Search Results (32)

The rapid expansion of Artificial Intelligence in Education (AIED) has created both remarkable opportunities and pressing concerns. Applications of intelligent tutoring systems, learning analytics, generative models, and educational robotics illustrate the transformative momentum of the field, yet they also raise fundamental questions regarding ethics, equity, and sustainability. The mission of AI in Education (MDPI) is to provide a rigorous, interdisciplinary, and inclusive platform where these debates can unfold. The journal bridges pedagogy and engineering, welcomes both empirical evidence of positive impacts and critical examinations of systemic risks, and advances responsible innovation in real educational settings. By integrating methodological standards, governance perspectives, and pedagogical ethics, including teacher-centered validation approaches, AI in Education positions itself as a space for constructive dialogue that values both enthusiasm and critique. Above all, the journal is committed to a human-centered vision for AIED, so that innovation in classrooms remains grounded in care, responsibility, and educational purpose. 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

Robot learning from human demonstration pioneers an effective mapping paradigm for endowing robots with human-like operational capabilities. This paper proposes a bio-signal-guided robot adaptive stiffness learning framework grounded in the conclusion that muscle activation of the human arm is positively correlated with the endpoint stiffness. First, we propose a human-teleoperated demonstration platform enabling real-time modulation of robot end-effector stiffness by human tutors during operational tasks. Second, we develop a dual-stage probabilistic modeling architecture employing the Gaussian mixture model and Gaussian mixture regression to model the temporal–motion correlation and the motion–sEMG relationship, successively. Third, a real-world experiment was conducted to validate the effectiveness of the proposed skill transfer framework, demonstrating that the robot achieves online adaptation of Cartesian impedance characteristics in contact-rich tasks. This paper provides a simple and intuitive way to plan the Cartesian impedance parameters, transcending the classical method that requires complex human arm endpoint stiffness identification before human demonstration or compensation for the difference in human–robot operational effects after human demonstration. Full article

The integration of artificial intelligence (AI) in special education has the potential to transform learning experiences and improve outcomes for students with disabilities. This systematic literature review examines the application of AI technologies in special education, focusing on personalized learning, cognitive and behavioral interventions, communication, emotional support, and physical independence. Through an analysis of 15 studies conducted between 2019 and 2024, the review synthesizes evidence on the effectiveness of AI tools, including intelligent tutoring systems, adaptive learning platforms, assistive communication devices, and robotic aids. The findings suggest that AI-driven technologies significantly enhance students’ academic performance, communication skills, emotional regulation, and physical mobility by providing tailored interventions that address individual needs. This review also highlights several challenges, including limited access to AI technologies in low-resource settings, the need for more comprehensive teacher training, and ethical concerns related to data privacy and algorithmic bias. Additionally, the geographic focus of the current research is primarily on developed countries, overlooking the specific challenges of implementing AI in resource-constrained environments. This review emphasizes the need for more diverse and ethical research to fully realize the potential of AI in supporting students with disabilities and promoting inclusive education. 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

The purposes of this study are to investigate college students’ attitudes toward ChatGPT and to understand whether gender makes any difference in their attitudes. We developed the ChatGPT attitude scale (CAS) and administrated it to a sample of 516 Taiwan college students. Through an exploratory factor analysis, the 5-T (Tool, Tutor, Talk, Trend, and Threat) model of CAS was extracted and validated via confirmatory factor analysis. The CAS exhibited good reliability and validity and can be used to explain ChatGPT attitudes. According to our findings, university students consider ChatGPT an important “Tool” in their daily life. Additionally, ChatGPT plays a significant “Tutor” role, assisting with language translation and knowledge learning. Besides its utilitarian functions, ChatGPT also serves as a “Talk” feature, offering interactive chat and emotional support. Currently, students also acknowledge ChatGPT as an important “Trend” of the times, but they are also deeply concerned about the potential “Threat” of content falsification and job displacement brought on by ChatGPT. In terms of gender differences, compared to females, males scored higher than females in the total scale and in the Tool, Tutor, and Trend subscales. However, there was no significant difference between males and females in the Talk and Threat subscales. This gender difference result differs from previous research on robots or social media. Full article

Facial expression recognition plays an increasingly important role in daily life, and it is used in several areas of human–computer interaction, such as robotics, assisted driving, and intelligent tutoring systems. However, the current mainstream methods are based on the whole face, and do not consider the existence of expression asymmetry between the left and right half-face. Hence, the accuracy of facial expression recognition needs to be improved. In this paper, we propose a half-face self-attention weighted approach called HSAW. Using statistical analysis and computer vision techniques, we found that the left half-face contains richer expression features than the right half-face. Specifically, we employed a self-attention mechanism to assign different weights to the left and right halves of the face. These weights are combined with convolutional neural network features for improved facial expression recognition. Furthermore, to attack the presence of uncertain categories in the dataset, we introduce adaptive re-labeling module, which can improve the recognition accuracy. Extensive experiments conducted on the FER2013 and RAF datasets have verified the effectiveness of the proposed method, which utilizes fewer parameters. Full article

During the learning of a new sensorimotor task, individuals are usually provided with instructional stimuli and relevant information about the target task. The inclusion of haptic devices in the study of this kind of learning has greatly helped in the understanding of how an individual can improve or acquire new skills. However, the way in which the information and stimuli are delivered has not been extensively explored. We have designed a challenging task with nonintuitive visuomotor perturbation that allows us to apply and compare different motor strategies to study the teaching process and to avoid the interference of previous knowledge present in the naïve subjects. Three subject groups participated in our experiment, where the learning by repetition without assistance, learning by repetition with assistance, and task Segmentation Learning techniques were performed with a haptic robot. Our results show that all the groups were able to successfully complete the task and that the subjects’ performance during training and evaluation was not affected by modifying the teaching strategy. Nevertheless, our results indicate that the presented task design is useful for the study of sensorimotor teaching and that the presented metrics are suitable for exploring the evolution of the accuracy and precision during learning. Full article

This review paper provides a comprehensive analysis of the automation of smart education in the context of Industry 5.0 from 78 papers, focusing on the integration of advanced technologies and the development of innovative, effective, and ethical educational solutions for the future workforce. As the world transitions into an era characterized by human–machine collaboration and rapidly evolving technologies, there is an urgent need to recognize the pivotal role of smart education in preparing individuals for the opportunities and challenges presented by the new industrial landscape. The paper examines key components of smart education, including intelligent tutoring systems, adaptive learning environments, learning analytics, and the application of the Internet of Things (IoT) in education. It also discusses the role of advanced technologies such as artificial intelligence (AI), machine learning (ML), robotics, and augmented and virtual reality (AR/VR) in shaping personalized and immersive learning experiences. The review highlights the importance of smart education in addressing the growing demand for upskilling and reskilling, fostering a culture of lifelong learning, and promoting adaptability, resilience, and self-improvement among learners. Furthermore, the paper delves into the challenges and ethical considerations associated with the implementation of smart education, addressing issues such as data privacy, the digital divide, teacher and student readiness, and the potential biases in AI-driven systems. Through a presentation of case studies and examples of successful smart education initiatives, the review aims to inspire educators, policymakers, and industry stakeholders to collaborate and innovate in the design and implementation of effective smart education solutions. Conclusively, the paper outlines emerging trends, future directions, and potential research opportunities in the field of smart education, emphasizing the importance of continuous improvement and the integration of new technologies to ensure that education remains relevant and effective in the context of Industry 5.0. By providing a holistic understanding of the key components, challenges, and potential solutions associated with smart education, this review paper seeks to contribute to the ongoing discourse surrounding the automation of smart education and its role in preparing the workforce for the future of work. Full article

Existing methods for scoring student presentations predominantly rely on computer-based implementations and do not incorporate a robotic multi-classification model. This limitation can result in potential misclassification issues as these approaches lack active feature learning capabilities due to fixed camera positions. Moreover, these scoring methods often solely focus on facial expressions and neglect other crucial factors, such as eye contact, hand gestures and body movements, thereby leading to potential biases or inaccuracies in scoring. To address these limitations, this study introduces Robotics-based Presentation Skill Scoring (RPSS), which employs a multi-model analysis. RPSS captures and analyses four key presentation parameters in real time, namely facial expressions, eye contact, hand gestures and body movements, and applies the fuzzy Delphi method for criteria selection and the analytic hierarchy process for weighting, thereby enabling decision makers or managers to assign varying weights to each criterion based on its relative importance. RPSS identifies five academic facial expressions and evaluates eye contact to achieve a comprehensive assessment and enhance its scoring accuracy. Specific sub-models are employed for each presentation parameter, namely EfficientNet for facial emotions, DeepEC for eye contact and an integrated Kalman and heuristic approach for hand and body movements. The scores are determined based on predefined rules. RPSS is implemented on a robot, and the results highlight its practical applicability. Each sub-model is rigorously evaluated offline and compared against benchmarks for selection. Real-world evaluations are also conducted by incorporating a novel active learning approach to improve performance by leveraging the robot’s mobility. In a comparative evaluation with human tutors, RPSS achieves a remarkable average agreement of 99%, showcasing its effectiveness in assessing students’ presentation skills. Full article

This study investigated how a physical robot can adapt goal-directed actions in dynamically changing environments, in real-time, using an active inference-based approach with incremental learning from human tutoring examples. Using our active inference-based model, while good generalization can be achieved with appropriate parameters, when faced with sudden, large changes in the environment, a human may have to intervene to correct actions of the robot in order to reach the goal, as a caregiver might guide the hands of a child performing an unfamiliar task. In order for the robot to learn from the human tutor, we propose a new scheme to accomplish incremental learning from these proprioceptive–exteroceptive experiences combined with mental rehearsal of past experiences. Our experimental results demonstrate that using only a few tutoring examples, the robot using our model was able to significantly improve its performance on new tasks without catastrophic forgetting of previously learned tasks. Full article

People with disabilities are severely underrepresented in the open labor market. Yet, pursuing a job has a positive impact in many aspects of life. This paper presents a possible approach to improve inclusion by including a robotic manipulator into context-aware Assistive Systems. This expands the assistance possibilities tremendously by adding gesture-based feedback and aid. The system presented is based on the intelligent control system of behavior trees, which—together with a depth camera, specifically designed policies, and a collaborative industrial robotic manipulator—can assist workers with disabilities in the workplace. A developed assistance node generates personalized action sequences. These include different robotic pointing gestures, from simple waving, to precisely indicating the target position of the workpiece during assembly tasks. This paper describes the design challenges and technical implementation of the first Context-Aware Robotic Assistive System. Moreover, an in-field user study in a Sheltered Workshop was performed to verify the concept and developed algorithms. In the assembly task under consideration, almost three times as many parts could be assembled with the developed system than with the baseline condition. In addition, the reactions and statements of the participants showed that the robot was considered and accepted as a tutor. Full article

The use of robotic surgery (RS) in urology has grown exponentially in the last decade, but RS training has lagged behind. The launch of new robotic platforms has paved the way for the creation of innovative robotics training systems. The aim of our study is to test the new training system from Hugo™ RAS System–Medtronic. Between July 2020 and September 2022, a total of 44 residents from urology, gynaecology and general surgery at our institution participated in advanced robotic simulation training using the Hugo™ RAS simulator. Information about sex, age, year of residency, hours spent playing video games, laparoscopic or robotic exposure and interest in robotics (90.9% declared an interest in robotics) was collected. The training program involved three robotic exercises, and the residents performed these exercises under the guidance of a robotics tutor. The residents’ performance was assessed based on five parameters: timing, range of motion, panoramic view, conflict of instruments and exercise completion. Their performance was evaluated according to an objective Hugo system form and a subjective assessment by the tutor. After completing the training, the residents completed a Likert scale questionnaire to gauge their overall satisfaction. The rate of the residents’ improvement in almost all parameters of the three exercises between the first and the last attempts was statistically significant (p < 0.02), indicating significant progress in the residents’ robotic surgical skills during the training. The mean overall satisfaction score ± standard deviation (SD) was 9.4 ± 1.2, signifying a high level of satisfaction among the residents with the training program. In conclusion, these findings suggest that the training program utilizing the Hugo™ RAS System is effective in enhancing robotic surgical skills among residents and holds promise for the development of standardized robotics training programs in various surgical specialties. Full article

In this fast-aging society, many older adults fail to meet the required level of exercise due to trainer shortages. Therefore, we developed a robot tutor to investigate the feasibility of robot-mediated exercise for older adults. Twenty older adults participated in an experimental study. A pre-exercise survey was used to assess their background. Each participant experienced a 30-min robot-led Tai Chi exercise followed by a post-exercise survey to evaluate the easiness of following the robot and expectations for future robot design. Participants’ Tai Chi performances were evaluated in terms of completion and accuracy. Associations between the surveys and the performance were also analyzed. All participants completed the study. Fifteen out of the twenty subjects had at least one chronic condition, and most practiced Tai Chi before the study but had never interacted with a robot. On average, the participants scored 93.09 and 85.21 out of 100 for movement completion and accuracy, respectively. Their initial movement accuracy was correlated with their attitude towards exercise. Most subjects reported that they could follow the robot’s movements and speeches well and were interested in using a robot tutor in the community. The study demonstrated the initial feasibility of robot-led Tai Chi exercise for older adults. Full article

In recent years, there have been many approaches to using robots to teach computer programming. In intelligent tutoring systems and computer-aided learning, there is also some research to show that affective feedback to the student increases learning efficiency. However, a few studies on the role of incorporating an emotional personality in the robot in robot-assisted learning have found different results. To explore this issue further, we conducted a pilot study to investigate the effect of positive verbal encouragement and non-verbal emotive behaviour of the Miro-E robot during a robot-assisted programming session. The participants were tasked to program the robot’s behaviour. In the experimental group, the robot monitored the participants’ emotional state via their facial expressions, and provided affective feedback to the participants after completing each task. In the control group, the robot responded in a neutral way. The participants filled out a questionnaire before and after the programming session. The results show a positive reaction of the participants to the robot and the exercise. Though the number of participants was small, as the experiment was conducted during the pandemic, a qualitative analysis of the data was carried out. We found that the greatest affective outcome of the session was for students who had little experience or interest in programming before. We also found that the affective expressions of the robot had a negative impact on its likeability, revealing vestiges of the uncanny valley effect. Full article