Robot Tutor Research Articles

Enriching Social Cognitive and Behavioral intention by Mobilizing Robotics Tutor-based GenAI in Education 6.0

The study aims to investigate the impact of incorporating robotics-based GenAI into pedagogical approaches, in order to enrich social cognitive development in the higher education context. By examining the impact of robotics-based Generative Artificial Intelligence (GenAI) on social cognition and learning outcomes, this study investigates the application of GenAI in Indonesian higher education, specifically in sociology programs. In poor countries like Indonesia, where pedagogical and infrastructural constraints prevent its widespread adoption, the report highlights the gap in the application of GenAI in the education system. Over the course of four weeks, 27 sociology students used ChatGPT as a robot tutor for undergraduate students to access information, and the study used a mixed-methods approach to collect data through observation and structured questionnaires. Despite the students' perception of GenAI as a valuable tool for enhancing engagement, problem-solving, and comprehension of intricate sociology topics, the findings revealed no correlation between the use of ChatGPT and students' social cognition. Furthermore, it appears that classroom dynamics and teacher engagement are more important factors influencing undergraduates’ social cognitive development. Future recommendations to fully incorporate GenAI into education and investigate its long-term benefits include strengthening infrastructure, addressing ethical issues, and improving teacher training.

Peer or tutor? The congruity effects of service robot role and service type on usage intention

The invention of service robots has reduced the labor cost and improved enterprises' efficiency and service quality. However, it is still difficult to enhance consumers' intention to use robot-by-robot design efficiently. Based on social roles of anthropomorphic cues, service robots can be divided into peer (e.g., kind and amiable friends) or tutor (e.g., authoritative and professional experts) robots. From a matching perspective, this paper investigates (1) whether robot role and service type have an impact on consumers' intention to employ service robots in different ways, and (2) how cognitive trust and affective trust can play a mediating role during this process. In this paper, the authors conducted an online a scenario-based experiment and collected a valid sample of 332 consumers. The results show that the participants are more willing to apply the tutor robot in the scenario of utilitarian service, and the peer robot in the scenario of hedonic service. In addition, cognitive trust and affective trust have a matching mediation effect. Specifically, for the utilitarian service, cognitive trust mediates the effect of robot role on consumers' intention to adopt the robots, while the mediating effect of affective trust is not significant. As for the hedonic service, affective trust mediated the effect of robot role on the intention to use, whereas the mediating effect of cognitive trust is not significant.

The impact of social robots' presence and roles on children's performance in musical instrument practice

AbstractResearch on the educational applications of social robots has shown how they can motivate children and help improve academic learning outcomes. Here, we examine how robots can support skill learning and, more specifically, musical instrument practice. Drawing from social facilitation theory and evaluation apprehension theory we expected that the robot's mere presence would impact children's performance and that this effect would be contingent upon the children expecting the robot to evaluate their performance. We report an experiment with children (N = 31) aged nine to twelve who practiced a familiar and new piece alone, in the presence of an evaluative robot, and in the presence of a non‐evaluative robot. We found that children performed better in terms of rhythm, pitch, and general impression in the presence of the non‐evaluative robot. These findings offer important insights for designing robot tutors for music learning. Practitioner notesWhat is already known about this topic Social robots have been applied in different educational scenarios (e.g., second language, math, and programming) and were proven to be beneficial for children's motivation. Musical instrument learning requires practice, perseverance, and social support to become successful. Social robots can be used as a provider of social support during musical instrument practice. Children tend to perform better on easy or well‐rehearsed tasks and worse on complex tasks or new ones with the presence of observers, but only when they believe the observer can evaluate them. What this paper adds Social robots are beneficial for children's performance in musical instrument learning. Limited evidence was found to prove that children tend to perform better on old melodies and worse on new melodies in the presence of a social robot. However, the results confirmed that the level of evaluative of the robot matters. Children tend to have better performance with the robot that did not provide evaluative comments when practicing a new melody (a difficult task) than alone and with the robot that offered evaluative comments. This study confirmed that social robots can provide support to children in practicing music, helping to improve their performance. Implications for practice and/or policy Social facilitation and evaluation apprehension effects need to be taken into consideration during the behaviour design of companion robots in learning scenarios. Robots, which were intended to motivate children in learning, should be designed to not provide evaluative comments.

RoboREIT: An interactive robotic tutor with instructive feedback component for requirements elicitation interview training

AbstractInterviewing stakeholders is the most popular technique for eliciting requirements. The success of an interview depends on the interviewer's theoretical knowledge, preparedness, and communication skills. Practice interviews allow students to apply their knowledge and improve their skills through experience. This practical training is resource‐intensive, requiring the time and effort of a stakeholder for each student, which may not be feasible for a large number of students. This paper introduces RoboREIT, an interactive Robotic tutor for Requirements Elicitation Interview Training. RoboREIT addresses the scalability problem of practice sessions with a robotic tutor acting as a stakeholder during the interview and providing feedback after the interview. We performed an exploratory user study to evaluate RoboREIT and demonstrate its applicability in requirements elicitation interview training. The quantitative and qualitative analyses of the users' responses reveal the appreciation of RoboREIT. Our study is the first in the literature that utilizes a social robot in requirements elicitation interview education. RoboREIT's design incorporates replaying faulty interview stages and allows the student to learn from mistakes by a second time practicing. All participants praised the feedback component, which is not present in the state of the art, for being helpful in identifying the mistakes. A favorable response rate of 81% for the system's usefulness indicates the positive perception of the participants.

Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context

Despite the significant emphasis placed on incorporating 21st century skills into the educational framework, particularly at the primary level, recent scholarly works indicate considerable variation in the implementation of these skills across different countries and regions, suggesting a demand for further research specifically focusing on primary education. The indications of the Digicomp framework and 21st-century skills in Europe have outlined the key competences for lifelong learning needed for all citizens, including teachers and students. In this perspective, Education plays a fundamental role in ensuring that citizens acquire the required skills. The objective in the common European framework is clear: to initiate a transition from the culture of knowledge to the culture of competence. Nowadays, technological advancement allows the researchers to create and combine different frameworks with the perspective of an even more tailored, and engaged education, some examples derived from the implementation of Virtual Reality (VR) and Augmented Reality (AR), in the combination of Gamification and AI, or the development of Intelligent Tutoring Systems (ITS) to foster and create an even more personalized learning and teaching. Following these premises, in this paper, we want to point out new research reflections and perspectives that could help researchers, teachers, educators (and consequently students) to reflect on the introduction of new technologies (e.g., artificial intelligence, robot tutors) and on how these can affect on human behavioral development and on the acquisition of new skills and competences (Specifically: Creativity, Critical Thinking, Problem Solving, and Computational Thinking) for the educational context. The analysis carried on, suggests a perspective on how creativity, critical thinking, and problem-solving can be effective in promoting computational thinking, and how Artificial Intelligence (AI) could be an aid instrument to teachers in the fostering of creativity, critical thinking, and problem-solving in schools and educational contexts.

Parent's Perception towards Robot Tutor in Egyptian Schools

Parent's Perception towards Robot Tutor in Egyptian Schools

An Exploration of Robot-Mediated Tai Chi Exercise for Older Adults

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.

Playful activity with robot for hand hygiene of elementary school students: quasi-experimental study.

To evaluate the effectiveness of the hand hygiene process after elementary school students participated in an educational activity using an automated digital technology called Tutor Robot. Quasi-experimental study developed in 2019 with elementary school students (n=203). Hand hygiene was performed with a fluorescent solution before and after participating in an educational activity with a tutor robot. The images were recorded in a dark chamber and the data related to area of residence, grade, gender, hand position and laterality were analyzed by Anova and Holm-Sidak Post-Hoc (p≤0,05). All conditions studied improved the hand hygiene process after activity with the tutor robot. There was no association between grade, gender, and dominant hand and the performance in the hand hygiene process, however, students from urban areas performed better than those from rural areas. The activity with the tutor robot represented an important resource for conducting health education actions on hand hygiene and can also be tested in other settings and populations.

The Influence of Robot Social Behaviors on Second Language Learning in Preschoolers

The use of social robots has flourished in the educational field. Using them in education appropriately entails considering whether robot social behaviors influence language learning outcomes and have additive influences on the outcomes of language learning. In this study, we used a social robot (i.e., Alpha Mini) to teach Chinese preschool children (N = 83, recruited from a preschool; aged 6–7 years) new English words; it had the pitch of a human boy, and the words’ referents were displayed on a tablet. During one learning session, the robot demonstrated one of several types of social behavior: verbal behavior (i.e., asking children to look at this robot), gestural behavior (i.e., pointing to the word’s referent), both verbal and gestural behaviors, and control (i.e., purely saying target words to the children). Children were randomly assigned to one of four conditions. Their vocabulary learning outcomes were measured by pre- and posttests of the children’s vocabulary of the target words. Our results show that the learning outcome in each type of social behavior was greater than zero, suggesting that any type of robot social behavior benefits word learning. Importantly, when robot social behavior included verbal demonstrations, the preschoolers learned more vocabulary than when the robot displayed other social behaviors (i.e., control and gestural behavior); however, gestural behavior showed no greater learning outcomes than the control condition. Hence, the efficacy of robot tutoring in preschoolers’ second language learning depends upon a robot’s social behavior, and social verbal behavior is the most effective behavior in increasing learning gains. Therefore, to achieve better learning outcomes, social verbal behavior should be critically considered to enhance robots’ tutoring function.

Toward Personalized Affect-Aware Socially Assistive Robot Tutors for Long-Term Interventions with Children with Autism

Affect-aware socially assistive robotics (SAR) has shown great potential for augmenting interventions for children with autism spectrum disorders (ASD). However, current SAR cannot yet perceive the unique and diverse set of atypical cognitive-affective behaviors from children with ASD in an automatic and personalized fashion in long-term (multi-session) real-world interactions. To bridge this gap, this work designed and validated personalized models of arousal and valence for children with ASD using a multi-session in-home dataset of SAR interventions. By training machine learning (ML) algorithms with supervised domain adaptation (s-DA), the personalized models were able to tradeoff between the limited individual data and the more abundant less personal data pooled from other study participants. We evaluated the effects of personalization on a long-term multimodal dataset consisting of four children with ASD with a total of 19 sessions, and derived inter-rater reliability (IR) scores for binary arousal (IR = 83%) and valence (IR = 81%) labels between human annotators. Our results show that personalized Gradient Boosted Decision Trees (XGBoost) models with s-DA outperformed two non-personalized individualized and generic model baselines not only on the weighted average of all sessions, but also statistically ( p < .05) across individual sessions. This work paves the way for the development of personalized autonomous SAR systems tailored toward individuals with atypical cognitive-affective and socio-emotional needs.

Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children

Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning.

Robot guides reveal A+ tutor-ant technique

A good teacher can make all the difference: inspiring, educating and changing lives. For tiny rock ants (Temnothorax albipennis) that live in colonies of about 100 workers, good teachers can be essential for the survival of the community. When a homeless colony relocates, scouts scurry about in search of the next residence. However, instead of laying down a pheromone trail for other members of the colony to follow to their new home, the scouts indulge in a spot of one-on-one tuition, known as a tandem run, guiding individual novices and pausing repeatedly to allow their acolytes to gain their bearings and learn the route. ‘[It] probably reduces the risk that individuals become lost’, says Nigel Franks from the University of Bristol, UK. But he was curious to understand what makes a good ant tutor; ‘what elements are necessary and sufficient for successful teaching’, he says. However, as they couldn't ask the tiny ant instructors to reveal their secrets, Franks and colleagues from the University of Bristol decided to find out how well novice ants learned when guided by a robot instructor. Alan Worley programmed a gantry above an ant arena to move back and forth with a small sliding robot attached to it, so that Jacob Podesta and Edward Jarvis could direct the robot to move at different speeds along either straight or wavy routes. Next, the researchers placed a rock ant nest in the arena and removed the scent glands from a worker ant, attaching them to the robot to make it smell like an ant teacher as it trained novices. Then the team waited for an intrepid pioneer to emerge from the nest before setting the robot off to guide the novice to a new home 35 cm away, allowing the robot to slow and pause when its apprentice stopped to survey its surroundings. But the true test came when the researchers tracked the student's return trip as it headed back to recruit other members of the nest to follow. How well had the naïve ant explorers learned from their wise robot instructor?Using statistics, Ana Sendova-Franks revealed that the ants that had been directed by the roboguide were more than twice as likely to make it back to their original nest as novices that the researchers had carried to the new location. And when the team compared the success of novice ants that had followed a straight path and those whose robot had meandered, both sets of ants were equally successful at relocating their original nest; the shape of the route made little difference. In fact, the returning ants that had been trained by a wandering teacher tended to take more direct return routes. ‘The sinusoidal paths may constitute longer lessons and thus may give followers more time to learn their surroundings', says Franks, adding ‘It is likely that tandem followers learn both local and more distant landmarks, which could be anything from the walls of the arena to windows in the laboratory’.And when the team released additional ants into the arena after the apprentice and its robot teacher had embarked, the trailing ants followed the wavy trainees’ more circuitous outbound routes. The apprentice ants were leaving behind pheromone trails for the trailer ants to follow. Franks explains that teacher ants usually deposit a pheromone trail when guiding novices, but it appears that apprentices can also leave such trails; ‘They may serve as safety lines to help ensure that individuals do not become lost’. And he is keen to find out whether trainee ants can, in turn, influence their robot tutor.

A Review on HRI for Child Learning

Human robot interaction (HRI) becomes an effective method of interacting with kids. Many HRI's system are currently available. In this paper, a review of various robotic tutor system based on design aspects, safety aspects, mechanical and electronic aspects of robots, learning outcome of kids, type of interaction, configuration of various learning modules, real time behavior monitoring and socio centric aspect are presented in this paper. As the robot tutoring has several benefits over human tutoring because these kinds of system have various pre-installed learning modules so that without any human monitoring child learn its concept. The robotic system is now a days become very effective for the learner for real time applications

Emotion Recognition in Human–Robot Interaction Using the NAO Robot

Affective computing can be implemented across many fields in order to provide a unique experience by tailoring services and products according to each person’s needs and interests. More specifically, digital learning and robotics in education can benefit from affective computing with a redesign of the curriculum’s contents based on students’ emotions during teaching. This key feature is observed during traditional learning methods, and robot tutors are adapting to it gradually. Following this trend, this work focused on creating a game that aims to raise environmental awareness by using the social robot NAO as a conversation agent. This quiz-like game supports emotion recognition with DeepFace, allowing users to review their answers if a negative emotion is detected. A version of this game was tested during real-life circumstances and produced favorable results, both for emotion analysis and overall user enjoyment.

Social Robots in Education for Long-Term Human-Robot Interaction : Socially Supportive Behaviour of Robotic Tutor for Creating Robo-Tangible Learning Environment in a Guided Discovery Learning Interaction

The article discusses the possible applications of social robots in education, the technological and pedagogical challenges they possess, and the ways in which they may affect learning outcomes, concentrating only on robots intended to assist students in learning via social interaction. Author has highlighted three significant research problems : 1) Does robot tutors help improve students’ learning outcomes? 2) Does appearance and behaviour of robots have a significant role to play on academic engagement of learners? 3) What could be the potential roles of a robot in an educational setting? A statistical meta-analysis of previously published research articles is used to substantiate author’s claims. The larger aim of this article is to provide effective groundwork for future research by describing the expected outcomes of using social robots to offer education and identifying potential research areas for further inquiry.

Social Robots in Applied Settings: A Long-Term Study on Adaptive Robotic Tutors in Higher Education.

Learning in higher education scenarios requires self-directed learning and the challenging task of self-motivation while individual support is rare. The integration of social robots to support learners has already shown promise to benefit the learning process in this area. In this paper, we focus on the applicability of an adaptive robotic tutor in a university setting. To this end, we conducted a long-term field study implementing an adaptive robotic tutor to support students with exam preparation over three sessions during one semester. In a mixed design, we compared the effect of an adaptive tutor to a control condition across all learning sessions. With the aim to benefit not only motivation but also academic success and the learning experience in general, we draw from research in adaptive tutoring, social robots in education, as well as our own prior work in this field. Our results show that opting in for the robotic tutoring is beneficial for students. We found significant subjective knowledge gain and increases in intrinsic motivation regarding the content of the course in general. Finally, participation resulted in a significantly better exam grade compared to students not participating. However, the extended adaptivity of the robotic tutor in the experimental condition did not seem to enhance learning, as we found no significant differences compared to a non-adaptive version of the robot.

When Even a Robot Tutor Zooms: A Study of Embodiment, Attitudes, and Impressions.

This study used an online second language (L2) vocabulary lesson to evaluate whether the physical body (i.e., embodiment) of a robot tutor has an impact on how the learner learns from the robot. In addition, we tested how individual differences in attitudes toward robots, first impressions of the robot, anxiety in learning L2, and personality traits may be related to L2 vocabulary learning. One hundred Turkish-speaking young adults were taught eight English words in a one-on-one Zoom session either with a NAO robot tutor (N = 50) or with a voice-only tutor (N = 50). The findings showed that participants learned the vocabulary equally well from the robot and voice tutors, indicating that the physical embodiment of the robot did not change learning gains in a short vocabulary lesson. Further, negative attitudes toward robots had negative effects on learning for participants in the robot tutor condition, but first impressions did not predict vocabulary learning in either of the two conditions. L2 anxiety, on the other hand, negatively predicted learning outcomes in both conditions. We also report that attitudes toward robots and the impressions of the robot tutor remained unchanged before and after the lesson. As one of the first to examine the effectiveness of robots as an online lecturer, this study presents an example of comparable learning outcomes regardless of physical embodiment.

Machine Teaching for Human Inverse Reinforcement Learning.

As robots continue to acquire useful skills, their ability to teach their expertise will provide humans the two-fold benefit of learning from robots and collaborating fluently with them. For example, robot tutors could teach handwriting to individual students and delivery robots could convey their navigation conventions to better coordinate with nearby human workers. Because humans naturally communicate their behaviors through selective demonstrations, and comprehend others’ through reasoning that resembles inverse reinforcement learning (IRL), we propose a method of teaching humans based on demonstrations that are informative for IRL. But unlike prior work that optimizes solely for IRL, this paper incorporates various human teaching strategies (e.g. scaffolding, simplicity, pattern discovery, and testing) to better accommodate human learners. We assess our method with user studies and find that our measure of test difficulty corresponds well with human performance and confidence, and also find that favoring simplicity and pattern discovery increases human performance on difficult tests. However, we did not find a strong effect for our method of scaffolding, revealing shortcomings that indicate clear directions for future work.

Effect of Robot Tutor’s Feedback Valence and Attributional Style on Learners

This study investigated the effect of a robot tutor’s feedback valence (positive feedback and negative feedback) and attributional style (internal-attribution and external-attribution) on learners. A Nao robot was designed to be a dance tutor that taught participants dance movements and gave performance feedback. Thirty-six graduate and undergraduate students participated in the experiments and were divided into two groups: an internal-attribution group and an external-attribution group. Participants in each group received both positive and negative feedback. The results indicated that the participants were more likely to accept positive feedback than negative feedback. In addition, they perceived greater influence by positive feedback than negative feedback. Moreover, participants did not the demonstrate self-serving attributional bias. They attributed more blame to themselves than to the robot for negative performance, whereas no difference was observed in the attribution of credit between the robot and the participants. Furthermore, the internal-attribution robot tutor was more likely to be regarded as a training coach instead of a training tool than the external-attribution robot tutor. Finally, participants reported a higher relationship closeness with the internal-attribution robot tutor than the external-attribution robot tutor.

Robot Tutoring of Multiplication: Over One-Third Learning Gain for Most, Learning Loss for Some

In the design of educational robots, it appears to be undecided as to whether robots should show social behaviors and look human-like or whether such cues are insignificant for learning. We conducted an experiment with different designs of social robots built from the same materials, which is unique in robotics research. The robots rehearsed multiplication tables with primary school children in Hong Kong, which is a user group not easily or often accessed. The results show that affective bonding tendencies may occur but did not significantly contribute to the learning progress of these children, which was perhaps due to the short interaction period. Nonetheless, 5 min of robot tutoring improved their scores by about 30%, while performance dropped only for a few challenged children. We discuss topics, such as teaching language skills, which may be fostered by human likeness in appearance and behaviors; however, for Science, Technology, Engineering, and Mathematics (STEM)-related subjects, the social aspects of robots hardly seem to matter.