNs), and deep learning (DL), are being used in dentistry for data recording and management, patient education, radiographic interpretation, diagnosis, and treatment plans. AI and ML tools are commonly employed to improve oral hygiene and patient compliance. This narrative review paper discusses the innovations in AI-based plaque control aids (toothbrushes and interdental aids) that have improved overall health and patients’ hygiene compliance. We performed a literature search using different databases using the following keywords: “Artificial intelligence or machine learning or robots or robotics” AND “Toothbrush OR Smart toothbrush”. We included all the studies evaluating the use of any smart toothbrush, AI, or robotics for oral hygiene, plaque control, and patient education. AI-based smart toothbrushes helped patients to brush effectively by indicating the amount of pressure and the time taken for brushing, along with providing feedback on their brushing performance. Many microrobots can even recognize and automatically remove biofilm. Some AI-based smart toothbrushes are beneficial for children, patients with disabilities lack of manual dexterity, and neurological disorders. However, dental professionals choose AI-based smart toothbrushes for patients with poor oral hygiene and poor compliance for more effective control of oral diseases and to provide better health. Full article

This paper seeks to identify the impact of learning programming and robotics in the Science, Technology, Engineering, and Mathematics (STEM) educational approach. Studying these areas of knowledge is important to prepare students to face contemporary technological challenges. The approach analyzes how to establish and define the curricular content articulated in developing critical 21st-century skills within the teaching–learning process. A methodological strategy is proposed in the scientific field using the historical-descriptive method to carry out a literature review and a bibliometric study, evaluating scientific articles indexed in Web of Science (WoS) and Scopus from 2020 to 2024. Later, an evaluation is carried out using satisfaction surveys directed to eighth-grade students and teachers of the Unidad Educativa Fiscal Ciudad de Girón. These surveys address various aspects related to the context of learning programming and robotics from the STEM perspective. Consequently, the analytic–synthetic approach revealed that teaching programming and robotics would promote cognitive skills from adolescence, which is crucial for building solid foundations in STEM concepts. The positive impact on the motivation for change in students and teachers is highlighted by facilitating interaction with technologies and applying knowledge in practical projects in the educational process. Full article

Educational robotics is a sector that is being integrated into classrooms to achieve innovative and effective learning in the early stages of children’s education. However, it is not only applied in education but has great importance in its use with children with special needs to improve their quality of life. The convenience of robots applied to interaction with children with autism spectrum disorder (ASD) has been widely demonstrated. In this work, a study is carried out on what design patterns a robot focused on children with autism should have. It is necessary to make known the characteristics of this and, from there, how to apply these concepts to the effective design of a device. A robot, TEA-2, is proposed that encompasses all these guidelines in a single robot. Full article

The integration of artificial intelligence (AI) technologies into education has gained increasing attention, yet limited research examines how the curriculum design can enhance learning outcomes and influence learners’ intentions to continue AI learning. This study addresses this gap by integrating the theory of planned behavior, technology acceptance model, theories of motivation, and computer self-efficacy to explore the factors affecting learners’ behavioral intentions in AI education. Using the AI course quality as the primary antecedent and “intention to continue taking courses” as the dependent variable, the study investigates the structural relationships and mediating variables between these factors. Data were collected through a stratified random sampling method from 19 universities in Taiwan, involving 200 students who had completed five core AI-related courses, including artificial intelligence, machine learning, internet of things, big data, and robotics. The analysis, conducted using PLS-SEM, revealed that AI course quality directly and indirectly influences learners’ behavioral intentions through mediating variables such as learning satisfaction, computer self-efficacy, technological literacy, and computer learning motivation. Moreover, AI course quality exerted a significant positive effect on computer motivation, which, in turn, influenced self-efficacy and learning outcomes. These findings provide valuable insights into the antecedents and processes shaping learners’ intentions to continue AI learning, offering practical and theoretical implications for AI education. Full article

With the increasing application of robots in human-centred environments, there is increasing motivation for incorporating some degree of human-like social competences. Fields such as psychology and cognitive science not only provide guidance on the types of behaviour that could and should be exhibited by the robots, they may also indicate the manner in which these behaviours can be achieved. The domain of social child–robot interaction (sCRI) provides a number of challenges and opportunities in this regard; the application to an educational context allows child-learning outcomes to be characterised as a result of robot social behaviours. One such social behaviour that is readily (and unconsciously) used by humans is behavioural alignment, in which the behaviours expressed by one person adapts to that of their interaction partner, and vice versa. In this paper, the role that robot non-verbal behavioural alignment for their interaction partner can play in the facilitation of learning outcomes for the child is examined. This behavioural alignment is facilitated by a human memory-inspired learning algorithm that adapts in real-time over the course of an interaction. A large touchscreen is employed as a mediating device between a child and a robot. Collaborative sCRI is emphasised, with the touchscreen providing a common set of interaction affordances for both child and robot. The results show that an adaptive robot is capable of engaging in behavioural alignment, and indicate that this leads to greater learning gains for the children. This study demonstrates the specific contribution that behavioural alignment makes in improving learning outcomes for children when employed by social robot interaction partners in educational contexts. Full article

In contrast to other learning technologies, social robots are social and affective entities that are defined by their physical presence, their anthropomorphic characteristics, and their advanced social, emotional, and cognitive skills. Social robots are intelligent tutoring systems that can improve students’ learning, affective, and cognitive outcomes when used as tutors or peer learners offering affective and personalized learning. As the field of social robots and their use in education is rapidly advancing, this study aims to provide a review regarding the integration of social robots in education through the analysis of the existing literature to present the state of the art and to identify future research directions. Additionally, the main characteristics and properties of social robots are defined and the benefits they can bring in education are discussed. Specifically, the study examines 361 documents that derived from Scopus and the Web of Science databases. To analyze the documents, Bibliometrix, VOSviewer, topic modeling through Latent Dirichlet Allocation (LDA), and content analysis are used. An analysis of the basic characteristics of the documents (e.g., publication frequency, citation count, authors, sources, countries, affiliations, etc.) and a more in-depth analysis focusing on identifying the most prominent topics and themes as well as the thematic evolution of the topic were carried out. Finally, through the content analysis, current limitations and challenges were revealed and emerging topics and future research directions were highlighted. Full article

This study focuses on selecting a suitable 3D printer and defining experimental methods to gather the necessary data for determining the optimal filament material for printing components of the VEX GO and VEX IQ robotic kits. The aim is to obtain the required data to identify an appropriate filament material and set 3D printing parameters to achieve the desired mechanical properties of the parts while maintaining cost-effectiveness. Another key objective is achieving optimal operational functionality, ensuring the required part performance with minimal printing costs. It is desirable for the modeled and printed parts to exhibit the required mechanical properties while maintaining economic efficiency. Another crucial aspect is achieving optimal functionality of the produced parts with minimal printing costs. This will be assessed by analyzing the impact of key 3D printing technology parameters, focusing in this research phase on material selection. The criteria for selecting filament materials include ease of printability under the conditions of primary and secondary schools, simplicity of printing, minimal need for post-processing, and adequate mechanical properties verified through experimental measurements and destructive tests on original parts from VEX GO and VEX IQ kits. The study analyzed various filaments regarding their mechanical properties, printability, and cost-effectiveness. The most significant practical contribution of this study is selecting a suitable filament material tested through a set of destructive tests, emphasizing maintaining the mechanical properties required for the real-life application of the parts. This includes repetitive assembly and disassembly of various robotic model constructions and their activation for demonstration purposes and applications of STEM/STEAM/STREAM methods in the educational process to achieve the properties of original components. Additionally, the study aims to set up 3D printing such that even a beginner-level operator, such as a primary or secondary school student under the supervision of their teacher or a teacher with minimal knowledge and experience in 3D printing, can successfully execute it. Further ongoing research focuses on evaluating the effects of characteristic 3D printing parameters, such as infill and perimeter, on the properties of 3D-printed parts through additional measurements and analyses. Full article

This study designs and develops a wearable exoskeleton piano assistance system for individuals recovering from neurological injuries, aiming to help users regain the ability to perform complex tasks such as playing the piano. While soft robotic exoskeletons have proven effective in rehabilitation therapy and daily activity assistance, challenges remain in performing highly dexterous tasks due to structural complexity and insufficient motion accuracy. To address these issues, we developed a modular division method based on multi-domain mapping and a top-down process model. This method integrates the functional domain, structural domain, and user needs domain, and explores the principles and methods for creating functional construction modules, overcoming the limitations of traditional top-down approaches in design flexibility. By closely combining layout constraints with the design model, this method significantly improves the accuracy and efficiency of module configuration, offering a new path for the development of piano practice assistance devices. The results demonstrate that this device innovatively combines piano practice with rehabilitation training and through the introduction of ontological modeling methods, resolves the challenges of multidimensional needs mapping. Based on five user requirements (P), we calculated the corresponding demand weight (K), making the design more aligned with user needs. The device excels in enhancing motion accuracy, interactivity, and comfort, filling the gap in traditional piano assistance devices in terms of multi-functionality and high adaptability, and offering new ideas for the design and promotion of intelligent assistive devices. Simulation analysis, combined with the motion trajectory of the finger’s proximal joint, calculates that 60° is the maximum bending angle for the aforementioned joint. Physical validation confirms the device’s superior performance in terms of reliability and high-precision motion reproduction, meeting the requirements for piano-assisted training. Through multi-domain mapping, the top-down process model, and modular design, this research effectively breaks through the design flexibility and functional adaptability bottleneck of traditional piano assistance devices while integrating neurological rehabilitation with music education, opening up a new application path for intelligent assistive devices in the fields of rehabilitation medicine and arts education, and providing a solution for cross-disciplinary technology fusion and innovative development. Full article

Robotic construction using three-dimensional (3D) concrete printing (3DCP) offers significant potential to transform Kuwait’s construction industry, particularly in reducing waste. This study explores the feasibility of integrating 3DCP into Kuwait’s construction waste management practices by examining the perspectives of key stakeholders. Through a mixed method approach of a comprehensive literature review, a survey of 87 industry professionals, and 33 in-depth interviews with representatives from the Public Authority for Housing Welfare (PAHW), Municipality, private sector, and the general public, the study identifies both the benefits and challenges of 3DCP adoption. The findings highlight key advantages of 3DCP, including increased construction efficiency, cost savings, enhanced design flexibility, and reduced material waste. However, several barriers, such as regulatory limitations, technical challenges in adapting 3DCP to local project scales, and cultural resistance, must be addressed. Results also indicate varying levels of stakeholder familiarity with 3DCP and existing waste management practices, underscoring the need for awareness and educational initiatives. This study makes two significant contributions: first, by providing a detailed analysis of the technical and regulatory challenges specific to Kuwait’s construction sector, and second, by offering a strategic roadmap for 3DCP integration, including regulatory reform, research into sustainable materials, and cross-sector collaboration. These recommendations aim to enhance waste management practices by promoting more sustainable and efficient construction methods by achieving SDGs 9, 11, 12, and 13. The study concludes that government support and policy development will be essential in driving the adoption of 3DCP and achieving long-term environmental benefits in Kuwait’s construction industry. Full article

Background: Advancements in artificial intelligence (AI) and machine learning (ML) are set to revolutionize healthcare, particularly in fields like endoscopic surgery that heavily rely on digital imaging. However, to effectively integrate these technologies and drive future innovations, pediatric surgeons need specialized AI/ML skills. This survey evaluated the current level of readiness and educational needs regarding AI/ML among members of the European Society of Pediatric Endoscopic Surgeons (ESPES). Methods: A structured survey was distributed via LimeSurvey to ESPES members via email before and during the 2024 Annual Conference. Responses were collected over four weeks with voluntary, anonymous participation. Quantitative data were analyzed using descriptive statistics. Results: A total of 125 responses were received. Two-thirds (65%) of respondents rated their AI/ML understanding as basic, with only 6% reporting advanced knowledge. Most respondents (86%) had no formal AI/ML training. Some respondents (31%) used AI/ML tools in their practice, mainly for diagnostic imaging, surgical planning, and predictive analytics; 42% of the respondents used these tools weekly. The majority (95%) expressed interest in further AI/ML training, preferring online courses, workshops, and hands-on sessions. Concerns about AI/ML in pediatric surgery were high (85%), especially regarding data bias (98%). Half of respondents (51%) expect AI/ML to play a significant role in advancing robotic surgery, oncology, and minimally invasive techniques. A strong majority (84%) felt that the ESPES should lead AI education in pediatric surgery. Conclusions: This survey presents the ESPES with a unique opportunity to develop a competency map of its membership’s AI/ML skills and develop targeted educational programs, thus positioning the society to take the lead in AI education and the advancement of AI solutions in pediatric endosurgery. Full article

As sustainable architecture is increasingly emphasizing material reuse, this study proposes a novel, interactive workflow that integrates robotic arms and artificial intelligence to transform waste materials from architectural models into creative design components. Unlike existing recycling efforts, which focus on the construction phase, this research uniquely targeted discarded architectural model materials, particularly polystyrene foam, that are often overlooked, despite their environmental impact. The workflow combined computer vision and machine learning, utilizing the YOLOv5 model, which achieved a classification accuracy exceeding 83% for the polygon, rectangle, and circle categories, demonstrating a superior recognition performance. Robotic sorting demonstrated the ability to process up to six foam blocks per minute under controlled conditions. By integrating Stable Diffusion, we further generated speculative architectural renderings, enhancing creativity and design exploration. Participant testing revealed that human interaction reduced stacking errors by 57% and significantly improved user satisfaction. Moreover, human–robot collaboration not only corrected robotic errors, but also fostered innovative and collaborative solutions, demonstrating the system’s potential as a versatile tool for education and industry while promoting sustainability in design. Thus, this workflow offers a scalable approach to creative material reuse, promoting sustainable practices from the model-making stage of architectural design. While these initial results are promising, further research is needed to adapt this technique for larger-scale construction materials, addressing real-world constraints and broadening its applicability. Full article

Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology. They are also increasingly being utilized in studies on ophthalmology-related exams, particularly those containing multiple-choice questions (MCQs). This narrative review evaluates both the opportunities and the challenges of integrating chatbots into ophthalmology research, with separate assessments of studies involving open- and close-ended questions. While chatbots have demonstrated sufficient accuracy in handling MCQ-based studies, supporting their use in education, additional exam security measures are necessary. The research on open-ended question responses suggests that AI-based LLM chatbots could be applied across nearly all areas of ophthalmology. They have shown promise for addressing patient inquiries, offering medical advice, patient education, supporting triage, facilitating diagnosis and differential diagnosis, and aiding in surgical planning. However, the ethical implications, confidentiality concerns, physician liability, and issues surrounding patient privacy remain pressing challenges. Although AI has demonstrated significant promise in clinical patient care, it is currently most effective as a supportive tool rather than as a replacement for human physicians. Full article

This study investigates the impact of AI-generated contexts on preservice teachers’ computational thinking (CT) skills and their acceptance of educational robotics. This article presents a methodology for teaching robotics based on AI-generated contexts aimed at enhancing CT. An experiment was conducted with 122 undergraduate students enrolled in an Early Childhood Education program, aged 18–19 years, who were training in the Computer Science and Digital Competence course. The experimental group utilized a methodology involving AI-generated practical assignments designed by their lecturers to learn educational robotics, while the control group engaged with traditional teaching methods. The research addressed five key factors: the effectiveness of AI-generated contexts in improving CT skills, the specific domains of CT that showed significant improvement, the perception of student teachers regarding their ability to teach with educational robots, the enhancement in perceived knowledge about educational robots, and the overall impact of these methodologies on teaching practices. Findings revealed that the experimental group exhibited higher engagement and understanding of CT concepts, with notable improvements in problem-solving and algorithmic thinking. Participants in the AI-generated context group reported increased confidence in their ability to teach with educational robots and a more positive attitude toward technology integration in education. The findings highlight the importance of providing appropriate context and support when encouraging future educators to build confidence and embrace educational technologies. This study adds to the expanding research connecting AI, robotics, and education, emphasizing the need to incorporate these tools into teacher training programs. Further studies should investigate the lasting impact of such approaches on computational thinking skills and teaching methods in a variety of educational environments. Full article

This paper presents a study on enhancing human–robot interaction (HRI) through multimodal emotional recognition within social robotics. Using the humanoid robot Pepper as a testbed, we integrate visual, auditory, and textual analysis to improve emotion recognition accuracy and contextual understanding. The proposed framework combines pretrained neural networks with fine-tuning techniques tailored to specific users, demonstrating that high accuracy in emotion recognition can be achieved by adapting the models to the individual emotional expressions of each user. This approach addresses the inherent variability in emotional expression across individuals, making it feasible to deploy personalized emotion recognition systems. Our experiments validate the effectiveness of this methodology, achieving high precision in multimodal emotion recognition through fine-tuning, while maintaining adaptability in real-world scenarios. These enhancements significantly improve Pepper’s interactive and empathetic capabilities, allowing it to engage more naturally with users in assistive, educational, and healthcare settings. This study not only advances the field of HRI but also provides a reproducible framework for integrating multimodal emotion recognition into commercial humanoid robots, bridging the gap between research prototypes and practical applications. Full article

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains. Full article